A neuro-fuzzy approach to automatic diagnosis and location of stator inter-turn faults in CSI-fed PM brushless DC motors

Abstract

The paper presents a neuro-fuzzy-based perspective to the automation of diagnosis and location of stator-winding interturn short circuits in CSI-fed brushless dc motors. Performance of the drive under normal and short-circuit conditions are obtained through classical lumped-parameter network models. Waveforms of the electromagnetic torque and summation of phase voltages are monitored to develop two independent diagnostic algorithms. Diagnostic indices derived from the characteristic waveforms using discrete Fourier transform (DFT) lead to identifying the number of shorted turns. Fault location is achieved through a different set of indices extracted by the short-time Fourier transform (STFT). Adaptive neuro-fuzzy inference systems (ANFIS)are trained based on simulation results to automate the diagnostic process. ANFIS testing along with the good agreement between simulated and measured waveforms show the effectiveness of the proposed techniques.

Modeling and Analysis of PMSMs under Inter Turn Short Faults

“…Numerous previous works have utilized these unbalance information to detect the inter turn fault. The simple turn short fault detection scheme has been implemented by comparison negative sequence elements with prefixed threshold value [1][2][3][4][5][6]. Arkan [1] and Briz [2] utilized the negative sequence impedance and the negative sequence current information using the high frequency voltage injection.…”

“…Cash [3] utilized the line-neutral voltage measurement. The neural network is also applied to the fault detection scheme to reinforce the detection speed and the accuracy of the negative sequence voltage and current information [4,5]. Kim [6] utilized second order harmonic current in the synchronous rotating reference frame (SRF).…”

Design of Diagnosis Function for Autonomous PLC Control System

“…Then the point value is written in real-time database of the server, while the corresponding events are generated such as alarm, full record, etc. All combined faults are diagnosed by the node diagnosis client task [7].…”

Brushless Dc Motor Speed Control Using Proportional-Integral And Fuzzy Controller

“…In 2005, Mohamed A. Awadallah et al [21] presented two independent schemes for automatic fault diagnosis and location of inter-turn short circuits on the stator winding of CSI-fed PM brushless dc motors. In 2010, K. Wang, M. A. Rahman and J. X. Shen [19] presented a paper on pertinent design aspects related to enhancing the third harmonic back-EMF, mainly from the stator topology.…”