scholarly journals The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

2017 ◽  
Vol 32 (1) ◽  
pp. 244-256 ◽  
Author(s):  
Angel Sapena-Bano ◽  
Jordi Burriel-Valencia ◽  
Manuel Pineda-Sanchez ◽  
Ruben Puche-Panadero ◽  
Martin Riera-Guasp
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Time Varying ◽  
Harmonic Order ◽  
Analysis Method ◽  
Order Tracking

Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines

2015 ◽  
Vol 30 (3) ◽  
pp. 833-841 ◽  
Author(s):  
Angel Sapena-Bano ◽  
Manuel Pineda-Sanchez ◽  
Ruben Puche-Panadero ◽  
Juan Perez-Cruz ◽  
Jose Roger-Folch ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Machines ◽  
Harmonic Order ◽  
Order Tracking ◽  
Novel Method

Fault Signature Analysis Based on Transient Current for Induction Motors

2010 ◽  
Vol 44-47 ◽  
pp. 1807-1811
Author(s):  
Feng Lv ◽  
Hao Sun ◽  
Wen Xia Du ◽  
Shue Li
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Scale Transformation ◽  
Stationary Condition ◽  
Analysis Method ◽  
Broken Rotor Bars ◽  
Multi Scale ◽  
Stator Current ◽  
Time Varying Frequency ◽  
Basic Characteristics

The characteristics of broken rotor bars in induction motors are reflected in the abnormal harmonic of the stator current. At present, fast Fourier transform( ) and time-varying frequency spectrum analysis method are used in such fault diagnosis, but non-stationary motors operation can bring a certain difficulties to the monitoring and diagnosis. This paper studies the basic characteristics of wavelet transform, adopting the wavelet analysis technologies of signal processing and selecting mother wavelet, the paper makes the multi-scale transformation to the motor starting current, excavates the harmonic informations on non-stationary condition, realizes fault diagnosis of motor broken rotor bars effectively, The consistent diagnostic results prove the effectiveness of the method.

scholarly journals A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhu Huang ◽  
Tao Wang ◽  
Wei Liu ◽  
Luis Valencia-Cabrera ◽  
Mario J. Pérez-Jiménez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Fault Analysis ◽  
Fault Prediction ◽  
P Systems ◽  
Analysis Method ◽  
Spiking Neural P Systems ◽  
Three Phase ◽  
Fuzzy Production Rules ◽  
Reasoning Algorithm

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.

Algorithm for Multiple Time-Frequency Curve Extraction From Time-Frequency Representation of Vibration Signals for Bearing Fault Diagnosis Under Time-Varying Speed Conditions

2017 ◽  
Author(s):  
Huan Huang ◽  
Natalie Baddour ◽  
Ming Liang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Time Varying ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Order Tracking ◽  
Frequency Representation ◽  
Bearing Vibration ◽  
Fault Characteristic

Bearing fault diagnosis under constant operational condition has been widely investigated. Monitoring the bearing vibration signal in the frequency domain is an effective approach to diagnose a bearing fault since each fault type has a specific Fault Characteristic Frequency (FCF). However, in real applications, bearings are often running under time-varying speed conditions which makes the signal non-stationary and the FCF time-varying. Order tracking is a commonly used method to resample the non-stationary signal to a stationary signal. However, the accuracy of order tracking is affected by many factors such as the precision of the measured shaft rotating speed and the interpolation methods used. Therefore, resampling-free methods are of interest for bearing fault diagnosis under time-varying speed conditions. With the development of Time-Frequency Representation (TFR) techniques, such as the Short-Time Fourier Transform (STFT) and wavelet transform, bearing fault characteristics can be shown in the time-frequency domain. However, for bearing fault diagnosis, instantaneous time-frequency characteristics, i.e. Time-Frequency (T-F) curves, have to be extracted from the TFR. In this paper, an algorithm for multiple T-F curve extraction is proposed based on a path-optimization approach to extract T-F curves from the TFR of the bearing vibration signal. The bearing fault can be diagnosed by matching the curves to the Instantaneous Fault Characteristic Frequency (IFCF) and its harmonics. The effectiveness of the proposed algorithm is validated by experimental data collected from a faulty bearing with an outer race fault and a faulty bearing with an inner race fault, respectively.

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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