When the failure probability of a system is extremely small or necessary statistical data from the system is scarce, it is very difficult or impossible to evaluate its reliability and safety with conventional fault tree analysis (FTA) techniques. New techniques are needed to predict and diagnose such a system’s failures and evaluate its reliability and safety. In this paper, we first provide a ...

In the fault diagnosis of the motor, the vibration signals can fully reflect the status of the motor. In this paper, on the basis of wavelet packet fault feature extraction, a new approach for motor fault diagnosis based on wavelet packet analysis and fuzzy RBF neural network was presented.The method gains the energy of characteristic channel of bearing failure vibration signals of asynchronous...

This paper describes about the ability of a system to detect the fault and diagnose of the test board by using generic logic array (GAL). It has been very popular in defense sectors like military systems, aerospace and medical instruments as well as in security and safety applications. The methodology used in this is “repair on the go” which detects the fault IC on the board that is under the t...

This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by u...

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vecto...

Accurate and efficient fault identification is a necessary task for system reconfiguration, fault prognostics, and fault adaptive control in complex dynamic systems. However, timely on-line fault identification for large systems can be computationally expensive. In this paper, we show how we can decompose a system model into sub-models, diagnose each sub-model independently by specifying the mo...

In this study, a fault diagnostic system in a multi-level inverter using a MLP network is developed. Using a mathematical model, it is difficult to diagnose a Multilevel-Inverter Drive (MLID) system, because MLID system complexity has a non-linear factor and it consist of many switching devices. Therefore neural network classification is applied to fault diagnosis of MLID system. Multilayer per...

This paper proposes an approach to software faults diagnosis in complex fault tolerant systems, encompassing the phases of error detection, fault location, and system recovery. Errors are detected in the first phase, exploiting the operating system support. Faults are identified during the location phase, through a machine learning based approach. Then, the best recovery action is triggered onc...

In this paper, we describe our current work on developing tools for experimental evaluation of the efficiency of implemented countermeasures against differential fault attacks on cryptographic cores in the FPGA based systems. The developed fault injection platform enables us to analyze the impact of injected faults at the selected points of the FPGA in its run time operation. In its compact ver...

The cloud computing paradigm is being adopted by many organizations in different application domains as it is cost effective and offers a virtually unlimited pool of resources. Engineering critical systems can benefit from clouds in attaining all dependability means: fault tolerance, fault prevention, fault removal and fault forecasting. Our research aims to investigate the potential of support...