Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review

2020 ◽  
Vol 144 ◽  
pp. 106908 ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Induction Motors ◽  
State Of The Art ◽  
Fault Detection And Diagnosis ◽  
Monitoring Techniques ◽  
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.

Development on Fault Detection and Diagnosis of Unmanned Aerial Vehicles

2014 ◽  
Vol 494-495 ◽  
pp. 861-864
Author(s):  
Yi Peng Zhang ◽  
Ke Cai Cao
Keyword(s):  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
State Of The Art ◽  
Fault Detection And Diagnosis ◽  
The Past ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Detection And Diagnosis

The reliability of unmanned aerial vehicles (UAVs) has caught the attention of many researchers in the past decades. This paper presents a review on the development and important issues of state-of-the-art researches in the field of fault detection and diagnosis (FDD) techniques. Faults on an individual unmanned aerial vehicle or a group of unmanned aerial vehicles are considered for providing an overall picture of fault detection and diagnosis approaches.

Automated Fault Detection and Diagnostics for Vapor Compression Cooling Equipment

2003 ◽  
Vol 125 (3) ◽  
pp. 266-274 ◽  
Author(s):  
James E. Braun
Keyword(s):  
Fault Detection ◽  
Case Studies ◽  
State Of The Art ◽  
General Structure ◽  
Fault Detection And Diagnosis ◽  
Vapor Compression ◽  
Widespread Application ◽  
Air Conditioners ◽  
Detection And Diagnosis ◽  
Vapor Compression Cooling

This paper provides an overview of research related to automated fault detection and diagnosis for chillers, packaged air conditioners, and other vapor compression cooling equipment. The paper discusses the benefits, constraints, and possible products for FDD applied in the HVAC&R industry, presents results of fault surveys for packaged air conditioners and chillers, outlines the general structure and elements of an FDD system for HVAC&R equipment, describes specific methods associated with different FDD elements, and presents results from some specific case studies. The paper also attempts to provide an assessment of the state-of-the-art in FDD for vapor compression equipment and to identify the steps necessary to achieve widespread application.

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