P. Vas

Bio: P. Vas is an academic researcher from University of Aberdeen. The author has contributed to research in topics: Artificial neural network & Fuzzy logic. The author has an hindex of 4, co-authored 4 publications receiving 1001 citations.

Recent developments of induction motor drives fault diagnosis using AI techniques

Abstract: This paper presents a review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). It covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques. The application of genetic algorithms is considered as well. In general, a diagnostic procedure starts from a fault tree developed on the basis of the physical behavior of the electrical system under consideration. In this phase, the knowledge of well-tested models able to simulate the electrical machine in different fault conditions is fundamental to obtain the patterns characterizing the faults. The fault tree navigation performed by an expert system inference engine leads to the choice of suitable diagnostic indexes, referred to a particular fault, and relevant to build an input data set for specific AI (NNs, fuzzy logic, or neuro-fuzzy) systems. The discussed methodologies, that play a general role in the diagnostic field, are applied to an induction machine, utilizing as input signals the instantaneous voltages and currents. In addition, the supply converter is also considered to incorporate in the diagnostic procedure the most typical failures of power electronic components. A brief description of the various AI techniques is also given; this highlights the advantages and the limitations of using AI techniques. Some applications examples are also discussed and areas for future research are also indicated.

AI techniques in induction machines diagnosis including the speed ripple effect

Abstract: Various applications of artificial intelligence (AI) techniques (expert systems, neural networks, and fuzzy logic) presented in the literature prove that such technologies are well suited to cope with on-line diagnostic tasks for induction machines. The features of these techniques and the improvements that they introduce in the diagnostic process are recalled, showing that, in order to obtain an indication on the fault extent, faulty machine models are still essential. Moreover, by the models, that must trade off between simulation result effectiveness and simplicity, it is possible to overcome crucial points of the diagnosis. With reference to rotor electrical faults of induction machines, a new and simple procedure based on a model which includes the speed ripple effect is developed. This procedure leads to a new diagnostic index, independent of the machine operating condition and inertia value, that allows the implementation of the diagnostic system with a minimum configuration intelligence.

Broken bar detection in induction machines: comparison between current spectrum approach and parameter estimation approach

Abstract: In this paper the diagnosis of induction machine rotor electrical faults is considered. Two approaches are compared: the current spectrum analysis and the apparent rotor resistance estimation. For the first approach the authors have developed several procedures based on different fault models of the machine. Their experience is used to approach the parameter estimation method from a theoretical point of view: the resistance variation of the balanced per-phase model is computed using the faulted machine model. It is possible to obtain, by the simulation, the expected resistance variation when some bars break in a specific machine. Moreover the numerical results can be generalized. Using a simplified model of a faulted machine a relationship is obtained, which correlates the apparent resistance variation with the number of broken bars. This relationship needs several assumptions and therefore it is an approximate one, but can be used to define the threshold level for the apparent resistance variation expected in the case of one broken bar. By this relationship it is possible to have an indication on the sensitivity of the parameter estimation approach. The superiority of the current spectrum approach over the parameter estimation approach is shown. >

Position sensorless control of a SRM drive using ANN-techniques

Abstract: Nowadays speed-sensorless electromechanical drives are feasible for many applications. The elimination of the mechanical transducer increases reliability, reducing costs. As far as synchronous machine drives are concerned, this issue is critical since the rotor position is fundamental to derive the proper switching sequence. If an SRM is used as an actuator different sensorless techniques have been proposed, essentially based on the rotor position detection by reluctance variation. Since the SRM is a highly nonlinear machine, it is an ideal candidate for the application of artificial neural networks (ANNs). In this paper, the results of a mixed closed loop scheme for the sensorless identification of an SRM drive are presented. A radial basis function ANN has been used, since it is well suited to simulate a nonlinear system over a wide operating area. Then the proposed ANN is applied within a standard control scheme as a position sensor. Simulation results show the effectiveness of the proposed method, since good agreement is reached with experimental results obtained from an SRM drive with a standard position sensor.

Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review

Abstract: Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.

A review of induction motors signature analysis as a medium for faults detection

Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing.

Advances in Diagnostic Techniques for Induction Machines

Abstract: This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.

A review of induction motors signature analysis as a medium for faults detection

Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis (MCSA) which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state of the art" terminology for the benefit of practicing engineers in facilities today who may not be familiar with signal processing.