Gearbox fault diagnosis using adaptive redundant Lifting Scheme

Jiang, Hongkai; He, Zhengjia; Duan, Chang‐Kui; Peng, Chen

doi:10.1016/j.ymssp.2005.06.001

Cited by 69 publications

(6 citation statements)

References 8 publications

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“…. , , the expression of the undecimated prediction operator of the th level is given by [34] According to the principle of equidistance subdivision, the predictor coefficient is calculated by Lagrange interpolation formula as [29] =∏…”

Section: Formulation Of Undecimated Lwpt With Boundarymentioning

confidence: 99%

Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Duan

Wang

et al. 2016

Shock and Vibration

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Effective signal processing in fault detection and diagnosis (FDD) is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet packet transform (ULWPT) is investigated for machinery incipient fault diagnosis. Undecimated lifting wavelet packet transform is firstly formulated to eliminate the frequency aliasing issue in traditional lifting wavelet packet transform. Next, Volterra series, as a boundary treatment method, is used to preprocess the signal to suppress the end distortion in undecimated lifting wavelet packet transform. Finally, the decomposed wavelet coefficients are trimmed to the original length as the signal of interest for machinery incipient fault detection. Experimental study on a reciprocating compressor is performed to demonstrate the effectiveness of the presented method. The results show that the presented method outperforms the conventional approach by dramatically enhancing the weak defect feature extraction for reciprocating compressor valve fault diagnosis.

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Section: Formulation Of Undecimated Lwpt With Boundarymentioning

confidence: 99%

Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Duan

Wang

et al. 2016

Shock and Vibration

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“…The second-generation wavelet packet decomposition is used to extract the energy features in the time-frequency domain [6, 26]. Basically, the second generation wavelet transform uses a general construction of the lifting scheme which has three major advantages (1) the fully in-place calculation of wavelet transforms, (2) no requirement for auxiliary memory, and (3) a faster implementation of the wavelet transform [27, 28].…”

Section: Online Embedded Machinery Diagnosis Approachmentioning

confidence: 99%

A Computationally Efficient and Adaptive Approach for Online Embedded Machinery Diagnosis in Harsh Environments

Jiang

Huang

2013

Advances in Mechanical Engineering

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Condition-based monitoring (CBM) has advanced to the stage where industry is now demanding machinery that possesses self-diagnosis ability. This need has spurred the CBM research to be applicable in more expanded areas over the past decades. There are two critical issues in implementing CBM in harsh environments using embedded systems: computational efficiency and adaptability. In this paper, a computationally efficient and adaptive approach including simple principal component analysis (SPCA) for feature dimensionality reduction and K-means clustering for classification is proposed for online embedded machinery diagnosis. Compared with the standard principal component analysis (PCA) and kernel principal component analysis (KPCA), SPCA is adaptive in nature and has lower algorithm complexity when dealing with a large amount of data. The effectiveness of the proposed approach is firstly validated using a standard rolling element bearing test dataset on a personal computer. It is then deployed on an embedded real-time controller and used to monitor a rotating shaft. It was found that the proposed approach scaled well, whereas the standard PCA-based approach broke down when data quantity increased to a certain level. Furthermore, the proposed approach achieved 90% accuracy when diagnosing an induced fault compared to 59% accuracy obtained using the standard PCA-based approach.

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“…In the field of mechanical engineering a problem of great importance is that under operating conditions of many mechanical equipment, signals carrying useful information are submerged by heavy background noise. Conventional methods for the enhancement of the useful signal, so that it can be detected, consist of filtering or masking the noise [2,3] while in the SR mechanism the noise is used to enhance the signal, reducing the risk of losing the information of interest. In this context the flow of the detection scheme is as follows.…”

Section: Introductionmentioning

confidence: 99%

Weak signal detection based on two dimensional stochastic resonance

Barbini

Cole

Hillis

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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The analysis of vibrations from rotating machines gives information about their faults. From the signal processing perspective a significant problem is the detection of weak signals embedded in strong noise. Stochastic resonance (SR) is a mechanism where noise is not suppressed but exploited to trigger the synchronization of a non-linear system and in its one-dimensional form has been recently applied to vibration analysis. This paper focuses on the use of SR in a twodimensional system of gradient type for detection of weak signals submerged in Gaussian noise. Comparing the traditional one-dimensional system and the two-dimensional used here, this paper shows that the latter can offer a more sensitive means of detection. An alternative metric is proposed to assess the output signal quality, requiring no a priori knowledge of the signal to be detected, and it is shown to offer similar results to the more conventional signal-to-noise ratio.

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Gearbox fault diagnosis using adaptive redundant Lifting Scheme

Cited by 69 publications

References 8 publications

Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

A Computationally Efficient and Adaptive Approach for Online Embedded Machinery Diagnosis in Harsh Environments

Weak signal detection based on two dimensional stochastic resonance

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