Noise-resistant Time-frequency Analysis Method and Its Application in Fault Diagnosis of Rolling Bearing

Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.

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Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network

IEEE Access ◽

10.1109/access.2020.3016888 ◽

2020 ◽

Vol 8 ◽

pp. 150248-150261

Author(s):

Zhihao Chen ◽

Jian Cen ◽

Jianbin Xiong

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Frequency Analysis ◽

Rolling Bearing ◽

Time Frequency Analysis ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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A Time-Frequency Analysis Approach Based on Plane Partition for Fault Diagnosis of the Planetary Gearbox Under Nonstationary Conditions

Volume 8: 30th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2018-85552 ◽

2018 ◽

Author(s):

Yue Hu ◽

Xiaotong Tu ◽

Fucai Li

Keyword(s):

Fault Diagnosis ◽

Frequency Analysis ◽

Analysis Method ◽

Time Frequency Analysis ◽

Planetary Gearbox ◽

Time Frequency ◽

Repair Costs ◽

Important Research Topic ◽

Planet Gear ◽

Nonstationary Conditions

The planetary gearbox is one of the key components in the rotating machinery. The planetary gearbox is prone to malfunction, which increases downtime and repair costs. Hence, the fault diagnosis of the planetary gearbox is an important research topic. The acquired signal from the planetary gearbox exhibit strongly time-variant and nonstationary features since the planetary gearbox usually works at time-varying speeds. In this study, a new time-frequency analysis method is proposed. This method takes the spectrum shape into account and partitions the time-frequency into several components. Then the fault feature of the planetary gearbox is detected by analyzing the decomposed components. The simulated signal and the experimental signals under nonstationary conditions are analyzed to verify the effectiveness the proposed method. Results show that the proposed method can efficiently extract the fault feature of the planet gear.

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Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

Measurement Science Review ◽

10.2478/msr-2019-0024 ◽

2019 ◽

Vol 19 (4) ◽

pp. 185-194 ◽

Cited By ~ 2

Author(s):

Meng-Kun Liu ◽

Peng-Yi Weng

Keyword(s):

Fault Diagnosis ◽

Frequency Analysis ◽

Ball Bearing ◽

Wavelet Packet ◽

Rolling Bearing ◽

Support Vector ◽

Time Frequency Analysis ◽

Bearing Faults ◽

Time Frequency ◽

Low Efficiency

Abstract Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

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Time-frequency analysis method of bearing fault diagnosis based on the generalized S transformation

Journal of Vibroengineering ◽

10.21595/jve.2017.18244 ◽

2017 ◽

Vol 19 (6) ◽

pp. 4221-4230 ◽

Cited By ~ 6

Author(s):

Jianhua Cai ◽

Yongliang Xiao

Keyword(s):

Fault Diagnosis ◽

Frequency Analysis ◽

Analysis Method ◽

Time Frequency Analysis ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Rolling Bearings Fault Diagnosis Based on Generalized Demodulation Time-Frequency Analysis Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.971-973.701 ◽

2014 ◽

Vol 971-973 ◽

pp. 701-704

Author(s):

Yong Xia Bu ◽

Jian De Wu ◽

Jun Ma ◽

Yu Gang Fan ◽

Xiao Dong Wang

Keyword(s):

Fault Diagnosis ◽

Energy Spectrum ◽

Frequency Analysis ◽

Rolling Element Bearing ◽

Original Signal ◽

Analysis Method ◽

Time Frequency Analysis ◽

Time Frequency ◽

Bearing Fault ◽

Instantaneous Energy

In view of the characteristics of the non-stationary and multi-component AM-FM signals of vibration signals in the rolling element bearing, the generalized demodulation time-frequency analysis method is used for its fault diagnosis, overcoming the problem that the maximal overlap discrete wavelet packet transform (MODWPT) has no adaptability. First of all, the original vibration signal is took preprocessing by generalized Fourier; Then, using MODWPT to decompose signals after pretreatment and obtaining weights; Once again, the weights are carried out the inverse generalized Fourier transform to get the weights of the original signal; Finally, reconstructing principal component of the original signal to get the Hilbert instantaneous energy spectrum, roller bearing fault diagnoses based on the Hilbert instantaneous energy spectrum. The experimental results show that the method can effectively diagnose rolling bearing fault.

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