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Title: Condition Monitoring and Fault Diagnosis of Induction Machine Using Artificial Intelligence Methods and Empirical Mode Decomposition
Keywords: Machine fault diagnosis, Self-organizing map, k-nearest neighbors, multi-class support vector machine, bearing fault, broken rotor bar fault
Issue Date: 6-Jun-2011
Citation: CHEN WEE YUAN (2011-06-06). Condition Monitoring and Fault Diagnosis of Induction Machine Using Artificial Intelligence Methods and Empirical Mode Decomposition. ScholarBank@NUS Repository.
Abstract: The field of machine condition monitoring and fault diagnosis is vast. A literature survey; which is presented subsequently, has shown wide ranging diagnostic techniques. Various machine operation quantities may be used for monitoring the health of a motor, e.g., partial discharge, thermo-graphic monitoring of hot-spots, chemical content; such as, oil degradation detection, wear debris detection, machine axial leakage flux, acoustic, torque, machine power efficiency, machine vibration signal, and motor current signature. Among these, the technique by analyzing machine stator current is known as Motor Current Signature Analysis (MCSA) is the state-of-the-art technique. It is a popular research area where many algorithms have been proposed, but a single effective method that is able to detect and diagnosis multiple classes of machine fault still elude researchers. The current harmonics that is present in the motor current is mainly created by the machine asymmetries and vibrations due to machine faults. Hence, this project focuses on two fault detection techniques, namely, vibration signature and MCSA. There are a number of issues to address in the formulation of a reliable fault detection and diagnosis scheme: 1. definition of a single diagnostic procedure for any type of faults 2. insensitive to and independent of operating conditions 3. reliable fault detection for position, speed and torque controlled drives 4. reliable fault detection for drives in time-varying conditions 5. quantify a stated fault threshold independent of operating conditions With the above issues in mind, this project aims to accomplish two main objectives, namely, Objective 1: To investigate and formulate an automatic machine condition monitoring scheme to detect and diagnose the most common machine fault modes, namely, bearing and unbalanced rotor fault, that is insensitive to machine operating speed Objective 2: To investigate and study the use of MCSA to cover a wider range of machine fault modes; apart from bearing and unbalanced rotor faults, to include broken rotor bars and shorted winding faults as well, where vibration analysis is difficult to diagnose, and to discover unique nonlinear and non-stationary features for automatic fault classifications In these studies, computational intelligence are applied. Of particular interests, are the Self-Organizing Map (SOM), multi-class Support Vector Machine (M-SVM), k-Nearest Neighbor (k-NN) case-based learning and the Empirical Mode Decomposition (EMD).
Appears in Collections:Master's Theses (Open)

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