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.

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.

the work presented in this paper is dedicated to improving the methods of detection and diagnosis of faults affecting production systems, particularly photovoltaic systems. We proposed a new intelligent algorithm for the detection and diagnosis of PV installations, capable of detecting and resonate to define the type of defects that can affect this type of system. This new algorithm is based on the notion of pattern recognition, for that it is able to prepare the representation space and the decision space on the one hand, and on the other hand, the classification of all new observations collected during the functioning of the system. This algorithm mainly based on the method of knearest neighbor and two tools of artificial intelligence to improve this method and increasing the rate of its classification, which are fuzzy logic to optimize the location of the centers of gravity of classes and also the new observations, and the neural network that can classify the case of discharges ambiguity and releases distance which presents the limitations of the method of the k-nearest neighbor. We tested the performance of our algorithm on a database of a photovoltaic system at the research unit of GHARDAIA Algeria

Solar energy is expected to provide a major contribution to the future global energy supply, while helping the transition toward a carbon-free economy. Because of its variable character, its efficient use will necessitate trustworthy forecast information of its availability in a variety of spatial and time scales, depending on application. This study proposes a new forecasting approach for irradiance time series that combines mutual information measures and an Extreme Learning Machine (ELM). The method is referred to as Minimum Redundancy – Maximum Relevance (MRMR). To assess the proposed approach, its performance is evaluated against four scenarios: long window (latest 50 variables), short window (latest 5 variables), standard Principal Components Analysis (PCA) and clear-sky model. All these scenarios are applied to three typical forecasting horizons (15-min ahead, 1-h ahead and 24-h ahead). Based on measured irradiance data from 20 sites representing a variety of climates, the test results reveal that the selection of a good set of relevant variables positively impacts the forecasting performance of global solar radiation. The present findings show that the proposed approach is able to improve the accuracy of solar irradiance forecasting compared to other proposed scenarios.

This paper presents a multi-agent system for multivariate process monitoring. The proposed multi-agent system combines several intelligences which are: multivariate control charts, neural networks, Bayesian networks, and expert systems. This system aims to realise a complete control of complex industrial process. In order to demonstrate the efficiency of the proposed multi-agent system, it has been applied and evaluated in the monitoring of the complex process Tennessee Eastman process (TEP).

This paper proposes an approach of mobile maintenance (m-maintenance) that aims is to reduce the maintenance cost and to overcome the unavailability of experts. The Condition-Based Maintenance strategy is chosen and a three-layered framework based on mobile agent technology and web services is proposed. To ensure the reliability, Petri Nets are used in order to formally model and verify the proposed approach. Agents are modeled and their behavior with Reconfigurable Object Nets formalism. A case study on a production line is conducted to evaluate the proposed approach using LabVIEW environment. Results provide evidence of the applicability of the model.

The long use of a system in a manufacturing environment causes its degradation, thus the maintenance activity is required in this environment to keep and to improve the efficiency of the system. The new development in networking technologies enhances maintenance strategies and gives birth to remote maintenance (tele-maintenance, e-maintenance, m-maintenance). This maintenance makes information available anywhere/anytime and provides maintenance-personnel with the necessary information at the suitable time. This new type of maintenance reduces the maintenance costs and solves the problem of the unavailability of experts. Mobile agent as a rich design concept brings many facilities in the development of m-maintenance, however few works are elaborated in this stage. The objective of this work is both: 1) the proposition of a based mobile multi-agent architecture dedicated for m-maintenance in manufacturing systems; 2) the exploitation of high level petri nets in the specification, simulation and verification phases of the architecture development.

This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in unsupervised process monitoring.

The aim of this paper is to propose a new fault diagnosis method for complex manufacturing system. We have used an artificial neural network (ANN) and an Ant Colony Optimization (ACO) algorithm to diagnosis the condition monitoring of a rotary cement kiln. The Ant Colony algorithm can found a small features subset from the original real time signals and the Extreme Learning Machine (ELM) enables a good accuracy with a limiting learning time. Many benchmark datasets have used to evaluate the performances of our algorithm and the result indicates its higher efficiency and effectiveness comparing to other methods.

This paper develops an Internet-based fuzzy control system for an industrial process plant to ensure the remote and fuzzy control in cement factories in Algeria. The remote process consists of control, diagnosing alarms occurs, maintaining and synchronizing different regulation loops. Fuzzy control of the kiln ensures that the system be operational at all times, with minimal downtime. Internet technology ensures remote control. The system reduces downtimes and can guided by operators in the main control room or via Internet.

The aim of this work is to develop an Internet and fuzzy based control and data acquisition system for an industrial process plant which can ensure remote running and fuzzy control of a cement factory. Cases studies of the proposed system application in three cement factories in Algeria, SCAEK (Setif), SCIMAT (Batna), and SCT (Tebessa), are discussed. The remote process control consists of alarms generated during running of the processes while maintaining and synchronizing different regulation loops thus ensuring automatic running of processes smoothly. In addition, fuzzy control of the kiln and the other two mills ensures that the system is operational at all times with minimal downtime. The process control system contains different operator station (OP), alarms table and a provision to monitor trends analysis. The operator can execute any operation according to his authorised access assigned by the system administrator using user administration tool. The Internet technology is used for human security by avoiding all times presence of operators at site for maintenance. Further, in case of a breakdown, the problem would be remotely diagnosed and resolved avoiding requirement of an expert on site thus eliminating traveling cost, security risks, visa formalities, etc. These trips are difficult to organize (costs, visas, risks). So the enterprise can reduce downtimes and travel costs. In order to realize a process control system guided by operators in the main control room or through Internet, the process control is based on programming in PCS 7 utilizing Cemat library and Fuzzy Control++ Siemens tools.

In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework.

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.

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.

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.

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.

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.

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.