IoT-Based Condition Monitoring and Fault Detection for Induction Motor

2018 ◽  
pp. 205-215 ◽  
Author(s):  
R. Kannan ◽  
S. Solai Manohar ◽  
M. Senthil Kumaran
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Condition Monitoring

Condition Monitoring of DSP Based Online Induction Motor External Fault Detection Using TMS320LF2407 DSP

2011 ◽  
Author(s):  
K. Vinoth Kumar ◽  
S. Suresh Kumar ◽  
R. Saravanakumar ◽  
A. Immanuel Selvakumar ◽  
Kishore Reddy ◽  
...  
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Condition Monitoring

scholarly journals Fault Detection (Condition Monitoring) of Induction Motor based on Wavelet Transform

2013 ◽  
pp. 268-271
Author(s):  
U. E. Hiwase ◽  
S. B. Warkad
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Fault Detection ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Discrete Wavelet ◽  
Steady State Analysis ◽  
Motor Current ◽  
Current Signatures ◽  
Vector Approach

Presently, many condition monitoring techniques that are based on steady-state analysis are being applied to Induction motor. However, the operation of induction motor is predominantly transient, therefore prompting the development of non-stationary techniques for fault detection. In this paper we apply steady-state techniques e.g. Motor Current Signatures Analysis (MCSA) and the Extended Park’s Vector Approach (EPVA), as well as a new transient technique that is a combination of the EPVA, the Discrete Wavelet Transform and statistics, to the detection of turn faults in a induction motor. It will be shown that steady-state techniques are not effective when applied to induction motor operating under transient.

A brief review of condition monitoring techniques for the induction motor

2019 ◽  
Vol 43 (4) ◽  
pp. 499-508
Author(s):  
Dileep Kumar Soother ◽  
Jawaid Daudpoto
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Condition Monitoring ◽  
State Of The Art ◽  
Low Cost ◽  
Cost Effective ◽  
Simple Construction ◽  
Monitoring Techniques ◽  
Effective Functioning ◽  
Production Processes

The induction motor is widely used in industry owing to its simple construction and low cost. In this paper, we present a state-of-the-art review of condition monitoring techniques for the induction motor. As the induction motor is used in many production processes, its uninterrupted and cost-effective functioning is of prime importance. Condition monitoring of the induction motor enables detection and prediction of different developing faults. Various techniques have been reported in literature for induction motor fault detection. In this paper, a brief review of these techniques is presented. Major induction motor faults and their sources are also described.

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.

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