A General Approach for Current-Based Condition Monitoring of Induction Motors

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
W. J. Bradley ◽  
M. K. Ebrahimi ◽  
M. Ehsani
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Industrial Condition ◽  
Detection Methods ◽  
Model Free ◽  
Motor Current Signature Analysis ◽  
Diagnosis Algorithm ◽  
Rotor Cage

The development and validation of a novel current-based induction motor (IM) condition monitoring (CM) system is described. The system utilizes only current and voltage signals and conducts fault detection using a combination of model-based and model-free (motor current signature analysis) fault detection methods. The residuals (or fault indicator values) generated by these methods are analyzed by a fuzzy logic diagnosis algorithm that provides a diagnosis with regard to the health of the induction motor. Specifically, this includes an indication of the health of the major induction motor subsystems, namely the stator windings, the rotor cage, the rolling element bearings, and the air-gap (eccentricity). The paper presents the overall system concept, the induction motor models, development of parameter estimation techniques, fault detection methods, and the fuzzy logic diagnosis algorithm and includes results from 110 different test cases involving four 7.5 kW four pole squirrel cage motors. The results show good performance for the four chosen faults and demonstrate the potential of the system to be used as an industrial condition monitoring tool.

scholarly journals Overview of fault detection methods for power plant control systems purposes

2019 ◽  
pp. 127-129
Author(s):  
Dmytro Shram ◽  
Oleksandr Stepanets
Keyword(s):  
Fault Detection ◽  
Control Systems ◽  
Power Plants ◽  
Fault Detection And Isolation ◽  
Data Driven ◽  
Detection Methods ◽  
Model Free ◽  
Power Plant Control ◽  
Technical Details ◽  
Plant Control

The main objective of this paper is to review of fault detection and isolation (FDI) methods and applications on various power plants. Due to the focus of the topic, on model and model-free FDI methods, technical details were kept in the references. We will overview the methods in terms of model-based, data driven and signal based methods further in the paper. Principles of three FDI methods are explained and characteristics of number of some popular techniques are described. It also summarizes data-driven methods and applications related to power generation plants. Parts of control system applications of FDI in TPPs with possible faults are shown in the Table I. Some popular techniques for the various faults in TPPs are discussed also.

Online Condition Monitoring of Traction Motor

2016 ◽  
pp. 489-523
Author(s):  
Anik Kumar Samanta ◽  
Arunava Naha ◽  
Devasish Basu ◽  
Aurobinda Routray ◽  
Alok Kanti Deb
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Traction Motor ◽  
Motor Current ◽  
Detection Technology ◽  
Current Voltage ◽  
Motor Current Signature Analysis ◽  
Complete Failure ◽  
Condition Monitoring System ◽  
The Cost

Squirrel Cage Induction Motors (SCIMs) are major workhorse of Indian Railways. Continuous online condition monitoring of the SCIMs like Traction Motor (TM) are essential to prevent unnecessary stoppage time in case of a complete failure. Before a complete failure, the TMs generally develop incipient or weak faults. Weak faults have minute influence on the motor performance but eventually leads to complete failure of the motor. If these weak faults are identified at the earliest then, a scheduled maintenance can be planned which will prevent any unplanned stoppage. The signals used for SCIM fault detection are motor current, voltage, vibration, temperature, voltage induced in search coil, etc. The most popular fault detection technology is based on Motor Current Signature Analysis (MCSA). MCSA based online and onboard TM condition monitoring system can be very useful for Indian railways to reduce the cost of operation and unplanned delay by shifting from unnecessary scheduled maintenance to condition-based maintenance of TM and other auxiliary SCIMs.

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.

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