RESEARCH PAPER
Local Entropy Selection Scaling-extracting Chirplet Transform for Enhanced Time-Frequency Analysis and Precise State Estimation in Reliability-Focused Fault Diagnosis of Non-stationary Signals
1
Beijing Jiaotong University, China
2
Beijing Institute of Petrochemical Technology, China
3
Tsinghua University, China
Submission date: 2025-02-17
Final revision date: 2025-03-30
Acceptance date: 2025-06-05
Online publication date: 2025-06-08
Publication date: 2025-06-08
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(1):205977
HIGHLIGHTS
- LESSECT enhances time-frequency resolution for non-stationary signals.
- LESSECT excels in resolving closely spaced, non-proportional instantaneous frequencies.
- LESSECT overcomes energy leakage and blurring issues in traditional TFA techniques.
- LESSECT improves the reliability of fault detection in rotating machinery systems.
KEYWORDS
fault diagnosissystem reliabilityTime-frequency analysisnon-stationary signalsnon-proportional instantaneous frequency
TOPICS
ABSTRACT
Under diverse conditions, the vibration signals of complex rotating machinery exhibit non-stationary behavior, multi-component characteristics, closely spaced frequencies, and non-proportionality, posing challenges to conventional time-frequency analysis (TFA) methods. These limitations hinder accurate instantaneous frequency (IF) estimation and time-frequency representation (TFR) construction, directly impacting machinery fault diagnosis. As such, we propose the Local Entropy Selection Scaling-Extracting Chirplet Transform (LESSECT), which optimizes entropy-based chirp rate (CR) selection to match non-proportional fundamental frequencies. By adaptively selecting multiple CRs at the same time center, LESSECT enhances TFR resolution and energy concentration, leading accurate IF identification. Experimental validation on bat echolocation, bearing fault, and planetary gearbox signals shows its superior performance in resolving non-proportional, closely spaced IFs. This significantly improves state estimation and enhances machinery diagnostics, contributing to greater system reliability.
REFERENCES (36)
1.
Wei Y, Li Y, Xu M, Huang W. A review of early fault diagnosis approaches and their applications in rotating machinery. Entropy. 2019;21(4):409. https://doi.org/10.3390/e21040....
2.
Liu D, Cui L, Wang H. Rotating machinery fault diagnosis under time-varying speeds: A review. IEEE Sensors J. 2023. https://doi.org/10.1109/JSEN.2....
3.
Zhu X, Sun J, Hu H, Li C, A Semi-Supervised Siamese Network for Complex Aircraft System Fault Detection with Limited Labeled Fault Samples, Eksploatacja i Niezawodnosc – Maintenance and Reliability 2023: 25(4) http://doi.org/10.17531/ein/17....
4.
Su Z, Hua Z, Hu D, Zhao M, Research on Equipment Reliability Modeling and Periodic Maintenance Strategies in Dynamic Environment, Eksploatacja i Niezawodnosc – Maintenance and Reliability 2025: 27(1) http://doi.org/10.17531/ein/19....
5.
Zhang X, Zhao J. Compound fault detection in gearbox based on time synchronous resample and adaptive variational mode decomposition. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22 (1): 161–169, http://dx.doi.org/10.17531/ein....
6.
Huang R, Xia J, Zhang B, Chen Z, Li W. Compound fault diagnosis for rotating machinery: State-of-the-art, challenges, and opportunities. J Dyn Monit Diagn. 2023;13-29. https://doi.org/10.37965/jdmd.....
7.
Tiboni M, Remino C, Bussola R, Amici C. A review on vibration-based condition monitoring of rotating machinery. Appl Sci. 2022;12(3):972, https : //doi.org/10.3390/app12030972.
8.
Lu S, Lu J, An K, Wang X, He Q. Edge computing on IoT for machine signal processing and fault diagnosis: A review. IEEE Internet Things J. 2023;10(13):11093-11116, https://doi.org/10.1109/JIOT.2....
9.
Zhang J, Kong Y, Chen Z, Han, T, Han, Q, Dong, M, Chu, F. CBAM-CRLSGAN: A novel fault diagnosis method for planetary transmission systems under small samples scenarios. Measurement, 2024;234:114795, https://doi.org/10.1016/j.meas....
10.
Kong, Y, Han, Q, Chu, F, Qin, Y, Dong, M. Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox. Renewable Energy. 2023;219:119373, https://doi.org/10.1016/j.rene....
11.
Lin C, Kong Y, Han Q, Zhang X, Qi J, Rao M, et al. An unsupervised multi-level fusion domain adaptation method for transfer diagnosis under time-varying working conditions. Mech Syst Signal Process. 2025;228:112458, https://doi.org/10.1016/j.ymss....
12.
Li, X, Yu, T, Zhang, F, Huang, J, He, D, Chu, F. Mixed style network based: A novel rotating machinery fault diagnosis method through batch spectral penalization. Reliability Engineering & System Safety, 2024;255:110667, https://doi.org/10.1016/j.ress....
13.
Li X, Yu T, Zhang F, Huang J, He D, Chu F. Mixed style network based: A novel rotating machinery fault diagnosis method through batch spectral penalization. Reliab Eng Syst Saf. 2025;255:110667, https://doi.org/10.1016/j.ress....
14.
Zhao D, Cai W, Cui L. Multi-perception Graph Convolutional Tree-embedded Network for Aero-engine Bearing Health Monitoring with Unbalanced Data[J]. Reliability Engineering & System Safety, 2025: 110888.
15.
Liu T, Yan S, Zhang W. Time-frequency analysis of nonstationary vibration signals for deployable structures by using the constant-Q nonstationary Gabor transform. Mech Syst Signal Process. 2016;75:228-244. https://doi.org/10.1016/j.ymss....
16.
Yang Y, Peng Z, Zhang W, et al. Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances. Mech Syst Signal Process. 2019;119:182-221. https://doi.org/10.1016/j.ymss....
17.
Portnoff M. Time-frequency representation of digital signals and systems based on short-time Fourier analysis. IEEE Trans Acoust Speech Signal Process. 1980;28(1):55-69. https://doi.org/10.1109/TASSP.....
18.
Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory. 1990;36(5):961-1005. https://doi.org/10.1109/18.571....
19.
Boashash B, Black P. An efficient real-time implementation of the Wigner-Ville distribution. IEEE Trans Acoust Speech Signal Process. 1987;35(11):1611-1618. https://doi.org/10.1109/TASSP.....
20.
Mann S, Haykin S. The chirplet transform: Physical considerations. IEEE Trans Signal Process. 1995;43(11):2745-2761. https://doi.org/10.1109/78.482....
21.
Yu G, Zhou Y. General linear chirplet transform. Mech Syst Signal Process. 2016;70-71:958-973. https://doi.org/10.1016/j.ymss....
22.
Guan Y, Liang M, Necsulescu DS. Velocity synchronous linear chirplet transform. IEEE Trans Ind Electron. 2018;66(6):6270-6280. https://doi.org/10.1109/TIE.20....
23.
Li M, Wang T, Chu F, et al. Scaling-basis chirplet transform. IEEE Trans Ind Electron. 2020;68(9):8777-8788. https://doi.org/10.1109/TIE.20....
24.
Czarnecki, K, Fourer, D, Auger, F, Rojewski, M. A fast time-frquency multi-window analysis using a tuning directional kernel. Signal Prcess. 2018.147:110-119. https://doi.org/10.1016/j.sigp....
25.
Miao Y, Sun H, Qi J. Synchro-compensating chirplet transform. IEEE Signal Process Lett. 2018;25(9):1413-1417. https://doi.org/10.1109/LSP.20....
26.
Zhang D, Feng Z. Proportion-extracting chirplet transform for nonstationary signal analysis of rotating machinery. IEEE Trans Ind Informatics. 2022;19(3):2674-2683. https://doi.org/10.1109/TII.20....
27.
Auger F, Flandrin P. Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Trans Signal Process. 1995;43(5):1068-1089. https://doi.org/10.1109/78.382....
28.
Zhao, D, Huang, X, Cui, L. Horizontal reassigning transform for bearing fault impulses characterizing. IEEE Sensors Journal. 2023;24(2):1837-1846. https://doi.org/10.1109/JSEN.2....
29.
Daubechies I, Lu J, Wu HT. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Appl Comput Harmonic Anal. 2011;30(2):243-261. https://doi.org/10.1016/j.acha....
30.
Yu G, Yu M, Xu C. Synchroextracting transform. IEEE Trans Ind Electron. 2017;64(10):8042-8054. https://doi.org/10.1109/TIE.20....
31.
Holighaus, N, Průša, Z, Søndergaard, P. L. Reassignment and synchrosqueezing for general time–frequency filter banks, subsampling and processing. Signal Processing. 2016;125:1-8. https://doi.org/10.1016/j.sigp....
32.
He Y, Hu M, Jiang Z, Feng K, Ming X. Local maximum synchrosqueezes from entropy matching chirplet transform. Mech Syst Signal Process. 2022;181:109476. https://doi.org/10.1016/j.ymss....
33.
Wu T, Zhang W, Zhang B, Luo H. Application of multi-base fusion generalized chirplet basis transform in vibration signal analysis of multiple rotor rotating machinery. Mech Syst Signal Process. 2023;185:109792. https://doi.org/10.1016/j.ymss....
34.
Randall RB, Antoni J. Rolling element bearing diagnostics-A tutorial. Mech Syst Signal Process. 2011;25(2):485-520. https://doi.org/10.1016/j.ymss....
35.
Huang H, Baddour N. Bearing vibration data collected under time-varying rotational speed conditions. Data Brief. 2018; 21:1745-1749. https://doi.org/10.1016/j.dib.....
36.
Z Feng, M J Zuo, Vibration signal models for fault diagnosis of planetary gearboxes, J. Sound Vib. 331 (2012) 4919–4939, https://doi.org/10.1016/j.jsv.....
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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