Dans cette thèse, nous nous sommes intéressés à un modèle de gestion des connaissances des entreprises industrielles. Certaines tâches manufacturières impliquent un niveau élevé de connaissance tacite des opérateurs qualifiés. L’industrie a besoin des méthodes fiables pour la capture et l’analyse de ces connaissances tacites afin qu’elles puissent être partagées et sans aucune perte. Nous proposons, un modèle de gestion contenant deux processus de gestion, le premier processus est la capitalisation des connaissances basée sur une tâche industrielle. Nous avons utilisé une combinaison de deux méthodologies : une méthodologie d’ingénierie de connaissances CommonKADS et une méthodologie d’élicitation des connaissances MACTAK. Dans la phase de modélisation, nous avons utilisé deux différentes techniques de modélisation, une modélisation basée sur les connaissances d’expert et la deuxième une représentation ontologique. Ce modèle facilite la capture des connaissances d’experts et transforme les connaissances tacites en explicites avec une maximisation des règles de production. Le deuxième processus concerne le partage des connaissances à base d’une ontologie des Tâches Manufacturières MATO en identifiant un ensemble des concepts de fabrication et leurs relations, cette ontologie proposée facilite le partage des connaissances entre les tâches de fabrication et aide à partager et à réutiliser les connaissances durant l’exécution des tâches. Ensuite, une application proposée pour le diagnostic de système d’alarme dans une centrale thermique a été présentée pour démontrer l’importance et l’apport de l’ontologie.

This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.

Nowadays, solar energy, which is the direct conversion of light into electricity, occupies a very important place among renewable energy resources due to its daily availability in most regions of the globe. Therefore, the wise exploitation of this clean energy will ultimately drive to cover all needed demands [1, 2]. This paper deals with the design of Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) system using a modified incremental conductance (IncCond) algorithm to extract maximum power from PV module. The considered PV system consists of a PV module, a DC-DC converter and a resistive load. In the literature, it is known that the conventional MPPT algorithms suffer from serious disadvantages such as fluctuations around the MPP and slow tracking during a rapid change in atmospheric conditions. Therefore, in this paper, and attempting to overcome the shortcomings of conventional approach. In this work, a new modified incremental conductance algorithm is proposed to find the Maximum Power Point Tracking (MPPT) of the Photovoltaic System. Simulation tests with different atmospheric conditions are provided to demonstrate the validity and the effectiveness of the proposed algorithm.

The automation of manufacturing systems is a major obligation to the developments because of exponential industrial equipment, and programming tools, so that growth needs and customer requirements. This automation is achieved in our work through the application programming tools from Siemens, which are PCS 7 (Process Control System) for industrial process control and FuzzyControl++ for fuzzy control. An industrial application is designed, developed and implemented in the cement factory in Ain-Touta (S.CIM.AT) located in the province of Batna, East of Algeria. Especially in the cement mill which gives the final product that is the cement.

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.

This paper investigates capacity planning with outsourcing under reliability-maintenance constraints. The considered supply-chain consists of a single-manufacturer and multiple-subcontractors. The manufacturer’s company is composed of a single unit subject to random failures. Corrective maintenance is endorsed when failures occur, and preventive maintenance can be carried out to reduce the degradation. The high in-house costs and the incapacity motivate the manufacturer outsourcing to independent subcontractors. In addition, based on the principle of comparative advantage, the manufacturer balances between in-house capacities and outsourcing services, which minimises the total cost. The aim is to propose a new policy based on the combination between integrated-maintenance and outsourcing policies. A mathematical model and an optimisation procedure have been developed in order to determine the best in-house production-maintenance and outsourcing plans for the manufacturer while minimising the total cost. In order to show the applicability of our approach, we conduct experimentations to study the management insights.

L’évolution récente des moyens de la communication sans fil a permet la manipulation de l’information à travers des unités de calculs portables, appelés capteurs. Ces derniers, qui ont des caractéristiques particulières, sont capables de récolter, de traiter et de transmettre des données environnementales d’une manière autonome.Dans ce livre sont introduites les connaissances de base nécessaires à la bonne compréhension des capteurs intelligents, des réseaux de capteurs et les différents types protocoles de routage spécifiques aux réseaux de capteurs. Nous fournirons ainsi les définitions généralement acceptées par ce type de réseau. Nous aborderons également par une description succincte les principales caractéristiques, contraintes et facteurs conceptuels qui surviennent dans ces réseaux. Nous présenterons ensuite les différentes orientations prises aux applications des réseaux de capteurs.

A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an alternative diagnosis methodology for breast cancer, this study presents a computer-aided diagnosis system based on convolutional neural networks (CNN) using thermal images. We demonstrate that CNNs are faster, reliable and robust when compared with different techniques. We study the influence of data pre-processing, data augmentation and database size on several CAD models. Among the 57 patients database, our CNN models obtained a higher accuracy (92%) and F1-score (92%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50, and Inception. This study exhibits that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database (up to 33%) but without data-augmentation. Finally, this study proposes a computer-aided system for breast cancer diagnosis but also, it stands as baseline research on the influence of data-augmentation and database size for breast cancer diagnosis from thermal images with CNNs

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

Requirements of users in developing countries differ from those of developed countries. This difference can be seen through wheelchair displacement in infrastructures that don’t meet international standards. However, developing countries are obliged to purchase products from developed countries that don’t necessarily meet all user’s requirements. The modification of these requirements will generate disruption on all the supply chain. This paper proposes a model for optimising the cost of requirement modification on the supply chain and seeks to evaluate the introduction of a new requirement on an existing product/process. This model is adapted to the redesign and development of products, such as wheelchairs, satisfying specific Algerian end-user requirements.

Solar energy is a vast and clean resource that can be harnessed with great benefit for humankind. It is still currently difficult, however, to convert it into electricity in an efficient and cost-effective way. One of the ways to produce energy is the use of various focusing technologies that concentrate the direct normal irradiance (DNI) to produce power through highly-efficient modules or conventional turbines. Concentrating technologies have great potential over arid areas, such as Northern Africa. A serious issue is that DNI can vary rapidly under broken-cloud conditions, which complicate its forecasts [1]. In comparison, the global horizontal irradiance (GHI) is much less sensitive to cloudiness. As an alternative to the direct DNI forecasting avenue, a possibility exists to derive the future DNI indirectly by forecasting GHI first, and then use a conventional separation model to derive DNI. In this context, the present study compares four of the most well-known separation models of the literature and evaluates their performance at Tamanrasset, Algeria, when used in combination with a new deep learning machine methodology introduced here to forecast GHI time series for short-term horizons (15-min). The proposed forecast system is composed of two separate blocs. The first bloc seeks to forecast the future value of GHI based on historical time series using the Long Short-Term Memory (LSTM) technique with two different search algorithms. In the second bloc, an appropriate separation (also referred to as “diffuse fraction” or “splitting”) model is implemented to extract the direct component of GHI. LSTMs constitute a category of recurrent neural network (RNN) structure that exhibits an excellent learning and predicting ability for data with time-series sequences [2]. The present study uses and evaluates the performance of two novel and competitive strategies, which both aim at providing accurate short-term GHI forecasts: Unidirectional LSTM (UniLSTM) and Bidirectional LSTM (BiLSTM). In the former case, the signal propagates backward or forward in time, whereas in the latter case the learning algorithm is fed with the GHI data once from beginning to the end and once from end to beginning. One goal of this study is to evaluate the overall advantages and performance of each strategy. Hence, this study aims to validate this new approach of obtaining 15- min DNI forecasts indirectly, using the most appropriate separation model. An important step here is to determine which model is suitable for the arid climate of Tamanrasset, a high-elevation site in southern Algeria where dust storms are frequent. Accordingly, four representative models have been selected here, based on their validation results [3] and popularity: 1) Erbs model [4]; 2) Maxwell’s DISC model [5]; 3) Perez’s DIRINT model [6]; and 4) Engerer2 model [7]. In this contribution, 1-min direct, diffuse and global solar irradiance measurements from the BSRN station of Tamanrasset are first quality-controlled with usual procedures [3, 8] and combined into 15-min sequences over the period 2013–2017. The four separation models are operated with the 15-min GHI forecasts obtained with each LSTM model, then compared to the 15-min measured DNI sequences. Table 1 shows the results obtained by the two forecasting strategies, for the experimental dataset.

In the last years, predictive maintenance 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 extreme learning machines using data issued from a propulsion system 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.

Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS).However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.

Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning…etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.

In this paper, our objective is dedicated to the detection of a deterioration in the estimated operating time by giving preventive action before a failure, and the classification of breakdowns after failure by giving the action of the diagnosis and / or maintenance. For this reason, we propose a new Neuro-fuzzy assistance prognosis system based on pattern recognition called "NFPROG" (Neuro Fuzzy Prognosis). NFPROG is an interactive simulation software, developed within the Laboratory of Automation and Production (LAP) -University of Batna, Algeria. It is a four-layer fuzzy preceptor whose architecture is based on Elman neural networks. This system is applied to the cement manufacturing process (cooking process) to the cement manufacturing company of Ain-Touta-Batna, Algeria. And since this company has an installation and configuration S7-400 of Siemens PLC PCS7was chosen as a programming language platform for our system.

 Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central warehouse, three distribution centres and six wholesalers. Each of them faces a random demand. In order to optimise the inventory management in the distribution network, we first propose to make a horizontal cooperation between actors of the same level in the form of product exchange; then we propose a second approach based on vertical-horizontal cooperation. Both approaches are modelled as a MIP model and solved using the CPLEX solver. The objective of this study is to analyse the performance in terms of costs, quantities in stock and customer satisfaction.

Au cours des dernières années, les entreprises sont émergées dans un environnement concurrentiel avancé. Afin de répondre aux exigences de réduction des coûts, la demande des clients, les délais imposés, la qualité et l'amélioration de la variété, les entreprises doivent améliorer leur performance pour rester compétitives, survivre et se développer. Pour atteindre cet objectif, plusieurs modèles sont utilisés comme TQM (Total Quality Management), Kaizen, JAT (Just A Time), ERP (Enterprise Resource Planning), BPR (Business Process Reengineering) et Six Sigma, etc. Dans ce travail, nous nous sommes focalisés sur des modèles efficaces (aspirés de l’approche Six Sigma,) utilisés principalement pour justifier la compétitivité d'une entreprise. On s’intéresse particulièrement aux modèles DMAIC (Définir, Mesurer, Analyser, Innover et Contrôler, le modèle DPMO pondéré (défauts par million d'Opportunité) et au modèle ROF (Rendement optimal des flux. Ces modèles sont appliqués pour mesurer les niveaux de processus et d'évaluer la compétitivité de l'entreprise. Les résultats de ces modèles sont appliqués dans deux systèmes industriels de fabrication du ciment et des bouteilles à gaz.

Renewable energies offer new solutions to an ever-increasing energy demand. Wind energy is one of the main sources of electricity production, which uses winds to be converted to electrical energy with lower cost and environment saving. The major failures of a wind turbine occur in the bearings of high-speed shafts. This paper proposes the use of optimized machine learning to predict the Remaining Useful Life (RUL) of bearing based on vibration data and features extraction. Significant features are extracted from filtered band-pass of the squared raw signal where the health indicators are automatically selected using relief technique. Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) by Partical Swarm Optimization (PSO) is used to model the non linear degradation of the extracted indicators. The proposed approach is applied on experimental setup of wind turbine where the results show its effectiveness for RUL estimation.

In this paper we address the integration of two-level supply chain with multiple items, production facility and retailers’ demand over a considered discrete time horizon. This two-level production distribution system features capacitated production facility supplying several retailers located in the same region. If production does take place, this process incurs a fixed setup cost as well as unit production costs. In addition, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles and routing costs are incurred. This work aims at implementing a solution to minimize the sum of the costs at the production facility and the retailers. The methodology adopted to tackle this issue is based on a hybrid heuristic, greedy and genetic algorithms that uses strong formulation to provide a good solution of a guaranteed quality that are as good or better than those provided by the MIP optimizer with a considerably larger run time. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.