The Average Coding Length of Huffman Coding Based Signal Processing and Its Application in Fault Severity Recognition
Source abstract
Abstract
The transient impact components in vibration signal, which are the major information for bearing fault severity recognition, are often interfered with by ambient noise. Meanwhile, for bearing fault severity recognition, the frequency band selection methods which are employed to pre-process the contaminated vibration signal only select the partial frequency band of the vibration signal and cause information loss of other frequency band. Aiming at this issue, this paper proposes a novel fault severity recognition method based on Huffman coding, which can retain all the information of the frequency band, and is applied for the first time to bearing fault severity recognition. Specifically, the average coding length of Huffman coding (ACLHC) of the original vibration signal is first calculated to reduce the noise and highlight the impact components of the signal. Then, the ACLHC is encoded by symbolic aggregate approximation (SAX) to reflect the modulation information of bearing. Finally, the Lempel‑Ziv indicator (LZ indicator) of the symbol sequence is calculated to reflect the fault severity. The proposed method is verified by the bearing datasets under different working conditions. Compared with the methods based on frequency band selection, the proposed method effectively recognizes the fault severity of bearing for more working conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China
References (29)
Show source-provided references
1. Model for the vibration produced by a single point defect in a rolling element bearing10.1016/0022-460X(84)90595-9 ↗
2. Du, Y., Chen, Y., Meng, G., Ding, J., and Xiao, Y. (2018). Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images. Appl. Sci. Basel., 8.10.3390/app8091538 ↗
3. Chen, Y., Zhang, T., Luo, Z., and Sun, K. (2019). A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method. Appl. Sci. Basel, 9.10.3390/app9112356 ↗
4. Complexity as a Measure for Machine Health Evaluation10.1109/TIM.2004.831169 ↗
5. Fault severity assessment for rolling element bearings using the Lempel‑Ziv complexity and continuous wavelet transform10.1016/j.jsv.2008.07.011 ↗
6. Fault severity assessment for rolling element bearings based on EMD and Lempel‑Ziv index
7. Fault severity assessment for rolling element bearings based on LMD and Lempel‑Ziv index
8. Radial Wear Degree Recognition of Bearing based on LMD and Lempel‑Ziv Index
9. Rolling bearing fault diagnosis based on ITD Lempel‑Ziv complexity and PSO-SVM
10. Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel‑Ziv complexity10.1016/j.jsv.2016.09.008 ↗
11. Quantitative trend fault diagnosis of a rolling bearing based on Sparsogram and Lempel‑Ziv10.1016/j.measurement.2018.06.051 ↗
12. Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms10.1007/s12206-009-0807-4 ↗
13. Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis10.1109/TIE.2015.2509913 ↗
14. Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR10.1016/j.apacoust.2017.01.005 ↗
15. The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines10.1016/j.ymssp.2004.09.002 ↗
16. Kurtogram manifold learning and its application to rolling bearing weak signal detection10.1016/j.measurement.2018.06.026 ↗
17. Meshing frequency modulation (MFM) index-based kurtogram for planet bearing fault detection10.1016/j.jsv.2018.06.051 ↗
18. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram10.1016/j.ymssp.2010.05.018 ↗
19. The design of a new sparsogram for fast bearing fault diagnosis10.1016/j.ymssp.2013.05.024 ↗
20. Periodicity-impulsiveness spectrum based on singular value negentropy and its application for identification of optimal frequency band10.1109/TIE.2018.2844792 ↗
21. Huffman, D.A. (1951). A Method for the Construction of Minimum-Redundancy Codes. Proceedings of Institute of Radio Engineers, IEEE.
22. Petrini, A.C., and Ionescu, V.M. (2016, January 19–21). Study of Huffman Coding Performance in Linux and Windows 10 IoT for Different Frameworks. Proceedings of the International Conference on European Transnational Education International Workshop on Soft Computing Models in Industrial and Environmental Applications Computational Intelligence in Security for Information Systems Conference, San Sebastian, Spain.10.1007/978-3-319-47364-2_76 ↗
23. A Fast Algorithm for Optimal Length-Limited Huffman Codes10.1145/79147.79150 ↗
24. Huffman Coding Based Adaptive Spatial Modulation10.1109/TWC.2017.2705679 ↗
25. On the Complexity of Finite Sequences10.1109/TIT.1976.1055501 ↗
26. EEG complexity as a measure of depth of anesthesia for patients10.1109/10.966601 ↗
27. Fault diagnosis of bearing based on Symbolic Aggregate approXimation and Lempel-Ziv10.1016/j.measurement.2019.02.011 ↗
28. Lessmeier, C., Kimotho, J.K., Zimmer, D., and Sextro, W. (2016, January 5–8). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. Proceedings of the European Conference of the Prognostics and Health Management Society, Bilbao, Spain.10.36001/phme.2016.v3i1.1577 ↗
29. The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data10.1016/j.ymssp.2018.10.010 ↗