Equipe 2

2017

Ouahab, Kadri, and Abdelhadi Adel. 2017. “An Efficient Hybrid Approach Based on Multi-Agent System and Emergence Method for the Integration of Systematic Preventive Maintenance Policies”. IJIME International Journal of Industrial and Manufacturing Engineering Vol 4 (N°5). Publisher's Version Abstract

This paper proposes a novel hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling

Adel, Abdelhadi, and Kadri Ouahab. 2017. “An Improved Approach Based on MAS Architecture and Heuristic Algorithm for Systematic Maintenance”. International Journal of Industrial and Manufacturing Engineering Vol 11 (N°5). Publisher's Version Abstract

This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Karima, Aksa, et al. 2017. “Gestion dynamique des carrefours à feux, e-ISSN 2421-9606”. IJMS - The International Journal of Multi-disciplinary Sciences Volume 1-17. Publisher's Version Abstract

Les technologies utilisées dans les systèmes de transport intelligents varient, allant de systèmes de gestion basiquescomme les systèmes de gestion des carrefours à feux, les systèmes de gestion des conteneurs, les panneaux à messages variables, radars automatiques ou la vidéosurveillance aux applications plus avancées qui intègrent des données en temps-réel avec retours d'informations de nombreuses sources, comme les informations météorologiques, ...etc.Cet article donne un bref aperçu surunegestion intelligente des carrefours à feuxutilisant des capteurs sans fils

Samia, Brahmi, Aitouche Samia, and Mouss Med Djamel. 2017. “ Measurement of Intellectual Capital in an Algerian Company, e-ISSN 2010-376X ”. International Journal of Economics and Management Engineering 11 (5) : 965-968. Publisher's Version Abstract

Every modern company should measure the value of its intellectual capital and to report to complement the traditional annual balance sheets. The purpose of this work is to measure the intellectual capital in an Algerian company (or production system) using the Weightless Wealth Tool Kit (WWTK). The results of themeasurement of intellectual capital are supplemented by traditional financial ratios. The measurement was applied to the National Company of Wells Services (ENSP) in Hassi Messaoud city, in thesouth of Algeria. We calculated the intellectual capital (intangible resources) of the ENSP to help the organization to better capitalize on its potential of workers and their know-how. The intangible value of the ENSP is evaluated at 16,936,173,345 DA in 2015

Abdelkrim, Soussa, et al. 2017. “The MAED and SVM for fault diagnosis of wind turbine system, e-ISSN 1309-0127”. International Journal of Renewable Energy Research-IJRER Vol 7 (N°2) : 758-769. Publisher's Version Abstract

Fault diagnosis is the best discipline to control the operation and maintenance costs of the wind turbine system. However, the fault diagnosis of wind turbine finds difficulties with the variation of wind speed and electrical energy (generator torque).

In this work, the proposed fault diagnosis approach is based on the Feature set algorithm, manifold learning and the Support Vector Machine classifier. First, the construction of the feature set is very important step, with the high dimension after application the MAED (Manifold Adaptive Experimental Design) algorithm on the data set. Moreover, the NPE(Neighborhood Preserving Embedding)manifold learning algorithm is applied for dimensionally reduction of feature set by the eigenvectors; it is easy to use as the input for the last step. Finally, the low dimension of eigenvectors is exploited by the Support Vector Machine classifier for recognition fault and making the maintenance decision.

This approach is implanted on the faults of the benchmark wind turbine and gives the best performance.

Mawloud, Titah, et al. 2017. “ Externalising and reusing of tacit knowledge in manufacturing task, ISSN / e-ISSN 1743-8268 / 1743-8276 ”. International Journal of Knowledge Management Studies Vol 8 (N° 3-4) : 351-374. Publisher's Version Abstract

In this paper, we present the application of knowledge engineering and externalisation of tacit knowledge in manufacturing industry, in order to improve the performance of a production system and save the knowledge capital of the company. The main aim of this study is to propose a knowledge model for manufacturing task combining common knowledge acquisition and design support (CommonKADS) and methodology for acquisition of tacit knowledge (MACTAK) methodologies, using two different knowledge base modelling based on two categories: (i) ontology and (ii) expert knowledge base. In that purpose, we suggest a process dedicated to industrial manufacturing, allowing to capitalise knowledge by: (1) Externalisation of tacit knowledge by MACTAK-methodology in industrial processes, (2) using knowledge engineering method; CommonKADS methodology, (3) Formalizing and modelling the domain knowledge using ontology and inference model, (4) presenting the implementation tool to support the knowledge model and (5) reusing the manufacturing knowledge model in decision support systems. The three pillars of methodology are: the externalisation process, Knowledge representation technique and quality tools. The proposed model is applied in manufacturing monitoring systems.

Karima, Aksa. 2017. “ Billiardo: A Novel Virtual Coordinates Routing Protocol Based on Multiple Sinks for Wireless Sensor Network, ISSN /e-ISSN 0929-6212 / 1572-834X ”. Wireless Personal Communications 94 (3) : 1147–1164. Publisher's Version Abstract

Geographic routing protocols based on virtual coordinate system are used in wireless sensor networks without GPS assistance or any localization technique. They rely completely on virtual coordinates derived from relative distances or hop counting to a set of anchor nodes in the sensor network. Despite the fact that the recently proposed virtual coordinate protocols have gained advantages as they are GPS free, they suffer from crucial inevitable problems. The reason for such a case lies, in fact, on these protocols which depend widely on the characteristic of “fixed reference points” (called anchors). The worst of these engendered problems is that of the unique reference framework where it is quite difficult to assign the existing nodes a unique identity. This lack of uniqueness cannot guarantee delivery and fails most of the time to forward the packet successfully. Moreover, a question rises here on how to select the anchors in order to use them in the field of work. Therefore; this paper comes to find out another way to solve the above-mentioned problems. The proposed routing protocol “Billiardo” is of greedy type based on virtual coordinates system. Its key idea is to use more than one sink, and all these sinks are used as anchors to allow each sensor to get its virtual coordinates. This protocol depends on hops’ count to find the shortest path towards just one selected sink among the other sinks without any complicated formula. Through tested simulation Billiardo proves to be far better and more efficient than the others to avoid all the thwarting problems in forwarding the packet.

2016

Ouahab, Kadri, Mouss Leila Hayet, and Mouss Med Djamel. 2016. “Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO, ISSN / e-ISSN 1738-494X / 1976-3824”. J MECH SCI TECHNOL. Publisher's Version Abstract

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

2015

Mounir, Aouadj, et al. 2015. “SCADA system for the modeling and optimization of oil collecting pipeline network: A case study of hassi messaoud oilfield ISSN / e-ISSN 2040-7459 / 2040-7467”. Research Journal of Applied Sciences, Engineering and Technology Vol 10 (Issue 7 ) : 789-804. Publisher's Version Abstract

This study aims are data acquisition, control and online modeling of an oil collection pipeline network using a SCADA «Supervisory Control and Data Acquisition» system, allowing the optimization of this network in real time by creating more exact models of onsite facilities. Indeed, fast development of computing systems makes obsolete usage of old systems for which maintenance became more and more expensive and their performances don't comply any more with modern company operations. SCADA system is a telemetry and control system adapted for particular requirements of an oilfield management. Thanks to its different functions, we take advantage of this system to solve production problems especially those related to oil collecting pipeline network. In fact this network is confronted to some problems, in particular pressure losses which has significant effect on the production. This problem can be taken under control by the awareness of pipeline network operation and all its process data (especially junctions) in real time. This will allow online creation of representative and accurate computerized models for the oil collecting pipeline network including producing wells, collecting pipelines, manifolds and others facilities.

Samia, Aitouche, et al. 2015. “Comparison and prioritisation of measurement methods of intellectual capital; IC-dVal, VAIC and NICI, ISSN / e-ISSN 1479-4853 / 1479-4861”. International Journal of Learning and Intellectual Capital 12 (2). Publisher's Version Abstract

The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.

Wail, Rezgui, et al. 2015. “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”. IREMOS International Review on Modelling and Simulations. 5197 DOI: https://doi.org/10.15866/iremos. Vol 8 ( issue 1) : 18-25. Publisher's Version Abstract

This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors.

Samia, Aitouche, et al. 2015. “SKACICM a method for development of knowledge management and innovation system e-KnowSphere, ISSN / e-ISSN 1755-8255 / 1479-4861”. International Journal of Knowledge and Web Intelligence Vol 5 (No 2). Publisher's Version Abstract

The purpose of this paper is to propose a hybrid method SKACICM of development of knowledge management systems. Based on weaknesses of the method of performance dashboards SKANDIA, we proposed a pragmatisation and adaptation of Skandia to give ASKANDIA, by enrichment of its performance book. We ameliorated CICM model against the requirements of GERAM to give ACICM model by mappings between their proposed metamodels. We tried to hybridise ACICM, ASKANDIA and business intelligence to propose a new method SKACICM of development of knowledge management systems. We applied SKACICM on a cement company to develop software containing three main modules, module knowledge management, module business intelligence and performance dashboard system. The developed system ameliorated the performance of the enterprise by 26% and could be generalised to other manufacturing or service systems.

Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective

Benbouzid, Mohamed, and Tarek Berghout. 2023. “Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective”. Electronics 12 (3) : 527. Publisher's Version Abstract

Data-driven prognostics and health management (PHM) is key to increasing the productivity of industrial processes through accurate maintenance planning. The increasing complexity of the systems themselves, in addition to cyber-physical connectivity, has brought too many challenges for the discipline. As a result, data complexity challenges have been pushed back to include more decentralized learning challenges. In this context, this perspective paper describes these challenges and provides future directions based on a relevant state-of-the-art review.

Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach

Berghout, Tarek, et al. 2023. “Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach”. IEEE Transactions on Transportation Electrification. Publisher's Version Abstract

Lithium-ion (Li-ion) batteries play an important role in providing necessary energy when acting as a main or backup source of electricity. Indeed, the unavailability of battery aging discharge data in most real-world applications makes the State of Health (SoH) assessment very challenging. Alternatively, accelerated aging is therefore adopted to emulate the degradation process and to achieve an SoH estimate. However, accelerated aging generates limited deterioration patterns suffering from a higher level of complexity due to the non-linearity and non-stationarity imposed by harsh conditions. In this context, this paper aims to provide a predictive model capable of solving incomplete data problems by providing two main solutions for each of the problems of complexity and missing patterns, respectively. First, to overcome the problem of lack of patterns, a robust collaborative feature extractor (RCFE) is designed by collaborating between a set of improved restricted Boltzmann machines (I-RBMs) to be able to share learning knowledge among different locally trained I-RBMs to create a more generalized global extraction model. Second, a set of RCFEs is then evolved through a neural network with an augmented hidden layer (NAHL) to enhance the predictive ability by further exploring representation learning to overcome pattern complexity issues. The designed RCFE-NAHL is trained to predict SoH using constant current (CC) discharge characteristics by implying multiple characteristics recorded through the constant voltage (CV) charging process as indicators of health. The proposed SoH prediction approach performances are evaluated on a set of battery life cycles from the well-known NASA database. In this context, the achieved results clearly highlight the higher accuracy and robustness of the proposed learning model.

2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

Othmane, Friha, et al. 2023. “2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT”. Computers & Security 127. Publisher's Version Abstract

Advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), underpin many of the innovations in Industry 4.0. However, the interconnectivity and open nature of such systems in smart industrial facilities can also be targeted and abused by malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP) Federated Learning (FL)-based IDS (2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three building blocks, namely: a key exchange protocol (for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of cyber attacks in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37%) with the centralized learning approach (94.37%) and outperforms the FL-based approach (93.91%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12%, 13%, and 9% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based IDS solutions.

Faulty Detection System Based on SPC and Machine Learning Techniques

Benrabah, Mohamed-Elamine, Ouahab Kadri, and Nadia-Kenza Mouss. 2023. “Faulty Detection System Based on SPC and Machine Learning Techniques”. Revue de l’Intelligence Artificielle : 969-977. Publisher's Version Abstract

Starting from a worrying observation, that companies have difficulties controlling the anomalies of their manufacturing processes, in order to have a better control over them, we have realized a case study on the practical data of the Fertial Complex to analyze the main parameters of the ammonia neutralization by nitric acid process. This article proposes a precise diagnostic of this process to detect dysfunction problems affecting the final product. We start with a general diagnosis of the process using the SPC method, this approach is considered an excellent way to monitor and improve the product quality and provides very useful observations that allowed us to detect the parameters that suffer from problems affecting the quality. After the discovery of the parameters incapable to produce the quality required by the standards, we applies two machine learning technologies dedicated to the type of data of these parameters for detected the anomaly, the first technique called The kernel connectivity-based outlier factor (COF) algorithm consists in recording for each object the degree of being an outlier, the second technique called the Isolation Forest, its principle is to establish a forest to facilitate the calculation and description. The results obtained were compared in order to choose which is the best algorithm to monitor and detect the problems of these parameters, we find that the COF method is more efficient than the isolation forest which leads us to rely on this technology in this kind of process in order to avoid passing a bad quality to the customer in future.

IoT-based food traceability system: Architecture, technologies, applications, and future trends

Mehannaoui, Raouf, Kinza-Nadia Mouss, and Karima Aksa. 2023. “IoT-based food traceability system: Architecture, technologies, applications, and future trends”. Food Control 145. Publisher's Version Abstract

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).

Laboratoire D'automatique et Productique