Adaptive tacholess order tracking method based on generalized linear chirplet transform and its application for bearing fault diagnosis

2021 ◽  
Author(s):  
Rongkai Duan ◽  
Yuhe Liao ◽  
Lei Yang
Keyword(s):  
Fault Diagnosis ◽  
Tracking Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Order Tracking ◽  
Chirplet Transform

Intelligent Bearing Fault Diagnosis Based on Tacholess Order Tracking for a Variable-Speed AC Electric Machine

2019 ◽  
Vol 19 (5) ◽  
pp. 1850-1861 ◽  
Author(s):  
Jiahao Niu ◽  
Siliang Lu ◽  
Yongbin Liu ◽  
Jiwen Zhao ◽  
Qunjing Wang
Keyword(s):  
Fault Diagnosis ◽  
Electric Machine ◽  
Variable Speed ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Order Tracking

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.

Match-Extracting Chirplet Transform With Application to Bearing Fault Diagnosis

2021 ◽  
Vol 70 ◽  
pp. 1-10 ◽  
Author(s):  
Quansheng Xu ◽  
Jingbo Liu ◽  
Yang Guan ◽  
Dengyun Sun ◽  
Zong Meng
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Chirplet Transform
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