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Generalized reassigning transform: Algorithm and applications

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  • Zhao, Dezun
  • Huang, Xiaofan
  • Wang, Tianyang
  • Cui, Lingli

Abstract

Time-frequency analysis (TFA) has attracted the attention of many scholars and engineers for analyzing nonstationary signals in the field of condition monitoring of rotating machinery. For complex mechanical equipment, measured signals always contain both harmonic and impulsive components, which presents a challenge for current TFA methods. To concurrently characterize harmonic and impulsive components, a novel TFA algorithm, called generalized reassigning transform (GRT) is developed in this paper. First, the time-frequency fusion extraction criterion (TFFEC), which includes time-frequency data fusion (TFDF) and time-frequency data extraction (TFDE), is designed to calculate time-frequency representation (TFR) from short-time Fourier transform (STFT) results under different window sizes, which improves time-frequency resolution and eliminates noise influence. Furthermore, the chirp rate (CR)-based postprocessing strategy is constructed to characterize nonstationary signals with both harmonic-like and impulsive-like components. Specifically, the CR discrimination criterion is introduced to classify the TFFEC result into two distinct types: harmonic-like component and impulsive-like component, and then, the TFR with high energy concentration and strong readability is obtained by advanced postprocessing TFA including the synchro-reassigning transform (SRT) and horizontal reassigning transform (HRT). The effectiveness of the GRT in condition monitoring and fault diagnosis is validated through numerical and experiment verification.

Suggested Citation

  • Zhao, Dezun & Huang, Xiaofan & Wang, Tianyang & Cui, Lingli, 2025. "Generalized reassigning transform: Algorithm and applications," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:reensy:v:255:y:2025:i:c:s0951832024007488
    DOI: 10.1016/j.ress.2024.110677
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    References listed on IDEAS

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    1. Liang, Pengfei & Tian, Jiaye & Wang, Suiyan & Yuan, Xiaoming, 2024. "Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Chaleshtori, Amir Eshaghi & Aghaie, Abdollah, 2024. "A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Su, Yunsheng & Shi, Luojie & Zhou, Kai & Bai, Guangxing & Wang, Zequn, 2024. "Knowledge-informed deep networks for robust fault diagnosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Tang, Shengnan & Zhu, Yong & Yuan, Shouqi, 2022. "Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Kumar, Anil & Parkash, Chander & Vashishtha, Govind & Tang, Hesheng & Kundu, Pradeep & Xiang, Jiawei, 2022. "State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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