Ensemble decision approach with dislocated time–frequency representation and pre-trained CNN for fault diagnosis of railway vehicle gearboxes under variable conditions

2021 ◽  
pp. 1-19
Author(s):  
Jinhai Wang ◽  
Jianwei Yang ◽  
Yuzhu Wang ◽  
Yongliang Bai ◽  
Tieling Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Railway Vehicle ◽  
Time Frequency ◽  
Frequency Representation

A velocity synchrosqueezing transform for fault diagnosis of planetary gearboxes under nonstationary conditions

2016 ◽  
Vol 231 (15) ◽  
pp. 2868-2884 ◽  
Author(s):  
Yunpeng Guan ◽  
Ming Liang ◽  
Dan-Sorin Necsulescu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Machinery Condition Monitoring ◽  
Frequency Representation ◽  
Nonstationary Vibration ◽  
Synchrosqueezing Transform ◽  
Fine Resolution ◽  
Nonstationary Conditions

Time–frequency analysis is widely used in the field of machinery condition monitoring and fault diagnosis under nonstationary conditions. Among the time–frequency methods synchrosqueezing transform outperforms others in providing fine-resolution time–frequency representation. However, it suffers from time–frequency smear when analysing nonstationary signals. To address this issue, this paper proposes a new synchrosqueezing-transform-based method which works by (1) mapping the raw nonstationary vibration signal into a corresponding stationary angle domain signal to meet the stationarity requirement of the synchrosqueezing transform, (2) performing the synchrosqueezing transform of the corresponding signal and (3) restoring the time–frequency representation of the raw signal from the synchrosqueezing transform result of the corresponding signal. As the synchrosqueezing transform is applied to the stationary corresponding signal, the time–frequency smear is eliminated in the synchrosqueezing transform result of the corresponding signal and the final signal time–frequency representation. As such the proposed method can generate a smear-free time–frequency representation with fine time–frequency resolution and thus provide more reliable diagnosis decisions. A fast implementation algorithm is also developed to simplify the implementation of the proposed method. The effectiveness of the proposed method is validated using both simulated and experimental vibration signals of planetary gearboxes.

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 Bearing Fault Diagnosis Based on Optimal Time-Frequency Representation Method

2019 ◽  
Vol 52 (11) ◽  
pp. 194-199
Author(s):  
Israel Ruiz Quinde ◽  
Jorge Chuya Sumba ◽  
Luis Escajeda Ochoa ◽  
Antonio Jr. Vallejo Guevara ◽  
Ruben Morales-Menendez
Keyword(s):  
Fault Diagnosis ◽  
Optimal Time ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Frequency Representation ◽  
Representation Method

Vibration Analysis of Multi Stages Rotor for Blade Faults Diagnosis

2013 ◽  
Vol 845 ◽  
pp. 133-137 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
M. Salman Leong ◽  
Lim Meng Hee ◽  
Kar Hoou Hui
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Vibration Analysis ◽  
Vibration Spectrum ◽  
Rotor System ◽  
Analysis Tool ◽  
Time Frequency ◽  
Multi Stage ◽  
Frequency Representation ◽  
Faults Diagnosis

Blade fault is one of the most common faults in turbomachinery. In this article, a rotor system consists of multiple rows of blade was developed. The effectiveness of conventional FFT spectrum and wavelet analysis in the diagnosis of multi stage blade rubbing faults is examined at different stages, variety of blade fault conditions, and different blades rubbing severity. Blade fault caused impacts and the use of wavelets as analysis tool to detect the blade faults was studied. Results showed that, vibration spectrum can clearly depict the location and the stage of blade rubbing, while it is difficult to be identified in wavelet analysis. The limitations of wavelet analysis for multi stage blade fault diagnosis were identified. Some probable solutions to improve wavelet time-frequency representation in blade fault diagnosis were also presented.

scholarly journals Fault diagnosis of gearbox based on improved polynomial adaptive chirp mode decomposition algorithm

2021 ◽  
Author(s):  
Lingli Cui ◽  
Yuchuan Peng ◽  
Tongtong LIU
Keyword(s):  
Fault Diagnosis ◽  
Instantaneous Frequency ◽  
Variable Speed ◽  
Vibration Signals ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Representation ◽  
Study Results ◽  
Gearbox Vibration

Abstract The adaptive chirp mode decomposition (ACMD) has good time-frequency representation results in analyzing chirp signals, while there is a time-frequency ambiguity problem in the analysis of variable speed planetary gearbox vibration signals. To address this problem, a planetary gearbox fault diagnosis method based on improved polynomial adaptive chirp mode decomposition wavelet is proposed (IPACMD). Using Adaptive chirp mode decomposition, the amplitude and instantaneous frequency of multiple signal components are estimated; To avoid over-decomposition to generate spurious signal components, the similarity conditional entropy is used to optimize the adaptive chirp mode decomposition threshold ;The polynomial chirp transform (PCT) using a polynomial function instead of the linear chirp kernel in the chirp transform to improve the time-frequency aggregation of the instantaneous frequency curve of each signal component and output high-resolution time-frequency representation results. Compared with the original method, the proposed method has better time-frequency aggregation and is more effective for the analysis of variable speed planetary gearbox vibration signals. The simulation and experimental study results show that the method can effectively identify the frequency components and time-frequency characteristics of the variable-speed planetary gearbox vibration signal and realize the fault diagnosis of the planetary gearbox.

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