An adaptive envelope spectrum technique for bearing fault detection

Sui, Wentao; Osman, Shazali; Wang, Wilson

doi:10.1088/0957-0233/25/9/095004

Cited by 28 publications

(25 citation statements)

References 23 publications

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“…In the matrix theory, singular values generated by SVD present the inherent feature of matrix and possess the characteristics of scale invariance, rotating invariance, and favorable stability. 1 Therefore, the singular values of the matrix whose rows are desired PRCs are very feasible to be the feature vector for ELM training and testing.…”

Section: Fault Singular Value Extraction Based On Svdmentioning

confidence: 99%

A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine

Tong

Cao

Han

et al. 2017

Advances in Mechanical Engineering

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The fault diagnosis of rolling element bearings is very important for ensuring the safe operation of rotary machineries. Targeting the nonstationary characteristics of the vibration signals of rolling element bearings, a novel approach based on dual-tree complex wavelet packet transform, improved intrinsic timescale decomposition, and the online sequential extreme learning machine is proposed in this article for the fault recognition of rolling element bearing. First, the feature extraction method of the measured signal is presented by combining improved intrinsic timescale decomposition with dual-tree complex wavelet packet transform as preprocessor and two-step screening processes based on the energy ratio, the vibration signal is adaptively decomposed into a set of proper rotation components; second, the matrix formed by different proper rotation components and singular value decomposition is used to obtain singular value as eigenvector; finally, singular values are input to online sequential extreme learning machine to realize the fault diagnosis of rolling element bearings. The effectiveness of the proposed method of fault diagnosis is demonstrated. The experimental results show that the proposed method can effectively extract the fault characteristics and accurately identify the fault patterns.

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Section: Fault Singular Value Extraction Based On Svdmentioning

confidence: 99%

A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine

Tong

Cao

Han

et al. 2017

Advances in Mechanical Engineering

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show abstract

“…However, it is difficult to determine the threshold value of the damage, in particular in different machines. Frequency-domain approaches are usually employed to find the fault's characteristic frequencies via frequency analysis, such as the Fourier spectrum, cepstrum analysis, and the envelope spectrum [8][9][10]. This approach is characterized by its simplicity and intuitive nature for locating the components corresponding to shaft frequency in the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling element bearings with a spectrum searching method

Qiu

Zhu

et al. 2017

Meas. Sci. Technol.

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Rolling element bearing faults in rotating systems are observed as impulses in the vibration signals, which are usually buried in noises. In order to effectively detect the fault of bearings, a novel spectrum searching method is proposed. The structural information of spectrum (SIOS) on a predefined frequency grid is constructed through a searching algorithm, such that the harmonics of impulses generated by faults can be clearly identified and analysed. Local peaks of the spectrum are projected onto certain components of the frequency grid, and then the SIOS can interpret the spectrum via the number and power of harmonics projected onto components of the frequency grid. Finally bearings can be diagnosed based on the SIOS by identifying its dominant or significant components. Mathematical formulation is developed to guarantee the correct construction of the SIOS through searching. The effectiveness of the proposed method is verified with simulated signals and experimental signals.

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“…Numerous techniques have been developed in the time domain [2], the frequency domain [3,4], and timefrequency domain [5,6]. Artificial intelligent techniques, fuzzy inference [7], neurofuzzy [8], and ART-Kohonen neural network (ART-KNN) [9], for instance, are introduced to enhance fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

Lin

Shangfei

et al. 2017

Shock and Vibration

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The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH) is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR) by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.

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An adaptive envelope spectrum technique for bearing fault detection

Cited by 28 publications

References 23 publications

A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine

A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine

Fault diagnosis of rolling element bearings with a spectrum searching method

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

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