Purpose The reliability prediction is among the most important objectives for achieving overall system performance, and this prediction carried out by anticipating system performance degradation. In this context, the purpose of this research paper is to development of methodology for the photovoltaic (PV) modules’ reliability prediction taking into account their future operating context. Design/methodology/approach The proposed methodology is framed by dependability methods, in this regard, two methods of dysfunctional analysis were used, the Failure Mode and Effects Criticality Analysis (FMECA) method is carried out for identification of the degradation modes, and the Fault Tree Analysis (FTA) method is used for identification the causes of PV modules degradation and the parameters influencing its degradation. Then, based on these parameters, accelerated tests have been used to predict the reliability of PV modules. Findings The application of the proposed methodology on PWX 500 PV modules’ in different regions of Algeria makes it possible to predict its reliability, taking into account the future constraints on its operation. In this case, the temperature and relative humidity vary from one region to another was chosen as constraints. The results obtained from the different regions confirms the reliability provided by the designer of the Saharan cities Biskra, In Salah, Tamanraset, and affirms this value for the two Mediterranean cities of Oran and Algiers. Originality/value The proposed methodology is developed for the reliability prediction of the PV modules taking into account their future operating context and, the choice of different regions confirms or disproves the reliability provided by the designer of the PV modules studied. This application confirms their performance within the framework of the reliability prediction.

The fire-fighting system is one of the proactive technical barriers related to liquefied petroleum gas storage tank safety. This paper presents an integrated approach that uses fuzzy set theory, an improved-dependent uncertain ordered weighted averaging operator and fault tree analysis to handle uncertainty in the unavailability assessment of fire-fighting systems. In this study, the center of area is used to defuzzify triangular fuzzy numbers. Furthermore, for the fire-fighting system fault tree, importance analysis, including Fussell–Vesely importance measure and risk reduction worth of basic events, are performed to identify the weak links of the fire-fighting system. In addition, a real case study on a fire-fighting system for a liquefied petroleum gas storage system in an LPG unit in Algeria is provided to illustrate the effectiveness of the proposed methodology. The research results show that the proposed methodology makes it possible to assess the unavailability of the entire system by analyzing weak links. Consequently, some suggestions are given to take preventive–corrective actions in advance, in order to reduce the failure probability of fire-fighting system and assist the practitioners in setting priorities for improving safety procedures in liquefied petroleum gas storage tanks. The study provides references for analyzing safety barriers in a complex system.

Let T ∈ B(H) be a bounded linear operator on a complex Hilbert space H. For n ∈ N, an operator T ∈ B(H) is said to be n-normal if T nT ∗ = T ∗T n. In this paper we investigate a necessary and sufficient condition for the n-normality of ST and T S, where S, T ∈ B(H). As a consequence, we generalize Kaplansky theorem for normal operators to n-normal operators. Also, In this paper, we provide new characterizations of n-normal operators by certain conditions involving powers of Moore-Penrose inverse.

Introduction. Constant increases in power consumption by both industrial and individual users may cause depletion of fossil fuels and environmental pollution, and hence there is a growing interest in clean and renewable energy resources. Photovoltaic power generation systems are playing an important role as a clean power electricity source in meeting future electricity demands. 

Problem. All photovoltaic systems have two problems; the first one being the very low electric-power generation efficiency, especially under low-irradiation states; the second resides in the interdependence of the amount of the electric power generated by solar arrays and the ever changing weather conditions. Load mismatch can occur under these weather varying conditions such that maximum power is not extracted and delivered to the load. This issue constitutes the so-called maximum power point tracking problem.

 Aim. Many methods have been developed to determine the maximum power point under all conditions. There are various methods, in most of them based on the well-known principle of perturb and observe. In this method, the operating point oscillates at a certain amplitude, no matter whether the maximum power point is reached or not. That is, this oscillation remains even in the steady state after reaching the maximum power point, which leads to power loss. This is an essential drawback of the previous method. In this paper, a cascade sliding mode maximum power point tracking control for a photovoltaic system is proposed to overcome above mentioned problems. 

Methodology. The photovoltaic system is mainly composed of a solar array, DC/DC boost converter, cascade sliding mode controller, and an output load. Two sliding mode control design strategies are joined to construct the proposed controller. The primary sliding mode algorithm is designed for maximum power point searching, i.e., to track the output reference voltage of the solar array. This voltage is used to manipulate the setpoint of the secondary sliding mode controller, which is used via the DC-DC boost converter to achieve maximum power output. 

Results. This novel approach provides a good transient response, a low tracking error and a very fast reaction against the solar radiation and photovoltaic cell temperature variations. The simulation results demonstrate the effectiveness of the proposed approach in the presence of environmental disturbances.

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems.

Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. 

Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. 

Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

This article deals with the problem of blind separation of statistically independent sources from the instantaneous linear model (n × n). When the observation signals are affected by the additive white gaussian noise (AWGN), the implementation of the proposed solution is performed by following three steps. The first step is a whitening process. The second step aims to convert the uncorrelated signals into statistically independent signals. The last step consists in reducing the noise existing in the noisy estimations. The main part of the proposed solution is to determine the adequate rotating angle (θ) that maximizes the kurtosis of the whitened signals. This rotating angle is obtained through the use of optimization techniques by applying a genetic algorithm. The proposed solution has the advantage of not converging to a local maximum, and also the separation method can be easily generalized to converge directly towards the global maximum for the case of several sources. The results obtained by applying many simulations, prove the effectiveness and the performance of the proposed method even in the noisy case and whatever the type of the signals (stationary or non-stationary).

This paper deals with the design and optimization of a 1.2 MVA medium-voltage (MV) power electronic traction transformer (PETT) for an AC 15 kV/16.7 Hz railway grid, in which a simple two-stage multi-cell PETT topology consisting of a bidirectional 170 kW, 2.5 kV AC rms to 6 kV DC power factor corrected (PFC) converter stage followed by a bidirectional isolated 46 kHz, 6 kV to 1.5 kV series resonant DC/DC converter for each cell is presented. This paper presents a methodology that maximizes the converter"s efficiency and minimizes the converter"s size and weight. Accordingly, the first stage employs 10 kV SiC MOSFETs based on the integrated Triangular Current Mode (iTCM). The second stage uses 10 kV SiC MOSFETs on the MV-side, 3.3 kV SiC MOSFETs on the LV-side, and a medium frequency (MF) MV transformer operating at 46 kHz. MF transformers offer a way to reduce weight and improve energy efficiency, particularly in electric multiple-unit applications. The MF MV transformer requires power electronic converters, which invert and rectify the voltages and currents at the desired operating frequency. The development of high voltage SiC MOSFETs, which can be used instead of Si IGBTs in PETT topologies, increases the operating frequency without reducing the converter"s efficiency. The designed MV PETT achieves 98.95% efficiency and 0.76 kVA/kg power density.

The growth of manufacturing industries and the huge competitive environment forced manufacturing organizations to develop advanced improvement strategies and enhance their sustainability performance. The integration of sustainable Manufacturing in industrial operations leads to enhanced process performances through the reduction of wastes, cost, and environmental impacts and satisfies ergonomic conditions. For this reason, various firms have adopted sustainable manufacturing concepts to enhance their performances and hold a prestigious competitive position. The purpose of this research is to develop an integrated Pythagorean Fuzzy MCDM model to enhance the application process of the conventional Lean Manufacturing approach (LM). Firstly, an extended Value Steam Mapping is proposed to assess the sustainability of the manufacturing process and identify the causes of waste from a sustainability viewpoint. Secondly, Pythagorean Fuzzy Decision-Making Trial And Evaluation Laboratory (PF-DEMATEL) is employed to analyze the interrelationship among the identified. Thirdly, Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS) is introduced to prioritize a set of solutions in order to overcome the investigated causes and improve the durability of the manufacturing operations. Finally, sensitivity analysis is conduced to assess the effectiveness of the obtained results. The proposed method has several attractive features. It can address the drawbacks of the conventional LM and enhance its analysis and improvement tasks. However, the proposed approach offers an advanced application process for Lean Manufacturing in a sustainability context. Additionally, the suggested strategy facilitates the leaders to assess the current state of the manufacturing processes and select the appropriate solutions for successful sustainability implementation. The validity of the proposed approach was investigated in a real case study. The results confirm its effectiveness and indicate that using MCDM approaches in LM application process offers a consistent and flexible demarche for sustainable manufacturing implementation.

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.

Over the past few decades, Lean Manufacturing (LM) has been the pinnacle of strategies applied for cost and waste reduction. However as the search for competitive advantage and production growth continues, there is a growing consciousness towards environmental preservation. With this consideration in mind this research investigates and applies Value Stream Mapping (VSM) techniques to aid in reducing environmental impacts of manufacturing companies. The research is based on empirical observation within the Chassis weld plant of Company X. The observation focuses on the weld operations and utilizes the cross member line of Auxiliary Cross as a point of study. Using various measuring instruments to capture the emissions emitted by the weld and service equipment, data is collected. The data is thereafter visualised via an Environmental Value Stream Map (EVSM) using a 7-step method. It was found that the total lead-time to build an Auxiliary Cross equates to 16.70 minutes and during this process is emitted. It was additionally found that the UPR x LWR stage of the process indicated both the highest cycle time and carbon emissions emitted and provides a starting point for investigation on emission reduction activity. The EVSM aids in the development of a method that allows quick and comprehensive analysis of energy and material flows. The results of this research are important to practitioners and academics as it provides an extension and further capability of Lean Manufacturing tools. Additionally, the EVSM provides a gateway into realising environmental benefits and sustainable manufacturing through Lean Manufacturing.

An effective Food Traceability System (FTS) in a Food Supply Chain (FSC) should adequately provide all necessary information to the consumer(s), meet the requirements of the relevant agencies, and improve food safety as well as consumer confidence. New information and communication technologies are rapidly advancing, especially after the emergence of the Internet of Things (IoT). Consequently, new food traceability systems have become mainly based on IoT. Many studies have been conducted on food traceability. They mainly focused on the practical implementation and theoretical concepts. Accordingly, various definitions, technologies, and principles have been proposed. The “traceability” concept has been defined in several ways and each new definition has tried to generalize its previous ones. Nevertheless, no standard definition has been reached. Furthermore, the architecture of IoT-based food traceability systems has not yet been standardized. Similarly, used technologies in this field have not been yet well classified. This article presents an analysis of the existing definitions of food traceability, and thus proposes a new one that aims to be simpler, general, and encompassing than the previous ones. We also propose, through this article, a new architecture for IoT-based food traceability systems as well as a new classification of technologies used in this context. We do not miss discussing the applications of different technologies and future trends in the field of IoT-based food traceability systems. Mainly, an FTS can make use of three types of technologies: Identification and Monitoring Technologies (IMT), Communication Technologies (CT), and Data Management Technologies (DMT). Improving a food traceability system requires the use of the best new technologies. There is a variety of promising technologies today to enhance FTS, such as fifth-generation (5G) mobile communication systems and distributed ledger technology (DLT).

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.

Permanent Ground Deformations (PGD) that follow slope failures caused catastrophic damages on buried pipelines. This paper presents a two-dimensional numerical analysis of the behavior of an 800mm water transport pipeline buried in the Aine-Tine slope (Mila, Algeria) subjected to shallow PGD triggered by the recent earthquake of August 07th, 2020 (M= 4.9). The soil-pipeline interaction was simulated focusing on the effect of (1) the magnitudes of the PGD and (2) the rigidity of the pipeline on the structural response of the pipeline. The pipeline deformations (i.e., translation and ovalization) and radial internal efforts (i.e., axial forces F_A, shear forces F_S, and bending moments M_B) result highlighted that shallow PGD can cause additional loads on pipelines that are proportional to the magnitude of PGD. Moreover, it was found that rigid pipelines are more performant than flexible pipelines. Through a simplified numerical simulation, the study helps engineers and planners to predict the actual causes of pipeline leaks and ruptures leading to severe disruption of their normal operations.

Accessibility of fresh water, the nature’s gift wheels the foremost part of the world economy. The sufficient supplies of water are essential for agriculture, human intake, industry as well as regeneration. The Oued Righ region is located in Algeria’s South-East, specifically in the NorthEast of the Sahara, on the Northern edge of the Grand Erg Oriental and the Southern border of the Aures massif. This area appears as a lower Sahara synclinal basin and is part of a broad North-South trending ditch. It is famous for its date palms, the development of the date culture in this region is attributed not only to the population’s efforts, but above all to the particular climatic conditions, the favorable soil characteristics and the existence of significant groundwater. The aim of this study is to understand the results obtained from using different approaches of water hydrodynamics in the Complex Terminal aquifer. The aquifer’s hydrodynamic characterization was carried out using hydrodynamic parameters and piezometry. As a result, the transmissivity and permeability obtained data using traditional Cooper-Jacob method showed that the flow capacities of the aquifer environment and the productivities of the structures are important in the studied zone where, the highest value of transmissivity equal 2.36× 102-m 2 /sis found in the central part of the study area in El-Meghair. The establishment of piezometric maps reveals a flow direction oriented toward the chott.

This paper is about the study of the well-posedness of a stochastic Cahn-Hilliard equation driven by white noise induced by a Q-Brownian motion. The proof of the existence of a unique global solution relies on the Galerkin method together with a monotonicity method.

Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI-based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL-based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO₂ (AEC). In addition, the cooperation between the different trained DRL-based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested.

In this paper, we consider the stationary problem in three dimensional thin domain ΩεΩε with maximal monotone graph and Tresca conditions. In the first step, we present the problem statement and give the variational formulation. We then study the asymptotic behavior when one dimension of the domain tends to zero. In the latter case a specific Reynolds limit equation is obtained and the uniqueness of the displacement of the limit problem are proved.

In most industrial environments, vibration analysis is widely used for fault diagnosis of rolling bearings. The vibration signal measured from a bearing represents a mixture of motor vibration, rolling vibration, noise, and other sources. Due to the high cost of devices and limited space, only one sensor can be installed to measure this signal. In this paper, a feature extraction method based on Single Channel Blind Source Separation (SCBSS) and Normal Distribution (ND) is proposed for vibration monitoring of rolling element bearings. To decompose the bearing signal, SCBSS is applied for separating the different sources. Because ND is sensitive to the type of fault, it is used as criterion to find an output that contains the maximum information about the fault by removing the other sources. In fact, the obtained signal contains other vibrations which affect the correct source of fault. A second SCBSS filter is, therefore, proposed to decompose the selected source and thus improves the performance of fault diagnosis. The application of the proposed method is carried out on a deep groove ball bearing with outer race fault, ball fault, and inner race fault in order to better validate the diagnosis results.

This paper proposed a new strategy of sinusoidal pulse width modulation (SPWM) technique to control three-phase nine-level switched-capacitor inverter (9LSCI) in grid-connected PV systems. The main advantage of this inverter is high voltage gain, achieved by switching the capacitors in series and parallel to boost up the output voltage using low voltage input. To improve the quality of solar energy for injection into the electrical grid, a rooted tree optimization (RTO) algorithm is used to get optimum values of initial angles of multi carriers SPWM technique, giving the lowest possible values of the total harmonic distortion (THD). The design also can maximize the efficiency of the multi-level inverter by minimizing its size using fewer components and a single DC source and reducing the rate of THD. The higher effectiveness and accuracy of the suggested RTO-SPWM technique was tested and verified in comparison to existing classical SPWM technique from the performance of PV-grid systems that it can effectively reduce the total harmonic distortion to 0.16 %.