@article {7205,
	title = {Federated Learning for Condition Monitoring of Industrial Processes: A Review on Fault Diagnosis Methods, Challenges, and Prospects},
	journal = {Electronics},
	volume = {12},
	number = {1},
	year = {2023},
	abstract = {Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.},
	url = {https://www.mdpi.com/2079-9292/12/1/158?type=check_update\&version=1},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Bentrcia, Toufik and Lim, Wei-Hong and Amirat, Yassine}
}
@article {7204,
	title = {ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions},
	journal = {Aerospace},
	volume = {10},
	number = {1},
	year = {2023},
	abstract = {Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies, strengthened with robust feature engineering. Initially, data engineering encompassing: (i) principal component analysis (PCA) dimensionality reduction; (ii) feature selection using correlation analysis; (iii) denoising with empirical Bayesian Cauchy prior wavelets; and (iv) feature scaling is used to obtain the required learning representations. Next, an adaptive deep learning model, namely ProgNet, is trained on a source domain with sufficient degradation trajectories generated from PrognosEase, a run-to-fail data generator for health deterioration analysis. Then, ProgNet is transferred to the target domain of obtained degradation features for fine-tuning. The primary goal is to achieve a higher-level generalization while reducing algorithmic complexity, making experiments reproducible on available commercial computers with quad-core microprocessors. ProgNet is tested on the popular New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset describing real flight scenarios. To the extent we can report, this is the first time that all N-CMAPSS subsets have been fully screened in such an experiment. ProgNet evaluations with numerous metrics, including the well-known CMAPSS scoring function, demonstrate promising performance levels, reaching 234.61 for the entire test set. This is approximately four times better than the results obtained with the compared conventional deep learning models.},
	url = {https://www.mdpi.com/2226-4310/10/1/10},
	author = {Berghout, Tarek and Mouss, Mohamed-Djamel and Mouss, Leila-Hayet and Benbouzid, Mohamed}
}
@article {7203,
	title = {Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects},
	journal = {Electronics},
	volume = {11},
	number = {23},
	year = {2022},
	abstract = {Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber{\textendash}physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.},
	url = {https://www.mdpi.com/2079-9292/11/23/3854},
	author = {Inayat, Usman and Zia, Muhammad-Fahad and Mahmood, Sajid and Berghout, Tarek and Benbouzid, Mohamed}
}
@proceedings {7256,
	title = {Deep Learning with Recurrent Expansion for Electricity Theft Detection in Smart Grids},
	journal = {48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022},
	year = {2022},
	address = {Brussels, Belgium},
	abstract = {The increase in electricity theft has become one of the main concerns of power distribution networks. Indeed, electricity theft could not only lead to financial losses, but also leads to reputation damage by reducing the quality of supply. With advanced sensing technologies of metering infrastructures, data collection of electricity consumption enables data-driven methods to emerge in such non-technical loss detections as an alternative to traditional experience-based human-centric approaches. In this context, such fraud prediction problems are generally a thematic of missing patterns, class imbalance, and higher level of cardinality where there are many possibilities that a single feature can assume. Therefore, this article is introduced specifically to solve data representation problem and increase the sparseness between different data classes. As a result, deeper representations than deep learning networks are introduced to repeatedly merge the learning models themselves into a more complex architecture in a sort of recurrent expansion. To verify the effectiveness of the proposed recurrent expansion of deep learning (REDL) approach, a realistic dataset of electricity theft is involved. Consequently, REDL has achieved excellent data mapping results proven by both visualization and numerical metrics and shows the ability of separating different classes with higher performance. Another important REDL feature of outliers correction has been also discovered in this study. Finally, comparison to some recent works also proved superiority of REDL model.},
	url = {https://ieeexplore.ieee.org/document/9968378},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Ferrag, Mohamed-Amine}
}
@proceedings {7258,
	title = {Detecting Cyberthreats in Smart Grids Using Small-Scale Machine Learning},
	journal = {ELECTRIMACS 2022},
	year = {2022},
	address = {Nancy, France},
	abstract = {Due to advanced monitoring technologies including the plug-in of the cyber and physical layers on the Internet, cyber-physical systems are becoming more vulnerable than ever to cyberthreats leading to possible damage of the system. Consequently, many researchers have devoted to studying detection and identification of such threats in order to mitigate their drawbacks. Among used tools, Machine Learning (ML) has become dominant in the field due to many usability characteristics including the blackbox models availability. In this context, this paper is dedicated to the detection of cyberattacks in Smart Grid (SG) networks which uses industrial control systems (ICS), through the integration of ML models assembled on a small scale. More precisely, it therefore aims to study an electric traction substation system used for the railway industry. The main novelty of our contribution lies in the study of the behaviour of more realistic data than the traditional studies previously shown in the state of the art literature by investigating even more realistic types of attacks. It also emulates data analysis and a larger feature space under most commonly used connectivity protocols in today{\textquoteright}s industry such as S7Comm and Modbus.},
	url = {https://www.researchgate.net/publication/360756643_Detecting_Cyberthreats_in_Smart_Grids_Using_Small-Scale_Machine_Learning},
	author = {Berghout, Tarek and Benbouzid, Mohamed}
}
@article {7194,
	title = {EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids},
	journal = {Reliability Engineering \& System Safety},
	volume = {226},
	year = {2022},
	abstract = {Reliability and security\&nbsp;of power distribution\&nbsp;and data traffic in smart grid (SG) are very important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is subject to several vulnerabilities that can damage or disrupt its process immunity via cyberthreats. Today{\textquoteright}s ICSs are experiencing highly complex data change and dynamism, increasing the complexity of detecting and mitigating cyberattacks. Subsequently, and since Machine Learning (ML) is widely studied in cybersecurity, the objectives of this paper are twofold. First, for algorithmic simplicity, a small-scale\&nbsp;ML algorithm\&nbsp;that attempts to reduce computational costs is proposed. The algorithm adopts a\&nbsp;neural network\&nbsp;with an augmented hidden layer (NAHL) to easily and efficiently accomplish the learning procedures. Second, to solve the data complexity problem regarding rapid change and dynamism, a label autoencoding approach is introduced for Embedding Labels in the NAHL (EL-NAHL) architecture to take advantage of labels propagation when separating data scatters. Furthermore, to provide a more realistic analysis by addressing real-world threat scenarios, a dataset of an electric traction\&nbsp;substation\&nbsp;used in the high-speed rail industry is adopted in this work. Compared to some existing algorithms and other previous works, the achieved results show that the proposed EL-NAHL architecture is effective even under massive dynamically changed and imbalanced data.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0951832022003131},
	author = {Berghout, Tarek and Benbouzid, Mohamed}
}
@article {7196,
	title = {Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis},
	journal = {Le{\"{\i}la-Hayet},
	volume = {24},
	number = {7},
	year = {2022},
	abstract = {The green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial application since dynamic operating conditions, including physical deterioration, have a serious impact on the cell performance. Under these circumstances, prognosis and health management (PHM) plays an important role in prolonging durability and preventing damage propagation via the accurate planning of a condition-based maintenance (CBM) schedule. In this specific topic, health deterioration modeling with deep learning (DL) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. In this context, the present paper proposes an investigation of further deeper representations by exposing DL models themselves to recurrent expansion with multiple repeats. Such a recurrent expansion of DL (REDL) allows new, more meaningful representations to be explored by repeatedly using generated feature maps and responses to create new robust models. The proposed REDL, which is designed to be an adaptive learning algorithm, is tested on a PEMFC deterioration dataset and compared to its deep learning baseline version under time series analysis. Using multiple numeric and visual metrics, the results support the REDL learning scheme by showing promising performances.},
	url = {https://www.mdpi.com/1099-4300/24/7/1009},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Bentrcia, Toufik and Amirat, Yassine and Le{\"{\i}la-Hayet Mouss}
}
@article {7198,
	title = {A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed},
	journal = {Mathematics},
	volume = {10},
	number = {19},
	year = {2022},
	abstract = {Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the weights and biases resulting from training-based stochastic gradient descent variants), are subject to many challenges, namely expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. In this context, our paper targets the four aforementioned challenges while focusing on reducing communication and computational costs by involving recursive least squares (RLS) training rules. Accordingly, to the best of our knowledge, this is the first time that the RLS algorithm is modified to completely accommodate non-independent and identically distributed data (non-IID) for federated transfer learning (FTL). Furthermore, this paper also introduces a newly generated dataset capable of emulating such real conditions and of making data investigation available on ordinary commercial computers with quad-core microprocessors and less need for higher computing hardware. Applications of FTL-RLS on the generated data under different levels of complexity closely related to different levels of cardinality lead to a variety of conclusions supporting its performance for future uses.},
	url = {https://www.mdpi.com/2227-7390/10/19/3528},
	author = {Berghout, Tarek and Bentrcia, Toufik and Ferrag, Mohamed-Amine and Benbouzid, Mohamed}
}
@proceedings {7257,
	title = {Improving Small-scale Machine Learning with Recurrent Expansion for Fuel Cells Time Series Prognosis},
	journal = {48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022)},
	year = {2022},
	address = {Brussels, Belgium},
	abstract = {The clean energy conversion characteristics of proton exchange membrane fuel cells (PEMFCs) have given rise to many applications, particularly in transportation. Unfortunately, the commercial application of PEMFCs is hampered by the early deterioration and low durability of the cells. In this case, accurate real-time condition monitoring plays an important role in extending the lifespan of PEMFCs through accurate planning of maintenance tasks. Accordingly, among the widely used modeling tools such as model-driven and data-driven, machine learning has received much attention and has been extensively studied in the literature. Small-scale machine learning (SML) and Deep Learning (DL) are subcategories of machine learning that have been exploited so far. In this context and since SML usually contains non-expansive approximators, this study was dedicated to improving its feature representations for better predictions. Therefore, a recurrent expansion experiment was conducted for several rounds to investigate a linear regression model under time series prognosis of PEMFCs. The results revealed that the prediction performance of SML tools under stationary conditions could be further improved.},
	url = {https://ieeexplore.ieee.org/document/9968566},
	author = {Berghout Tarek and Benbouzid, Mohamed and Amirat, Yassine}
}
@article {7195,
	title = {Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects},
	journal = {International Journal of Critical Infrastructure Protection},
	volume = {38},
	year = {2022},
	abstract = {In modern Smart Grids (SGs) ruled by advanced computing and networking technologies, condition monitoring relies on secure cyberphysical connectivity. Due to this connection, a portion of transported data, containing confidential information, must be protected as it is vulnerable and subject to several cyber threats. SG cyberspace adversaries attempt to gain access through networking platforms to commit several criminal activities such as disrupting or malicious manipulation of whole electricity delivery process including generation, distribution, and even customer services such as billing, leading to serious damage, including financial losses and loss of reputation. Therefore, human awareness training and software technologies are necessary precautions to ensure the reliability of data traffic and power transmission. By exploring the available literature, it is undeniable that Machine Learning (ML) has become the latest in the timeline and one of the leading artificial intelligence technologies capable of detecting, identifying, and responding by mitigating adversary attacks in SGs. In this context, the main objective of this paper is to review different ML tools used in recent years for cyberattacks analysis in SGs. It also provides important guidelines on ML model selection as a global solution when building an attack predictive model. A detailed classification is therefore developed with respect to data security triad, i.e., Confidentiality, Integrity, and Availability (CIA) within different types of cyber threats, systems, and datasets. Furthermore, this review highlights the various encountered challenges, drawbacks, and possible solutions as future prospects for ML cybersecurity applications in SGs.},
	url = {https://ideas.repec.org/a/eee/ijocip/v38y2022ics1874548222000348.html},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Muyeen, S-M}
}
@article {7179,
	title = {A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction},
	journal = {IEEE Transactions on Energy Conversion},
	volume = {37},
	number = {2},
	year = {2022},
	abstract = {Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e., bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validation performed using the PRONOSTIA induction machine bearing degradation datasets clearly proves the capacity and higher performance of the proposed deep learning knowledge transfer-based prognosis approach.},
	url = {https://ieeexplore.ieee.org/document/9552475},
	author = {Berghout, Tarek and Mouss, Leila-Hayet and Bentrcia, Toufik and Benbouzid, Mohamed}
}
@article {7193,
	title = {A Systematic Guide for Predicting Remaining Useful Life with Machine Learning},
	journal = {Electronics},
	volume = {11},
	number = {7},
	year = {2022},
	abstract = {Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system{\textquoteright}s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.},
	url = {https://www.mdpi.com/2079-9292/11/7/1125},
	author = {Berghout, Tarek and Benbouzid, Mohamed}
}
@article {7185,
	title = {Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	abstract = {Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.},
	url = {https://ieeexplore.ieee.org/document/9610082},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Muyeen, S-M and Bentrcia, Toufik and Mouss, Le{\"\i}la-Hayet}
}
@article {7177,
	title = {A deep supervised learning approach for condition-based maintenance of naval propulsion systems},
	journal = {Ocean Engineering},
	volume = {221},
	number = {1},
	year = {2021},
	abstract = {In the last years,\&nbsp;predictive maintenance\&nbsp;has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of\&nbsp;extreme learning machines\&nbsp;using data issued from a\&nbsp;propulsion system\&nbsp;simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0029801820314323},
	author = {Berghout, Tarek and Mouss, Leila-Hayet and Bentrcia, Toufik and Elbouchikhi, Elhoussin and Benbouzid, Mohamed}
}
@article {7214,
	title = {Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review},
	journal = {Energies},
	volume = {14},
	number = {18},
	year = {2021},
	abstract = {Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.},
	url = {https://ideas.repec.org/a/gam/jeners/v14y2021i18p5967-d639498.html},
	author = {Benbouzid, Mohamed and Berghout, Tarek and Sarma, Nur and Djurovi{\'c}, Sini{\v s}a and Wu, Yueqi and Ma, Xiandong}
}
@article {7187,
	title = {Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction},
	journal = {Energies},
	volume = {14},
	number = {8},
	year = {2021},
	abstract = {
	Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long{\textendash}short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

},
	url = {https://www.mdpi.com/1996-1073/14/8/2163},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Mouss, ,Hayet-Le{\"\i}la}
}
@proceedings {7242,
	title = {Machine Learning for Photovoltaic Systems Condition Monitoring: A Review},
	journal = {47th Annual Conference of the IEEE Industrial Electronics Society, IECON},
	year = {2021},
	address = {Toronto, Canada},
	abstract = {Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.},
	url = {https://ieeexplore.ieee.org/document/9589423},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Mouss, Leila-Hayet}
}
@article {7180,
	title = {Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects},
	journal = {Energies},
	volume = {14},
	number = {19},
	year = {2021},
	abstract = {To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.},
	url = {https://www.mdpi.com/1996-1073/14/19/6316},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Bentrcia, Toufik and Ma, Xiandong and Djurovi{\'c}, Sini\v sa and Mouss, Leila-Hayet}
}
@article {7197,
	title = {A Semi-Supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction},
	journal = {IEEE Transactions on Instrumentation and Measurement (2022)},
	volume = {37},
	number = {2},
	year = {2021},
	abstract = {Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e., bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validation performed using the PRONOSTIA induction machine bearing degradation datasets clearly proves the capacity and higher performance of the proposed deep learning knowledge transfer-based prognosis approach.},
	url = {https://ieeexplore.ieee.org/document/9552475},
	author = {Berghout, Tarek and Mouss, Le{\"\i}la-Hayet and Bentrcia, Toufik and Benbouzid, Mohamed}
}
@proceedings {7241,
	title = {Sequence-To-Sequence Health Index Estimation of Rolling Bearings with Long-Short Term Memory and Transfer Learning},
	journal = {47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021},
	year = {2021},
	address = {Toronto, Canada},
	abstract = {One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting sufficient deterioration samples from the bearing life cycle is not possible due to the huge memory requirements and processing costs. As a result, accelerated life tests are believed to be the primary alternatives to such a situation. However, and unfortunately, the recorded samples always are subject to lack of real patterns. Therefore, in this paper, a transfer learning approach is performed to solve such kind of problem where PRONOSTICO dataset is used to assess the current procedures.},
	url = {https://ieeexplore.ieee.org/document/9589114},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Mouss, Leila-Hayet}
}
@article {62709,
	title = {Auto-NAHL: A neural network approach for condition-based maintenance of complex industrial systems},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	month = {2021},
	pages = {152829-152840},
	abstract = {
	Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.

},
	isbn = {2169-3536},
	url = {https://ieeexplore.ieee.org/abstract/document/9610082},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Muyeen, SM and Bentrcia, Toufik and Mouss, Le{\"\i}la-Hayet}
}
@article {62707,
	title = {A deep supervised learning approach for condition-based maintenance of naval propulsion systems},
	journal = {Ocean EngineeringOcean Engineering},
	volume = {221},
	year = {2021},
	month = {2021},
	pages = {108525},
	abstract = {
	In the last years,\&nbsp;predictive maintenance\&nbsp;has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of\&nbsp;extreme learning machines\&nbsp;using data issued from a\&nbsp;propulsion system\&nbsp;simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.

},
	isbn = {0029-8018},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0029801820314323},
	author = {Berghout, Tarek and Mouss, Le{\"\i}la-Hayet and Bentrcia, Toufik and Elbouchikhi, Elhoussin and Benbouzid, Mohamed}
}
@article {62733,
	title = {Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review},
	journal = {Energies},
	volume = {14},
	number = {18},
	year = {2021},
	month = {2021},
	pages = {5967},
	abstract = {
	Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

},
	url = {https://www.mdpi.com/1996-1073/14/18/5967},
	author = {Benbouzid, Mohamed and Berghout, Tarek and Sarma, Nur and Djurovi{\'c}, Sini{\v s}a and Wu, Yueqi and Ma, Xiandong}
}
@article {62711,
	title = {Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction},
	journal = {Energies},
	volume = {14},
	number = {8},
	year = {2021},
	month = {2021},
	pages = {2163},
	abstract = {
	Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long{\textendash}short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

},
	url = {https://www.mdpi.com/1996-1073/14/8/2163},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Mouss, Le{\"\i}la-Hayet}
}
@proceedings {62712,
	title = {Machine Learning for Photovoltaic Systems Condition Monitoring: A Review},
	journal = {IECON 2021{\textendash}47th Annual Conference of the IEEE Industrial Electronics Society},
	year = {2021},
	month = {2021},
	pages = {1-5},
	publisher = {IEEE},
	address = {Toronto,  Canada},
	abstract = {Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.},
	isbn = {1-66543-554-2},
	url = {https://ieeexplore.ieee.org/abstract/document/9589423},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Mouss, Le{\"\i}la-Hayet}
}
@article {62713,
	title = {Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects},
	journal = {Energies},
	volume = {14},
	year = {2021},
	month = {2021},
	abstract = {
	To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

},
	url = {https://www.mdpi.com/1996-1073/14/19/6316},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Bentrcia, Toufik and Ma, Xiandong and Djurovi{\'c}, Sini{\v s}a and Mouss, Le{\"\i}la-Hayet}
}
@article {62708,
	title = {A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction},
	journal = {IEEE Transactions on Energy ConversionIEEE Transactions On Energy Conversion},
	year = {2021},
	month = {2021},
	isbn = {0885-8969},
	author = {Berghout, Tarek and Mouss, Leila-Hayet and Bentrcia, Toufik and Benbouzid, Mohamed}
}
@proceedings {62710,
	title = {Sequence-To-Sequence Health Index Estimation of Rolling Bearings with Long-Short Term Memory and Transfer Learning},
	journal = {IECON 2021{\textendash}47th Annual Conference of the IEEE Industrial Electronics Society},
	year = {2021},
	month = {2021},
	pages = {1-5},
	publisher = {IEEE},
	address = {Toronto, Canada},
	abstract = {One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting sufficient deterioration samples from the bearing life cycle is not possible due to the huge memory requirements and processing costs. As a result, accelerated life tests are believed to be the primary alternatives to such a situation. However, and unfortunately, the recorded samples always are subject to lack of real patterns. Therefore, in this paper, a transfer learning approach is performed to solve such kind of problem where PRONOSTICO dataset is used to assess the current procedures.},
	isbn = {1-66543-554-2},
	url = {https://ieeexplore.ieee.org/abstract/document/9589114},
	author = {Berghout, Tarek and Benbouzid, Mohamed and Mouss, Le{\"\i}la-Hayet}
}
@article {7169,
	title = {Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine},
	journal = {Appl. Sci},
	volume = {10},
	number = {3},
	year = {2020},
	abstract = {The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.},
	url = {https://www.mdpi.com/2076-3417/10/3/1062},
	author = {Berghout, Tarek and Le{\"{\i}la-Hayet Mouss and Ouahab KADRI and Sa{\"{\i}di, Lotfi and Benbouzid, Mohamed}
}
@article {63107,
	title = {Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine},
	journal = {Engineering Applications of Artificial IntelligenceEngineering Applications of Artificial Intelligence},
	volume = {96},
	year = {2020},
	month = {2020},
	pages = {103936},
	isbn = {0952-1976},
	author = {Berghout, Tarek and Mouss, Le{\"\i}la-Hayet and Ouahab KADRI and Sa{\"\i}di, Lotfi and Benbouzid, Mohamed}
}
@article {63106,
	title = {Aircraft engines remaining useful life prediction with an improved online sequential extreme learning machine},
	journal = {Applied SciencesApplied Sciences},
	volume = {10},
	year = {2020},
	month = {2020},
	pages = {1062},
	author = {Berghout, Tarek and Mouss, Le{\"\i}la-Hayet and Ouahab KADRI and Sa{\"\i}di, Lotfi and Benbouzid, Mohamed}
}
@proceedings {65769,
	title = {Photovoltaic module simultaneous open-and short-circuit faults modeling and detection using the I{\textendash}V characteristic},
	journal = {2015 IEEE 24th International Symposium on Industrial Electronics (ISIE)},
	year = {2015},
	month = {2015},
	pages = {855-860},
	publisher = {IEEE},
	isbn = {1-4673-7554-3},
	author = {Rezgui, Wail and Mouss, Hayet and Mouss, Nadia and Mouss, Djamel and Benbouzid, Mohamed and Amirat, Yassine}
}
@article {65768,
	title = {Smart Algorithm Based on the Optimization of SVR Technique by k-NNR Method for the Prognosis of the Open-Circuit and the Reversed Polarity Faults in a PV Generator},
	journal = {International Review on Modelling and Simulations (IREMOS)International Review on Modelling and Simulations (IREMOS)},
	volume = {8},
	year = {2015},
	month = {2015},
	pages = {18-25},
	author = {Rezgui, Wail and MOUSS, Nadia Kinza and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@article {65766,
	title = {Smart algorithm for the preventive monitoring of the impedance fault within a PV generator},
	year = {2015},
	month = {2015},
	author = {Rezgui, Wail and MOUSS, Nadia Kinza and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@proceedings {65767,
	title = {Smart diagnosis algorithm of the open-circuit fault in a photovoltaic generator},
	journal = {2015 3rd International Conference on Control, Engineering \& Information Technology (CEIT)},
	year = {2015},
	month = {2015},
	pages = {1-5},
	publisher = {IEEE},
	isbn = {1-4799-8212-1},
	author = {Rezgui, Wail and MOUSS, Nadia Kinza and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@article {66185,
	title = {Electrical faults modeling of the photovoltaic generator},
	journal = {International Review on Modelling and SimulationsInternational Review on Modelling and Simulations},
	volume = {7},
	year = {2014},
	month = {2014},
	pages = {245-257},
	author = {Rezgui, Wail and Mouss, Le{\"\i}la-Hayet and MOUSS, Nadia Kinza and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@proceedings {66181,
	title = {Faults modeling of the impedance and reversed polarity types within the PV generator operation},
	journal = {3rd International Symposium on Environmental Friendly Energies and Applications (EFEA)},
	year = {2014},
	month = {2014},
	pages = {1-6},
	publisher = {IEEE},
	isbn = {1-4799-7517-6},
	author = {Rezgui, Wail and MOUSS, Nadia Kinza and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@proceedings {66182,
	title = {Modeling the PV generator behavior submit to the open-circuit and the short-circuit faults},
	journal = {3rd International Symposium on Environmental Friendly Energies and Applications (EFEA)},
	year = {2014},
	month = {2014},
	pages = {1-6},
	publisher = {IEEE},
	isbn = {1-4799-7517-6},
	author = {Rezgui, Wail and MOUSS, Nadia Kinza and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@proceedings {66183,
	title = {New Algorithm for the IV Characteristic Modeling of the Photovoltaic Generator Malfunction within Impedance and Reversed Polarity Faults},
	journal = {2014 IEEE EFEA},
	year = {2014},
	month = {2014},
	pages = {1-6},
	author = {Rezgui, Wail and MOUSS, Nadia Kinza and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@article {66186,
	title = {Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator},
	journal = {International Review on Modelling and Simulations (IREMOS)International Review on Modelling and Simulations (IREMOS)},
	volume = {7},
	year = {2014},
	month = {2014},
	pages = {863-870},
	author = {Rezgui, Wail and Mouss, Kinza-Nadia and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Amirat, Yassine and Benbouzid, Mohamed}
}
@proceedings {66180,
	title = {A regression algorithm for the smart prognosis of a reversed polarity fault in a photovoltaic generator},
	journal = {2014 First International Conference on Green Energy ICGE 2014},
	year = {2014},
	month = {2014},
	pages = {134-138},
	publisher = {IEEE},
	isbn = {1-4799-3602-2},
	author = {Rezgui, Wail and MOUSS, Nadia Kinza and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Benbouzid, Mohamed}
}
@proceedings {66184,
	title = {A smart algorithm for the diagnosis of short-circuit faults in a photovoltaic generator},
	journal = {2014 First International Conference on Green Energy ICGE 2014},
	year = {2014},
	month = {2014},
	pages = {139-143},
	publisher = {IEEE},
	isbn = {1-4799-3602-2},
	author = {Rezgui, Wail and Mouss, Le{\"\i}la-Hayet and MOUSS, Nadia Kinza and Mouss, Mohamed Djamel and Benbouzid, Mohamed}
}
@article {67336,
	title = {Fuzzy pattern recognition based fault diagnosis},
	journal = {International Review on Modelling and Simulations (I. RE. MO. S.)International Review on Modelling and Simulations (I. RE. MO. S.)},
	volume = {4},
	year = {2011},
	month = {2011},
	pages = {3361-3370},
	author = {Bensaadi, Rafik and Mouss, Le{\"\i}la-Hayet and Mouss, Mohamed Djamel and Benbouzid, Mohamed}
}
@proceedings {61625,
	title = {Modeling and Evaluation of Single Machine Flexibility Using Fuzzy Entropy and Genetic Algorithm Based Approach},
	year = {2011},
	author = {Mouss, Hayet and Mouss, M Djamel and Benbouzid, Mohamed}
}
@proceedings {62290,
	title = {Comparative analysis of estimation techniques of SOFC induction motor for electric vehicles},
	year = {2008},
	month = {2008},
	pages = {1-6},
	author = {Haddoune, Abdelhakim and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Ghouili, Jamel and Srairi, Kamel}
}
@article {62291,
	title = {Modeling, analysis and neural network control of an EV electrical differential},
	journal = {IEEE Transactions on Industrial ElectronicsIEEE Transactions on Industrial Electronics},
	volume = {55},
	year = {2008},
	month = {2008},
	author = {Haddoune, Abdelhakim and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Ghouli, J. and Srairi, K.}
}
@proceedings {62166,
	title = {Analysis, modelling and neural network traction control of an electric vehicle without differential gears},
	volume = {1},
	year = {2007},
	month = {2007},
	pages = {854-859},
	author = {Haddoune, Abdelhakim and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Ghouili, Jamel and Srairi, Kamel}
}
@proceedings {62167,
	title = {Comparative analysis of control techniques for efficiency improvement in electric vehicles},
	year = {2007},
	month = {2007},
	pages = {629-634},
	author = {Haddoune, Abdelhakim and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Ghouili, Jamel and Srairi, Kamel}
}
@article {62168,
	title = {A loss minimisation DTC scheme for EV induction motors},
	journal = {IEEE Transactions on Vehicular TechnologyIEEE Transactions on Vehicular Technology},
	volume = {56},
	year = {2007},
	month = {2007},
	author = {Haddoune, Abdelhakim and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Ghouli, Jamel and Srairi, Kamel}
}
@proceedings {62169,
	title = {SDTC neural network traction control of an electric vehicle without differential gears},
	year = {2007},
	month = {2007},
	pages = {259-266},
	author = {Haddoune, Abdelhakim and Khoucha, F. and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Srairi, Kamel}
}
@article {62064,
	title = {Induction motors stator fault detection and diagnosis by a rather simple fuzzy logic approach. A case study for electric machines fuzzy condition monitoring},
	journal = {Journal of Electrical Engineering, RomaniaJournal of Electrical Engineering, Romania},
	year = {2006},
	month = {2006},
	author = {Mouss, M. D. and Mouss, L. H. and Abdessemed, Rachid and Benbouzid, Mohamed}
}
@proceedings {62046,
	title = {Sliding mode control of EV electric differential system},
	journal = {ICEM{\textquoteright}06},
	year = {2006},
	month = {2006},
	pages = {6pp},
	author = {Haddoun, Abdelhakim and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Ghouili, Jamel and Srairi, Kamel}
}
@proceedings {61988,
	title = {Default Diagnosis and A.I.T},
	year = {2005},
	month = {2005},
	author = {Nezzar, Malika and Benbouzid, Mohamed and Mouss, Hayet and Abdessemed, Rachid}
}
@proceedings {61942,
	title = {Fuzzy Pattern Recognition Based Fault Diagnosis.},
	journal = {ICEIS (2)},
	year = {2005},
	month = {2005},
	pages = {347-356},
	author = {Bensaadi, Rafik and Mouss, Hayet and Mouss, Mohamed Djamel and Benbouzid, Mohamed}
}
@proceedings {61972,
	title = {A loss-Minimization DTC scheme for EV Induction motors},
	year = {2005},
	month = {2005},
	pages = {315-321},
	author = {Haddoune, Abdelhakim and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid and Ghouili, Jamel and Srairi, Kamel}
}
@article {61866,
	title = {Automatic default diagnosis for an asynchronous machine fed by a PWM converter},
	journal = {JEE, Romania.JEE, Romania.},
	volume = {3},
	year = {2003},
	month = {2003},
	pages = {23-27},
	author = {Nezzar, Malika and Abdessemed, Rachid and Benbouzid, Mohamed and Mouss, Leila-Hayet}
}
@article {61798,
	title = {Adaptive fuzzy logic control for high performance PM synchronous drives},
	journal = {Electrical Power Components and systems, Taylor \&FrancisElectrical Power Components and systems, Taylor \&Francis},
	volume = {29},
	year = {2001},
	month = {2001},
	author = {Kadjoudj, M. and Abdessemed, Rachid and Golea, Noureddine and Benbouzid, Mohamed}
}
@article {61812,
	title = {A Fuzzy rotor resistance updating scheme for an IFOC induction motor drive},
	journal = {Power engineering Review, IEEEPower engineering Review, IEEE},
	volume = {21},
	year = {2001},
	month = {2001},
	pages = {47-50},
	author = {Zidani, Fatiha and Nait-Said, Mohamed-Said and Benbouzid, Mohamed and Diallo, Demba and Abdessemed, Rachid}
}
@proceedings {61799,
	title = {A Robust hybrid current control for permanent magnet synchronous motor Drive},
	volume = {3},
	year = {2001},
	month = {2001},
	pages = {2068-2073},
	author = {Kadjoudj, M. and Benbouzid, Mohamed and Abdessemed, Rachid and Ghennai, C.}
}
