Publications by Year: 2016

2016

Nafissa, Rezki. 2016. “Using several intelligences for complex industrial process monitoring: Detection and diagnosis”. In IOEM 2016 International Conference on Industrial Engineering and Operations Management At Kuala Lumpur, Malaysia ,. Publisher's Version Abstract

The objective of the current paper is to present an intelligent system for complex process monitoring, based on

artificial intelligence technologies. This system aims to realise with success some complex process monitoring tasks that are:

detection, diagnosis. For this purpose, the development of a multi-agent system that combines multiple intelligences such as:

multivariate control charts, neural networks, has became a necessary. The proposed system is evaluated in the monitoring of the

complex process Tennessee Eastman Process.

Hanane, Zermane. 2016. “Development of an internet-based fuzzy control system of manufacturing plant”. IOEM 2016 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia . Publisher's Version

Ouahab, Kadri. 2016. “Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO, ISSN / e-ISSN 2157-362X / 2157-3611”. In ICIEM 2016 : 18th International Conference on Industrial Engineering and Management, Barcelona, Spain,. 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.

Hatem, Mezache. 2016. “Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction.”. IREC2016 Seventh International Renewable Energy Congress. Publisher's Version Abstract

Nowadays, wind power andprecise forecasting are of great importancefor the development ofmodern electricalgrids.In this paperwepropose aprediction systemfor time seriesbased onKernelPrincipalComponent Analysis(KPCA) andExtremeLearningMachine(ELM). To compare the proposed approach, threedimensionality reduction techniques were used:full space (50 variables), part of space (last four variables) and classical Principal Components Analysis (PCA). These models were compared using three evaluation criteria: mean absoluteerror (MAE), root mean squareerror (RMSE), and normalizedmean square error (NMSE). The results show that the reduction of the original input space affectspositively the prediction output of the wind speed.Thus,It can be concluded that the non linear model (KPCA)model outperform the other reduction techniques in terms of prediction performance.

Adel, Abdelhadi, and Kadri Ouahab. 2016. “ An Efficient Hybrid Approach Based on Multi-Agent System and Emergence Method for the Integration of Systematic Preventive Maintenance Policies, ISSN 2279-0764”. IJCIE International Journal of Computer and Information Engineering Vol 3 (N°4). 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.

Ouahab, Kadri, Abdelhadi Adel, and Mouss Leila Hayet. 2016. “Toolbox Supports Group Awareness In Groupware, ISSN 1583-7165”. Annals. Computer Science Series. : pp. 117-122. Publisher's Version Abstract

Group awareness tools are developed to minimize the time of cooperative application realization and spare designers a lot of effort devoted to integrating the group awareness aspect into groupware. But these tools have several disadvantages, such as dependence on a single type of application or overloading the minds of users with unnecessary information. From here comes the need to develop a tool that allows to offer information of group awareness configurable and to be both generic and easy to use. Our article presents some tools that have inspired several ideas. It proposes a design of a new toolbox that allows a better interpretation of group awareness information. Finally, it presents a variant of the client/server architecture based on work area.

Ouahiba, Chouhal, et al. 2016. “A Multi-Agent Solution to Distributed Fault Diagnosis of Preheater Cement Cyclone, ISSN / e-ISSN 0219-6867 / 1793-6896”. Journal of Advanced Manufacturing Systems Vol 15 (Issue 04) : pp. 209-221. Publisher's Version Abstract

Systems health monitoring is essential to guaranteeing the safe, efficient, and reliable operation of engineering systems. Integrated systems health management methodologies include fault diagnosis mechanism. Diagnosis involves detecting when a fault has occurred, isolating the true fault, and identifying the true damage to the system. This important issue is even harder when the systems to be diagnosed are dynamic and spatially distributed systems with their successively increasing complexity. For such systems, a single diagnostic entity having a model of the whole system approach is inappropriate. Whereas a distributed approach of multiple diagnostic agents can offer a solution. An overall systematic solution for these issues could be obtained by an artificial intelligent mechanism called the multi-agent system (MAS). This paper presents a MAS model for fault diagnosis based on logical theory of diagnosis. In this approach, each local diagnostic agent has knowledge above its subsystem and an abstract view of the neighboring subsystems and it is able to determine the local minimal diagnoses that are consistent with global diagnoses. The multi-agent models are simulated in Java Agent Development Framework and are applied to the preheated cement cyclone in the workshop of SCIMAT clinker.

Nafissa, Rezki, et al. 2016. “On the use of multi-agent systems for the monitoring of industrial systems, ISSN /e-ISSN 1735-5702 / 1735-5702”. Journal of Industrial Engineering International vol 12 : pp. 111–118. Publisher's Version Abstract

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

Khadija, Abid, et al. 2016. “A Novel Approach for Mobile Maintenance Using Mobile Agents Technology and Mobile Devices, ISSN / e-ISSN 0219-6867 / 1793-6896”. Journal of Advanced Manufacturing Systems 14 (2) : pp. 55-74. Publisher's Version Abstract

The long use of a system causes its degradation. Hence, the maintenance activity is required in order to keep and improve the efficiency in the system. With the rapid development in networking technology, a need appears to change the manufacturing strategies. These new technologies improve the maintenance process, and establish remote maintenance (tele-maintenance, e-maintenance and m-maintenance). These kinds of maintenance try to provide personnel maintenance with the right information at the suitable time, which makes information available, anywhere and anytime. Our proposition is the use of mobile agent technology to reduce the maintenance costs and solve the problem of the unavailability of an expert in all phases of condition-based maintenance (CBM) strategy. The mobile agent technology overcomes a lot of problems and there is not much work that has used this technology. We have also used the web services (WS) to insure interoperability between machines and to support interaction over the network. Our approach gives great support to the maintenance engineer as it facilitates the access to decision-making support, work order, etc. which are available in the device like smartphone. This paper presents the development of a mobile maintenance support system based on mobile agent technology. The proposed system, the web and agent technology as well as remote communication were tested successfully.

Adel, Chouari, Hamouda Chaabane, and Ghagui Azziz. 2016. “ Data monitoring and performance analysis of a 1.6kWp grid connected PV system in Algeria, ISBN 1309-0127 ”. IJRER International Journal of Renewable Energy Research Vol 6 (N°1) : 34-42. Publisher's Version Abstract

The present study deals with the performance of a 1.6kWp grid connected PV system installed at Batna University, in Algeria. The average solar energy received was 5.21 kWh/m².d, the grid connected PV system seems to be a good candidate for generating electricity in this region. The system was monitored during one year of continuous operation and data analysis to evaluate the performance of the grid connected PV system. The performance ratio of the system ranged between 51 and 61%. Furthermore, the total produced energy by the PV array was 1931.7kWh and the supplied energy to the grid was 1705kWh. The annual final yield was 1065.6kWh/kWp. Moreover, an analysis of the energy losses in the system was performed, this makes it possible to determine the effect of the capture and system losses on the total energy balance of the system. All the electricity generated by the system was fed into the internal electrical grid of the university.

Badreddine, Boutagriout, Chaabane Hamouda, and Smadi Hacene. 2016. “Theoretical and experimental investigation of solar heat potential at low temperatures: Towards large scale integration in the agro food sector, ISBN 1309-0127”. IJRER International Journal of Renewable Energy Research Vol.6 ( No.1). Publisher's Version Abstract

Several applications in the agro food sub-sector have been classified as suitable to be supplied by solar heating systems. The present work deals with the potential evaluation of solar thermal energy under climatic conditions of Algeria. In a first part, an experimental investigation conducted on small scale solar water heating system is presented followed by simulation. The experimental tests revealed the thermal behavior of the system as well as the outlet temperature levels which can be reached by locally manufactured flat plat collectors. The second part presents the potential of solar heat for industrial applications, particularly those consisting of heating make-up water and water feedback from heat recovery wich exists in almost all the agro food industries. Thus, water inlet temperature through the secondary circuit is considered to be ranging from 20 to 90°C. The results showed that the annual specific energy yield delivered by the solar system decreases by increasing water feedback temperature.

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