scholarly journals Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 101 ◽  
Author(s):  
Jing Tang ◽  
Yongheng Yang ◽  
Jie Chen ◽  
Ruichang Qiu ◽  
Zhigang Liu
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Experimental Tests ◽  
Electromagnetic Torque ◽  
Current Harmonics ◽  
Flux Observer ◽  
Rotor Faults ◽  
Space Harmonics ◽  
Characteristics Analysis ◽  
Motor Current Signature Analysis

Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.

Fault Diagnosis of Induction Motors Using Motor Current Signature Analysis

2019 ◽  
pp. 1-37
Author(s):  
Muthiah Geethanjali ◽  
Hemavathi Ramadoss
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Future Research ◽  
Signature Analysis ◽  
Damage Spreading ◽  
Motor Current ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signature

Induction motors are termed as horses of modern industry because they are playing a vital role in industries. They are simple, efficient, robust, rugged, and highly reliable. The feasibility of mishap in induction motors is less, but they are prone to faults, which are left unobserved most of the time. Hence, more attention has been paid to detection and diagnosis of incipient faults to prevent damage spreading and increase the lifetime of the motor. To detect and diagnose the faults, online condition monitoring of the machine has been utilized in a wide manner. At present, focus is made on optimization procedures for fault diagnosis in induction motors to obtain a quick assessment at industry level. This chapter discloses an overview of various types of possible faults in induction motors. In addition, the conventional (invasive) and innovative techniques (noninvasive), especially motor current signature analysis (MCSA), techniques for fault detection and diagnosis in induction machines are covered with a focus on future research.

scholarly journals Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

2020 ◽  
Vol 11 (1) ◽  
pp. 314
Author(s):  
Gustavo Henrique Bazan ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Wagner Fontes Godoy ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Operating Conditions ◽  
Industrial Systems ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Three Phase ◽  
C4.5 Decision Tree ◽  
The Time Domain

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

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