Induction motor fault detection and diagnosis by vibration analysis using MEMS accelerometer

2013 ◽  
Author(s):  
Vineetha P. Raj ◽  
K. Natarajan ◽  
Sri. T.G. Girikumar
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Fault Detection And Diagnosis ◽  
Mems Accelerometer ◽  
Detection And Diagnosis

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

2012 ◽  
Vol 3 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Manjeevan Seera ◽  
Chee Peng Lim ◽  
Dahaman Ishak
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Broken Rotor Bars ◽  
Power Spectral ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signatures

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

Induction Motor Fault Detection and Diagnosis Using Artifical Neural Networks

2005 ◽  
Author(s):  
Chris K. Mechefske ◽  
Lingxin Li
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Induction Motor ◽  
Mechanical Vibration ◽  
Fault Detection And Diagnosis ◽  
Self Organizing Maps ◽  
Artifical Neural Networks ◽  
On Line ◽  
Motor Condition ◽  
Detection And Diagnosis

This paper investigates induction motor fault detection and diagnosis using Artificial Neural Networks (ANN). The ANN techniques include feedforward backpropagation networks (FFBPN) and self organizing maps (SOM), used individually and in combination. Common induction motor faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using dynamic measurements of stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs are discussed. This study shows that, while the feedforward ANNs give satisfactory results and the SOMs can classify the type of motor fault during steady state working conditions, using a combination of SOM and FFBPN techniques yields superior fault detection and diagnostic accuracy. In addition, incipient motor fault detection has been investigated. The above results show that improved induction motor maintenance strategies may be possible through the use of comprehensive on-line induction motor condition monitoring and fault diagnosis systems.

scholarly journals Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique

2021 ◽  
Vol 9 (1) ◽  
pp. 45-59
Author(s):  
Annamalai Balamurugan ◽  
Thangavel Swaminathan Sivakumaran
Keyword(s):  
Standard Deviation ◽  
Fault Detection ◽  
Induction Motor ◽  
Fault Detection And Diagnosis ◽  
Gradient Boosting ◽  
Discrete Wavelet ◽  
Data Set ◽  
Statistical Measures ◽  
Detection And Diagnosis ◽  
Multi Phase

In this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM). The proposed method is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called the GBDTI2HO method. Here, additional operators are included in this paper to improve HHO’s search behaviour namely crossover and mutation. Distorted waveforms are generated by different frequency patterns to indicate the time domain frequency as an assessment of failure. For this signal representation, the discrete wavelet transformation (DWT) is suggested. It extracts the characteristics and forwards them to IHHO technique to form the possible data sets. After the generation of the data set, GBDT classifies the ways of failure reached as winding of stator in multi-phase IM. The implementation of the proposed system is compared with existing systems, such as ANN, S-Transform and GBDT. The proposed method is executed on MATLAB/Simulink work platform to demonstrate the successfulness of proposed system, statistical measures are determined, as precision, sensitivity and specificity, mean median and standard deviation. For demonstrating the successfulness of proposed system, statistical measures are determined as precision, sensitivity, specificity, mean median as well as standard deviation. In 50 trails the proposed method, 0.98 for accuracy, 0.96 for specificity, 1.60 for recall as well as 0.97 for precision. In 100 trail the proposed method, 0.96 for accuracy, 0.93 for specificity, 0.87 for recall as well as 0.99 for precision.

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