Adaptive synchroextracting transform and its application in bearing fault diagnosis

Yan, Zhu; Xu, Yonggang; Zhang, Kun; Hu, Aijun; Yu, Gang

doi:10.1016/j.isatra.2023.01.006

Cited by 20 publications

(6 citation statements)

References 37 publications

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“…Recently, various advanced TFA methods have been investigated for the fault diagnosis of machinery under time-varying speeds. The researchers at the universities such as University of Science and Technology Beijing [106] , [107] , [108] , [109] , [110] , [111] , Shanghai Jiao Tong University [112], [113], [114], [115], [116], Xi'an Jiaotong University [117] , [118] , [119] , [120] , Beijing University of Chemical Technology [121] , [122] , [123] , University of Jinan [124] , [125] , [126] , [127] , Tsinghua University [128], [25], [129], [130], and Beijing University of Technology [131] , [132] , [133] , [134] , have made great contributions to this field.…”

Section: Time-frequency Analysis Methodsmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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Rotating machinery often works under time-varying speeds, and nonstationary conditions as well as harsh environments make its key parts, such as rolling bearings and gears, prone to faults. Therefore, a number of fault diagnosis methods including nonstationary signal processing methods and data-driven methods have been developed. This paper presents a comprehensive review on the fault diagnosis of rotating machinery under time-varying speeds proposed mainly during the last five years. First, spectrum analysis-based methods, including order tracking, cyclic spectrum correlation and generalized demodulation, are reviewed. Second, the time-frequency analysis (TFA) methods in machinery fault diagnosis are divided into postprocessing methods and chirplet transform-based methods and are reviewed. Then, the artificial feature extraction-and deep learning-enabled intelligent diagnosis methods proposed specifically for time-varying speed conditions are reviewed. Finally, the research prospects are discussed.We not only review the relevant state-of-art methods and analyze how they overcome the problems caused by speed fluctuations but also discuss their advantages and disadvantages as well as the challenges that will be encountered when applying them to industrial applications. This paper is expected to provide new graduate students, institutions and companies with a preliminary understanding of the methods on this topic.

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Section: Time-frequency Analysis Methodsmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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“…The following procedure is adopted to determine the two parameters: First, a fixed delay time is selected, then the cost function of the delay time can be obtained according to the selection criterion. The optimal delay time is searched in the given range according to the cost function of equation (20):…”

Section: Proposal Of Afmdpsfementioning

confidence: 99%

“…The AFMDPSFE algorithm operates on the optimal component: initially, the proposed compound kurtosis criterion is employed to ascertain the optimal delay time τ and embedding dimension M, and the initial ranges of the two parameters are set as 2 ⩽ M ′ ⩽ 30, τ ∈ 2, round fs fg . According to equation (20), the optimal delay time can be searched primarily, and then the optimal embedding dimension searched by combining the optimal delay time with equation (21). The relationship between the two parameters and the kurtosis value obtained at last is shown as figure 7, which can be seen as…”

Section: Simulation Modelmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Xin,

Jiang,

Jiang

et al. 2024

Meas. Sci. Technol.

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The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

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“…Ma et al [23] combine demodulation filtering and SET for fault diagnosis in planetary gearboxes. Yan et al [24] adaptively adjust the filter window width of SET. Xin et al [25] proposed an enhanced empirical wavelet transform based on SET and used it to identify time-varying system.…”

Section: Introductionmentioning

confidence: 99%

A novel time-frequency analysis method for fault diagnosis based on generalized S-transform and synchroextracting transform

Wang,

Fang,

Wang

et al. 2023

Meas. Sci. Technol.

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Rotating machinery usually operates under variable-speed conditions, and how to effectively handle nonstationary signal in fault diagnosis is a critical task. The time-frequency analysis (TFA) method is widely used in the processing of nonstationary signal. To improve the time-frequency resolution and clearly identify instantaneous frequency (IF) characteristics, the adaptive generalized S-synchroextracting transform (AGSSET), which is a novel TFA method proposed in this paper. Firstly, a new transform named adaptive generalized S-transform (AGST) is put forward by optimizing the window function of generalized S transform. In this paper, an adaptive window function optimization method based on the frequency spectrum of the vibration signal is introduced, and the energy concentration measure is used to determine the window function’s parameters in AGST. Simultaneously, the synchrony extraction idea is incorporated into the AGST, then the AGSSET is derived. To address more complex IF characteristics, the synchronous extraction operator (SEO) is reconstructed. In the simulation experiment, the GMLC signal model is selected to represent nonstationary signal and to verify the effectiveness of the proposed method. In addition, bearing fault data is also used for fault diagnosis experiments. The results of both numerical simulation and experimental analysis indicate that AGSSET performs well in identifying the time-varying IF characteristic in nonstationary signals. It can also efficiently detect faults with high accuracy and strong stability.

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Adaptive synchroextracting transform and its application in bearing fault diagnosis

Cited by 20 publications

References 37 publications

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

A novel time-frequency analysis method for fault diagnosis based on generalized S-transform and synchroextracting transform

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