Kondo H. Adjallah

PWM Energy Converters: Fractal Method of Dynamics Forecasting in Practical Application

A fractal method of dynamics forecasting is investigated from the viewpoint of practical application regarding pulsewidth modulation (PWM) energy converters (PECs). The method realizes the idea of PEC state estimation relative to the preliminary formed domains of periodic processes. With this purpose, a special space ldquois designedrdquo into which the periodic process domains are mapped from the parametrical space and transient phase trajectories-from the phase space. Mapping is provided with use of geometrical interpretation of fractal regularities that exist in the structures of both periodic process and transient phase trajectories. Computational and experimental investigations of the fractal method efficiency are carried out within the bounds of the scenario of dynamics evolution toward chaos through period doubling.

Kernel estimator of maintenance optimization model for a stochastically degrading system under different operating environments

This paper investigates the preventive age replacement policy (ARP) for a system subject to random failures. Unlike most maintenance models in the literature, our model considers a system that is exploited under different operating environments each characterized by its own degree of severity. The system lifetimes follow a different distribution depending on the environment it is operating under. Furthermore, the system lifetimes distribution is assumed unknown and therefore estimated from field reliability data. The reliability of the system is calculated using two kernel estimators. This method offers the advantage of non-parametric estimation methods and completely determined by two parameters, namely the smoothing parameter and the kernel function. First, a probability maintenance cost model is derived and conditions under which an optimal preventive maintenance age exists are provided. Then, a statistical maintenance cost model is developed using two kernel estimators. The impact of the variability of the kernel smoothing parameter on the cost model is also investigated. Numerical experiments are provided to illustrate the proposed approach. Results obtained demonstrate the accuracy of the proposed statistical maintenance cost model.

Fault detection in nonlinear systems

This paper deals with the problem of fault detection and localization for a wide class of nonlinear systems subjected to bounded nonlinearities. A dedicated nonlinear observer scheme for fault detection and identification of observable systems is proposed.<<ETX>>

Genetic algorithm based scheduling method for lifespan extension of a wireless sensors network

The problem of Keeping a wireless sensor network a life for maximum possible monitoring time, considering its limited energy source is widely addressed. Linear programming, genetic algorithms and other optimization methods are used on disjoint set covers to maximize the lifetime. This paper provided a genetic algorithm-based secluding method to generate the solution with the maximum possible lifetime. A simple way of chromosome creation is used for problem encoding and several crossover and mutation strategies are investigated. The extended lifetime is obtained from different operation situations, considering a number of sensors with different initial energy and different energy consumption rate. The simulation based on the proposed method is coded using a C programming language.

Non disjoint set covers approach for wireless sensor networks lifetime optimization

The lifetime optimization problem of wireless sensor networks is widely solved using disjoint sets covers in which a sensor cannot participate in more than one cover. The proposed method gives an opportunity for a sensor to join more than one cover. The genetic algorithm is used to find the maximum number of non-disjoint sets covers to be scheduled to optimize wireless sensor networks lifetime. The investigations show that the algorithm complexity increases when the problem is reasonably divided into two sub-problems. The obtained results encourage to search for better solutions using this approach rather than that based on disjoint set covers.

NDSC based methods for maximizing the lifespan of randomly deployed wireless sensor networks for infrastructures monitoring

Abstract This paper addresses the problem of wireless sensor network (WSN) lifetime maximization, under limited available energy constraint. The investigations have shown that the valid amount of energy was not used by the existing disjoint set covers (DSC) based scheduling method for WSNs lifetime maximization, because of the DSC constraints. Instead, we suggest in this paper to schedule non-disjoint sets covers (NDSC) for maximizing WSNs lifetime. Thus, we have formulated this problem, using the integer linear programming (ILP) mathematical model, then we developed an approach based on genetic algorithm GA to find the maximal lifespan. As main contributions, we investigated and designed a new method using the NDSC instead of the DSC. This approach removes the latter’s constraint and gives the opportunity to a sensor to participate in more than one cover, and thereby improves significantly the WSNs’ lifetime. We proposed an exact method and a genetic algorithms (GA) for the NDSC efficient scheduling for the WSN lifetime maximization. The exact method lies on the integer linear programming (ILP). For the GA based heuristics, we used a specific arrangement of chromosomes combining several crossover and mutation strategies for encoding the solutions. We provided experimental results for different instances involving sensors with non-identical amount of initial energy and power consumption. In addition, we provided comparative analysis results between the solutions obtained by our both methods and the existing methods based on DSC. The comparisons of the run times and the solutions’ quality revealed the dominance of the solutions yielded by our methods based on the NDSC compared to those based on the DSC.

Optimizing the reliability, maintainability and safety data collection process through lifecycle

This paper focuses on an approach to develop a robust, reliable, maintainable and proactive process of data collection through relevant indicators. Mainly, it aims to model and optimize the effectiveness of the RAMS data collection process (DCP), under cost constraints, while taking into account the its operating conditions and human factors. It also deals with the data collection system maintenance, data collection process and database maintenance over time. The authors have proposed an approach to model and optimize the RAMS DCP based on data relevance assessment, indicators of complexity, the usefulness and the quantity of information collected using the DCP and database.

Characterisation, monitoring and failure risks assesment of buried ductile steel pipeline subject to earthquakes by using wireless sensors

This paper addresses the problem of damage monitoring system for buried ductile steel pipeline subject to earthquakes. To determine the capacity of a pipeline to perform its mechanical functions face to occasional or incidental damages, such as earthquakes, it is needed to evaluate the structural integrity of pipe and to track relevant environment parameters. This may be performed through intelligent monitoring system of earthquakes environment. For risk prevention decision support, it is necessary to collect and analyze data to predict the failures and damages, and to provide data. First, we propose a model for the steel pipes structural behavior under earthquake strengths. Simulation result, allowed identifying key parameters to be monitored and to suggest sensors based instrumentation and placement for data collection and risk assessment. Then we suggest an intelligent data acquisition system with a strategy following 3 phases of earthquake impact of buried pipes: damage accumulation, crack initiation and crack propagation. The analysis results of the current study suggest that an intelligent data acquisition system requires intelligent sensors based instrumentation with an appropriate technology for buried ductile steel pipeline subjected to earthquakes. The resulting intelligent instrumentation system requires solving an optimal positioning problem of networked smart wireless sensors, under uncertainties constraints, with self-adaptive and remote monitoring operational capacities.

Resiliency assessment of NDSC based lifetime maximization approach for heterogeneous wireless sensor network by Monte Carlo simulation

For the WSNs lifetime optimization, the non-disjoint set covers (NDSC) based coverage control approach has brought out a better performance. In addition, it yields a promising indicators in term of reliability, resilience and the possibility to be used for heterogeneous WSNs. This paper addresses the WSNs resiliency assessment via NDSC approaches to WSNs lifetime optimization. We investigated the ability to provide and maintain the required level of coverage when facing various failures. It uses mathematical model for the WSNs lifetime, the Genetic Algorithm (GA) used for NDSC finding and the GA used for WSNs lifetime optimization to search the best reconfiguration in case of a sensor failure. Then, it uses the Monte Carlo dynamic simulation method to assess the WSN resilience based on NDSC. For a WSN with m sensors included in q NDSC scheduled for a given number of sensing periods, our method could estimate the WSN resilience and reconfigurability that enables to prolong the network lifetime considering the failure hazards on a given operating time corresponding to the number of monitoring seasons.

Monte Carlo based Petri net simulation for maintenance strategies assessment in series-parallel-series multi-physic systems

The authors propose a methodology to assess the effectiveness of a maintenance strategy on the availability of a serial-parallel multi-physic system, using Monte Carlo simulation embedded in a Petri net model. The systems are composed of heterogenous components that are characterized by specific degradations and failure mechanisms. Building an effective maintenance strategy to improve the availability of such a system requires to monitoring the degradation of each component. We assume that each component is subject to stochastic degradations. Also, we consider that each component might have three health status, according to degradation thresholds, function of the component reliability: “healthy”, “degraded” and “failed”. The health condition of the overall system relies on the health status of each component. A model for tracking the status of each component has been worked out using a colored stochastic Petri net (CSPN). Each health status is modeled by a place within the CSPN model, where each component is modeled by a colored token. The degradation of each component of the system is evaluated based on the Monte Carlo simulation technique. Transition firing regarding a given color model the evolution of the associated component from a health condition to another due to the degradation mechanism or to a maintenance action aimed to restore partially or totally its performance. However, the degradation of each component does not have the same influence on the performance of the overall system. Operational performance indicators are introduced to quantify the influence of each component on the performance of the entire system. Furthermore, maintenance actions are defined taking into account the degradation level of each component, the influence that each component has on the performance of the system and the available repairman. The effectiveness of the maintenance strategy on the system availability is evaluated through simulation.