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Sensors 2014, 14(1), 382-402; doi:10.3390/s140100382
Article

Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis

1,* , 1
 and 2
Received: 5 November 2013; in revised form: 10 December 2013 / Accepted: 12 December 2013 / Published: 27 December 2013
(This article belongs to the Section Physical Sensors)
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Abstract: Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods.
Keywords: vibration sensor; data denoising; time-frequency manifold; machinery fault diagnosis; bearing vibration sensor; data denoising; time-frequency manifold; machinery fault diagnosis; bearing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

He, Q.; Wang, X.; Zhou, Q. Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis. Sensors 2014, 14, 382-402.

AMA Style

He Q, Wang X, Zhou Q. Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis. Sensors. 2014; 14(1):382-402.

Chicago/Turabian Style

He, Qingbo; Wang, Xiangxiang; Zhou, Qiang. 2014. "Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis." Sensors 14, no. 1: 382-402.


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