Sensitivity Assessment of Wound Rotor Induction Generator Bearing Fault Detection Using Machine Electrical Quantities

Author(s):  
Damian S. Vilchis-Rodriguez ◽  
Sinisa Djurović ◽  
Alexander C. Smith

This paper investigates the sensitivity of machine electrical quantities when employed as a means of bearing fault detection in wound rotor induction generators. Bearing failure is the most common failure mode in rotating AC machinery. With the widespread use of wound rotor induction machines in modern wind power generation, achieving effective detection of bearing faults in these machines is becoming increasingly important in order to minimize wind turbine maintenance related downtime. Current signature analysis has been demonstrated to be an effective technique for achieving detection of different fault types in ac machines. However, this technique lacks sensitivity when used for detection of bearing failures and therefore sophisticated post processing techniques have recently been suggested to improve its performance. As an alternative, this paper investigates the sensitivity of a range of machine electrical quantities to bearing faults, with the aim of examining the possibility of achieving improved bearing fault detection based on identifying a clear fault spectral signature. The reported signatures can be subjected potentially to refined processing techniques to further improve fault detection.

2018 ◽  
Vol 8 (8) ◽  
pp. 1392 ◽  
Author(s):  
Moussa Hamadache ◽  
Dongik Lee ◽  
Emiliano Mucchi ◽  
Giorgio Dalpiaz

This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.


2019 ◽  
Vol 24 ◽  
pp. 01004
Author(s):  
Siwanu Lawbootsa ◽  
Prathan Chommaungpuck ◽  
Jiraphon Srisertpol

Nowadays, Factors of a competition of Hard Disk Drive (HDD) industry have reduced the cost of manufacturing process via increasing the rate of productivity and reliability of the automation machine. This paper aims to increase the efficacy of Condition-Based Maintenance (CBM) of linear bearing in Auto Core Adhesion Mounting machine (ACAM). The linear bearing faults considered in three causes such as healthy bearing, one ball bearing damage and one ball bearing damage with starved lubricant. The Fast Fourier Transform spectrum (FFT spectrum) can be detected for linear bearing faults and Artificial Neural Network (ANN) method used to analyze the cause of linear bearing faults in operational condition. The experimental results show the potential application of ANN and FFT spectrum technique as Fault Detection and Isolation (FDI) tool for linear bearing fault detection performance. The accuracy and decision making of ANN is enough to develop the diagnostic method for automation machine in operational condition.


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