A Time-Frequency Distribution based on a Moving and Combined Kernel and its Application in the Fault Diagnosis of Rotating Machinery

There are still some remaining issues for time–frequency distribution application in rolling bearing fault diagnosis, such as noise suppression and resolution improvement. In this paper, we proposed a novel time–frequency correlation matching and reconstruction method to enhance the ability of rolling bearing fault identification. Firstly, we use the optimal simulated bearing fault signal to obtain the matching template through time–frequency distribution. Then, correlation matching operation is conducted between the obtained matching template and the original time–frequency distribution of analyzed signal. Finally, the original time–frequency distribution is reconstructed with the correlation coefficients and matching template using the template reconstruction algorithm. The reconstructed time–frequency distribution has inherited the capability of matching template in noise suppression, and can reveal the fault impulses of interest in a unified scale. The effectiveness of the proposed method has been proved by experimental result.

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Adaptive radial sinc kernel distribution and its application in mechanical fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406215616985 ◽

2016 ◽

Vol 231 (3) ◽

pp. 485-493 ◽

Cited By ~ 2

Author(s):

Zhinong Li ◽

Ming Zhu ◽

Fulei Chu ◽

Xuping He

Keyword(s):

Fault Diagnosis ◽

Frequency Distribution ◽

Kernel Function ◽

Rolling Bearing ◽

Kernel Functions ◽

Frequency Resolution ◽

Good Time ◽

Time Frequency ◽

Fixed Kernel ◽

Time Frequency Distribution

Based on the deficiency of fixed-kernel in the traditional time–frequency distribution, which is lack of adaptability, a new adaptive kernel function, which is named as the adaptive radial sinc kernel, is proposed according to design criteria of adaptive optimal kernel. The definition and algorithm of radial sinc kernel are given, and the proposed method is compared with the tradition time–frequency distribution. The simulation results show that the proposed method is superior to the traditional fixed-kernel functions, such as Wigner–Ville distribution, Choi–Williams distribution, cone-kernel distribution and continuous wavelet transform. The adaptive radial sinc kernel can overcome the deficiency of fixed-kernel function in traditional time–frequency distribution, adopt the optimizing method to filter the cross-terms adaptively according to the signal distribution, obtain good time–frequency resolution and has extensive adaptability for an arbitrary signal. Finally, the proposed method has been applied to the fault diagnosis of rolling bearing, and the experiment result shows that the proposed method is very effective.

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MECHANICAL FAULT DIAGNOSIS BASED ON A NEW TIME-FREQUENCY DISTRIBUTION

Journal of Mechanical Engineering ◽

10.3901/jme.2003.07.150 ◽

2003 ◽

Vol 39 (07) ◽

pp. 150 ◽

Cited By ~ 1

Author(s):

Xinqing Wang

Keyword(s):

Fault Diagnosis ◽

Frequency Distribution ◽

Time Frequency ◽

Time Frequency Distribution ◽

New Time

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Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216639342 ◽

2016 ◽

Vol 231 (14) ◽

pp. 2664-2680

Author(s):

Shangbin Zhang ◽

Qingbo He ◽

Haibin Zhang ◽

Kesai Ouyang ◽

Fanrang Kong

Keyword(s):

Time Series ◽

Fault Diagnosis ◽

Frequency Distribution ◽

Signal Separation ◽

Frequency Distributions ◽

Time Frequency ◽

S Transform ◽

Generalized S Transform ◽

Original Time ◽

Time Frequency Distribution

The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.

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