Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction

2018 ◽  
Vol 414 ◽  
pp. 43-60 ◽  
Author(s):  
Huan Huang ◽  
Natalie Baddour ◽  
Ming Liang
Keyword(s):  
Fault Diagnosis ◽  
Rotational Speed ◽  
Multiple Time ◽  
Time Varying ◽  
Frequency Curve ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

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.

A performance enhanced time-varying morphological filtering method for bearing fault diagnosis

Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109163
Author(s):  
Bingyan Chen ◽  
Dongli Song ◽  
Weihua Zhang ◽  
Yao Cheng ◽  
Zhiwei Wang
Keyword(s):  
Fault Diagnosis ◽  
Time Varying ◽  
Filtering Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Morphological Filtering ◽  
A Performance

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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