Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems

Robust detection of incipient faults in VSI-fed induction motors using quality control charts.

A considerable amount of papers have been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper a new method is proposed which is based on robust statistical techniques applied in Quality Control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked, broken bar, a faulty bearing and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults

ROBUST FAULT ANALYSIS FOR PERMANENT MAGNET DC MOTOR IN SAFETY CRITICAL APPLICATIONS

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.the Iraqi Ministry of Higher Education and Scientific Researc

Fault Diagnosis in Induction Motor Using Soft Computing Techniques

Induction motors are one of the commonly used electrical machines in industry because of various technical and economical reasons. These machines face various stresses during operating conditions. These stresses might lead to some modes of failures/faults. Hence condition monitoring becomes necessary in order to avoid catastrophic faults. Various fault monitoring techniques for induction motors can be broadly categorized as model based techniques, signal processing techniques, and soft computing techniques. In case of model based techniques, accurate models of the faulty machine are essentially required for achieving a good fault diagnosis. Sometimes it becomes difficult to obtain accurate models of the faulty machines and also to apply model based techniques. Soft computing techniques provide good analysis of a faulty system even if accurate models are unavailable. Besides giving improved performance these techniques are easy to extend and modify. These can be made adaptive by the incorporation of new data or information. Multilayer perceptron neural network using back propagation algorithm have been extensively applied earlier for the detection of an inter-turn short circuit fault in the stator winding of an induction motor. This thesis extends applying other neuro-computing paradigms such as recurrent neural network (RNN), radial basis function neural network (RBFNN), and adaptive neural fuzzy inference system (ANFIS) for the detection and location of an inter-turn short circuit fault in the stator winding of an induction motor. By using the neural networks, one can identify the particular phase of the induction motor where the inter-turn short circuit fault occurs. Subsequently, a discrete wavelet technique is exploited not only for the detection and location of an inter-turn short circuit fault but also to find out the quantification of degree of this fault in the stator winding of an induction motor. In this work, we have developed an experimental setup for the calculation of induction motor parameters under both healthy and inter-turn short circuit faulty conditions. These parameters are used to generate the phase shifts between the line currents and phase voltages under different load conditions. The detection and location of an inter-turn short circuit fault in the stator winding is based on the monitoring of these three phase shifts. Extensive simulation results are presented in this thesis to demonstrate the effectiveness of the proposed methods

Proceedings of the 8th International Conference EEMODS'2013 Energy Efficiency in Motor Driven Systems

This book contains the papers presented at the eighth international conference on Energy Efficiency in Motor Driven Systems EEMODS 2013 EEMODS 2013 was organised in Rio de Janeiro, Brasil from 28 to 30 October 2013. This major international conference, which was previously been staged in Lisbon (1996), London (1999), Treviso (2002), Heidelberg (2005), Beijing (2007), Nantes (2009) and Washington DC (2011) has been very successful in attracting an international and distinguished audience, representing a wide variety of stakeholders in policy implementation and development, manufacturing and promotion of energy-efficient motor systems, including key policy makers, equipment manufacturers, academia and end-users. Potential readers who may benefit from this book include researchers, engineers, policymakers, energy agencies, electric utilities, and all those who can influence the design, selection, application, and operation of electrical motor driven systems.JRC.F.7-Renewables and Energy Efficienc