A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor
Source abstract
Abstract
The accuracy and stability of the envelope estimation function are enduring issues throughout the research process of LMD. This paper presents double interpolation and mutation interval reconstruction local mean decomposition (DIMIRLMD) to improve the stability of the demodulation process and the accuracy of PF components. DIMIRLMD first proposes a mutation interval reconstruction envelope algorithm using extreme symmetry points to suppress the demodulation mutation phenomenon, which disturbs the stability of the demodulation process, and then selects the optimal PF component from a double interpolation PF component library based on the index of orthogonality (IO) for a better hierarchical property. DIMIRLMD was employed to analyze the simulation signal and vibration signal of a reciprocating compressor in an oversized bearing clearance state, and the results illustrate its performances are more excellent than those of three other LMD methods. Furthermore, the envelope frequency spectrum obtained from the proposed LMD presents a clear double rotation fault frequency and lower noise disturbance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Tribology and Lubrication Engineering · Hydraulic and Pneumatic Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Mechanical Science and Engineering Institute, Northeast Petroleum University, Daqing 163318, China
Shandong Fenghui Equipment Technology Co., Ltd., Jinan 250200, China
Funding
Heilongjiang Natural Science Foundation in China · China Innovation and Entrepreneurship Training Program for College Students
References (38)
Show source-provided references
1. Basic research on machinery fault diagnostics: Past, present, and future trends10.1007/s11465-018-0472-3 ↗
2. A compound interpolation envelope local mean decomposition and its application for fault diagnosis of reciprocating compressors10.1016/j.ymssp.2018.03.035 ↗
3. Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples10.1016/j.ymssp.2013.01.017 ↗
4. Machine learning applications in power system fault diagnosis: Research advancements and perspectives10.1016/j.engappai.2021.104504 ↗
5. Variational time–frequency adaptive decomposition of machine multi-impact vibration signals10.1016/j.ymssp.2022.110084 ↗
6. Performance evaluation of decomposition methods to diagnose leakage in a reciprocating compressor under limited speed variation10.1016/j.ymssp.2018.07.029 ↗
7. Investigation on recognition method of acoustic emission signal of the compressor valve based on the deep learning method10.1016/j.egyr.2021.10.053 ↗
8. Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network10.1016/j.measurement.2020.107619 ↗
9. Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor10.1016/j.neucom.2019.11.006 ↗
10. Lv, Q., Cai, L., Yu, X., Ma, H., Li, Y., and Shu, Y. (2022). An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN. Appl. Sci., 12.10.3390/app12105182 ↗
11. Fault Feature Extraction Method of Reciprocating Compressor Valve Based on SPA-MF10.1109/ACCESS.2022.3226512 ↗
12. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis10.1098/rspa.1998.0193 ↗
13. The Local Mean Decomposition and Its Application to EEG Perception Data
14. Variational Mode Decomposition10.1109/TSP.2013.2288675 ↗
15. Stability analysis in milling process using spline based local mean decomposition (SBLMD) technique and statistical indicators10.1016/j.measurement.2021.108999 ↗
16. Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems10.1109/ACCESS.2021.3064976 ↗
17. Research on gearbox composite fault diagnosis based on improved local mean decomposition10.1007/s40435-020-00744-6 ↗
18. Improved local mean decomposition for modulation information mining and its application to machinery fault diagnosis10.1016/j.jsv.2017.02.055 ↗
19. Elimination of End Effects in Local Mean Decomposition Using Spectral Coherence and Applications for Rotating Machinery10.1016/j.dsp.2016.04.007 ↗
20. Early Fault Diagnosis of Rotating Machinery by Combining Differential Rational Spline-Based LMD and K–L Divergence10.1109/TIM.2017.2664599 ↗
21. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems10.1016/j.measurement.2011.10.010 ↗
22. Review of local mean decomposition and its application in fault diagnosis of rotating machinery10.21629/JSEE.2019.04.17 ↗
23. Empirical Mode Decomposition Based on Higher Order Spline interpolation
24. Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning10.1109/ACCESS.2021.3067152 ↗
25. Zhang, Y., Li, S., Zhang, A., Li, C., and Qiu, L. (2022). A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets. Entropy, 24.10.3390/e24091295 ↗
26. Fault diagnosis of gearbox based on local mean decomposition and discrete hidden Markov models10.1177/0954406216638885 ↗
27. An order tracking technique for the gear fault diagnosis using local mean decomposition method10.1016/j.mechmachtheory.2012.04.008 ↗
28. A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines10.1109/TII.2020.3045002 ↗
29. Time Sequential Phase Partition and Modeling Method for Fault Detection of Batch Processes10.1109/ACCESS.2017.2778095 ↗
30. Adaptively Detecting the Transient Feature of Faulty Wind Turbine Planetary Gearboxes by the Improved Kurtosis and Iterative Thresholding Algorithm10.1109/ACCESS.2018.2809744 ↗
31. Online Semi-Supervised Broad Learning System for Industrial Fault Diagnosis10.1109/TII.2020.3048990 ↗
32. Mezni, Z., Delpha, C., Diallo, D., and Braham, A. (2022). Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features. Entropy, 24.10.3390/e24091251 ↗
33. Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis10.1109/ACCESS.2021.3065307 ↗
34. Intelligent fault detection of reciprocating compressor using a novel discrete state space10.1016/j.ymssp.2021.108583 ↗
35. Detection and estimation of valve leakage losses in reciprocating compressor using acoustic emission technique10.1016/j.measurement.2019.107315 ↗
36. Bearing failure of reciprocating compressor sub-health recognition based on CAGOA-VMD and GRCMDE10.1177/16878132221082975 ↗
37. A Fault Feature Extraction Method for Rolling Bearings Based on Refined Composite Multi-Scale Amplitude-Aware Permutation Entropy10.1109/ACCESS.2021.3078823 ↗
38. Ma, J., Zhuo, S., Li, C., Zhan, L., and Zhang, G. (2021). An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis. Symmetry, 13.10.3390/sym13040617 ↗