Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Oct 26, 2023
Ashwani Kumar
Daniel Strömbergsson
Par Marklund
Fredrik Sandin
Abstract
Nowadays, lots of data are generated in industries using vibration sensors to evaluate the equipment’s working condition and identify faults. A significant challenge is that only a small fraction of data can be transmitted for intelligent fault diagnosis and storage. The edge processing capacity is often insufficient for advanced analysis due to time and resource constraints. The neuromorphic signal encoding scheme efficiently reduces the data rate by encoding relevant signal changes into spike trains while discarding redundant information and noise, enabling energy-efficient neuromorphic processing. Due to the presence of dominant operational features and noise in the original measurements, signal pre-processing is required to extract the relevant features before spike coding and processing. The work investigates the effects of different filter banks (pre-processing methods) on the spike encodings for vibration measurements from bearings. This also includes bearing fault features diagnosis based on statistical analysis of generated spikes. The comparative analysis is made for benchmarking different signal pre-processing methods (e.g., envelope, empirical mode decomposition (EMD), and gammatone filter) on bearing vibration datasets. An event-triggered scheme, i.e., Level-crossing analog-to-digital converters (LC-ADCs) is applied to encode the vibration measurement to spikes. Inter-spike intervals (ISIs) statistics are analysed for fault diagnosis of bearings. The results obtained for CWRU bearing databases indicate a possible fault detection and diagnosis with significant data rate reduction and an opportunity for improved computational efficiency. With the developed approach, the envelope filter is found to be the most efficient of all. This work enables a new approach to improve the energy efficiency of condition monitoring systems and further sets a new course of research development in this area using neuromorphic technologies.
How to Cite
Kumar, A. ., Strömbergsson, D., Marklund, P., & Sandin, F. (2023). Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3493
##plugins.themes.bootstrap3.article.details##
Keywords
Fault Diagnosis, neuromorphic processing, Interspike interval (ISI), Bearing, vibration
References
Abdul, Z. K., Al-Talabani, A. K., & Ramadan, D. O. (2020). A Hybrid Temporal Feature for Gear Fault Diagnosis Using the Long Short Term Memory. IEEE Sensors Journal, 20(23), 14444–14452. https://doi.org/10.1109/JSEN.2020.3007262
Ahmer, M., Sandin, F., Marklund, P., Gustafsson, M., & Berglund, K. (2022). Using Multivariate Quality Statistic for Maintenance Decision Support in a Bearing Ring Grinder. Machines, 10(9). https://doi.org/10.3390/machines10090794
Antoni, J. (2021a). A Critical Overview of the “Filterbank-Feature-Decision” Methodology in Machine Condition Monitoring. Acoustics Australia, 49(2), 177–184. https://doi.org/10.1007/s40857-021-00232-7
Antoni, J. (2021b). A Critical Overview of the “Filterbank-Feature-Decision” Methodology in Machine Condition Monitoring. Acoustics Australia, 49(2), 177–184. https://doi.org/10.1007/s40857-021-00232-7
Attoui, I., Oudjani, B., Boutasseta, N., Fergani, N., Bouakkaz, M. S., & Bouraiou, A. (2020). Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis. International Journal of Advanced Manufacturing Technology, 106(7–8), 3409–3435. https://doi.org/10.1007/s00170-019-04729-4
Ault, T., & Bradley, T. (2022a). Risk-based approach for managing obsolescence for automation systems in heavy industries. Systems Engineering, 25(6), 551–560. https://doi.org/10.1002/sys.21635
Ault, T., & Bradley, T. (2022b). Risk-based approach for managing obsolescence for automation systems in heavy industries. Systems Engineering, 25(6), 551–560. https://doi.org/10.1002/sys.21635
Chen, B., Cheng, Y., Zhang, W., & Gu, F. (2022). Enhanced bearing fault diagnosis using integral envelope spectrum from spectral coherence normalized with feature energy. Measurement: Journal of the International Measurement Confederation, 189. https://doi.org/10.1016/j.measurement.2021.110448
Cocatre-Zilgien, J. H., & Delcomyn, F. (1992). Identification of bursts in spike trains. In Journal of Neuroscience Methods (Vol. 41).
Cotterill, E., & Eglen, S. J. (2018). Burst detection methods. http://arxiv.org/abs/1802.01287
de Sá Só Martins, D. H. C., Viana, D. P., de Lima, A. A., Pinto, M. F., Tarrataca, L., Lopes e Silva, F., Gutiérrez, R. H. R., de Moura Prego, T., Monteiro, U. A. B. V., & Haddad, D. B. (2021). Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines. International Journal of Advanced Manufacturing Technology, 114(9–10), 3077–3092. https://doi.org/10.1007/s00170-021-06873-2
Deepu, C. J., Xu, X. Y., Wong, D. L. T., Heng, C. H., & Lian, Y. (2018). A 2.3 μ W ECG-On-Chip for Wireless Wearable Sensors. IEEE Transactions on Circuits and Systems II: Express Briefs, 65(10), 1385–1389. https://doi.org/10.1109/TCSII.2018.2861723
Dennler, N., Haessig, G., Cartiglia, M., & Indiveri, G. (2021). Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks. http://arxiv.org/abs/2106.00687
Dybała, J., & Zimroz, R. (2014). Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Applied Acoustics, 77, 195–203. https://doi.org/10.1016/j.apacoust.2013.09.001
He, Y., Corradi, F., Shi, C., Ven, S. van der, Timmermans, M., Stuijt, J., Detterer, P., Harpe, P., Lindeboom, L., Hermeling, E., Langereis, G., Chicca, E., & Liu, Y. H. (2022). An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves. IEEE Journal of Solid-State Circuits. https://doi.org/10.1109/JSSC.2022.3193846
Hoang, D. T., & Kang, H. J. (2019). A survey on Deep Learning based bearing fault diagnosis. Neurocomputing, 335, 327–335. https://doi.org/10.1016/j.neucom.2018.06.078
Holguín-Londoño, M., Cardona-Morales, O., Sierra-Alonso, E. F., Mejia-Henao, J. D., Orozco-Gutiérrez, Á., & Castellanos-Dominguez, G. (2016). Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/7906834
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Chao Tung, C., & Liu, H. H. (1998). The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. In Source: Proceedings: Mathematical, Physical and Engineering Sciences (Vol. 454). https://www.jstor.org/stable/53161
Ingemarsdotter, E., Kambanou, M. L., Jamsin, E., Sakao, T., & Balkenende, R. (2021). Challenges and solutions in condition-based maintenance implementation - A multiple case study. Journal of Cleaner Production, 296. https://doi.org/10.1016/j.jclepro.2021.126420
Ishii, T., & Hosoya, T. (2020). Interspike intervals within retinal spike bursts combinatorially encode multiple stimulus features. PLoS Computational Biology, 16(11). https://doi.org/10.1371/journal.pcbi.1007726
Kaneoke, Y., & Vitek, J. L. (1996). Burst and oscillation as disparate neuronal properties. In Journal of Neuroscience Methods (Vol. 68).
Karimov, T., Druzhina, O., Karimov, A., Tutueva, A., Ostrovskii, V., Rybin, V., & Butusov, D. (2022). Single-coil metal detector based on spiking chaotic oscillator. Nonlinear Dynamics, 107(1), 1295–1312. https://doi.org/10.1007/s11071-021-07062-2
Kumar, A., Berrouche, Y., Zimroz, R., Vashishtha, G., Chauhan, S., Gandhi, C. P., Tang, H., & Xiang, J. (2023). Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects. Measurement: Journal of the International Measurement Confederation, 211. https://doi.org/10.1016/j.measurement.2023.112615
Kumar, A., Parkash, C., Vashishtha, G., Tang, H., Kundu, P., & Xiang, J. (2022). State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing. Reliability Engineering and System Safety, 221. https://doi.org/10.1016/j.ress.2022.108356
Kuncan, M., Kaplan, K., Mi̇naz, M. R., Kaya, Y., & Ertunç, H. M. (2020). A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. ISA Transactions, 100, 346–357. https://doi.org/10.1016/j.isatra.2019.11.006
Li, J., Ashraf, A., Cardiff, B., Panicker, R. C., Lian, Y., & John, D. (2020). Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms. IEEE Open Journal of Circuits and Systems, 1, 115–123. https://doi.org/10.1109/ojcas.2020.3009822
Loparo, K. A. ,. (2012). Case western reserve university bearing data center. Case Western Reserve University. https://engineering.case.edu/bearingdatacenter
MarketsandMarkets. (2023). Oil Condition Monitoring Market. https://www.marketsandmarkets.com/Market-Reports/machine-health-monitoring-market-29627363.html
Miao, Y., Zhang, B., Lin, J., Zhao, M., Liu, H., Liu, Z., & Li, H. (2022). A review on the application of blind deconvolution in machinery fault diagnosis. Mechanical Systems and Signal Processing, 163. https://doi.org/10.1016/j.ymssp.2021.108202
Mohammed, O. D., & Rantatalo, M. (2020). Gear fault models and dynamics-based modelling for gear fault detection – A review. In Engineering Failure Analysis (Vol. 117). Elsevier Ltd. https://doi.org/10.1016/j.engfailanal.2020.104798
Nguyen, K. T. P., Medjaher, K., & Tran, D. T. (2022). A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10260-y
Pasquale, V., Martinoia, S., & Chiappalone, M. (2010). A self-adapting approach for the detection of bursts and network bursts in neuronal cultures. Journal of Computational Neuroscience, 29(1–2), 213–229. https://doi.org/10.1007/s10827-009-0175-1
Peeters, C., Antoni, J., & Helsen, J. (2020). Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring. Mechanical Systems and Signal Processing, 138. https://doi.org/10.1016/j.ymssp.2019.106556
Quiñones-Grueiro, M., Prieto-Moreno, A., Verde, C., & Llanes-Santiago, O. (2019). Data-driven monitoring of multimode continuous processes: A review. In Chemometrics and Intelligent Laboratory Systems (Vol. 189, pp. 56–71). Elsevier B.V. https://doi.org/10.1016/j.chemolab.2019.03.012
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics-A tutorial. In Mechanical Systems and Signal Processing (Vol. 25, Issue 2, pp. 485–520). Academic Press. https://doi.org/10.1016/j.ymssp.2010.07.017
Saeed, M., Wang, Q., Martens, O., Larras, B., Frappe, A., Cardiff, B., & John, D. (2021). Evaluation of Level-Crossing ADCs for Event-Driven ECG Classification. IEEE Transactions on Biomedical Circuits and Systems, 15(6), 1129–1139. https://doi.org/10.1109/TBCAS.2021.3136206
Safa, A., Van Assche, J., Frenkel, C., Bourdoux, A., Catthoor, F., & Gielen, G. (2023). Exploring Information-Theoretic Criteria to Accelerate the Tuning of Neuromorphic Level-Crossing ADCs. ACM International Conference Proceeding Series, 63–70. https://doi.org/10.1145/3584954.3584994
Sahu, P. K., & Rai, R. N. (2022). Fault Diagnosis of Rolling Bearing Based on an Improved Denoising Technique Using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method. Journal of Vibration Engineering and Technologies. https://doi.org/10.1007/s42417-022-00591-z
Slaney, M. (1993). An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank.
Smith, W. A., & Randall, R. B. (2015a). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. In Mechanical Systems and Signal Processing (Vols. 64–65, pp. 100–131). Academic Press. https://doi.org/10.1016/j.ymssp.2015.04.021
Smith, W. A., & Randall, R. B. (2015b). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. In Mechanical Systems and Signal Processing (Vols. 64–65, pp. 100–131). Academic Press. https://doi.org/10.1016/j.ymssp.2015.04.021
Strömbergsson, D., Marklund, P., & Berglund, K. (2021). Multi-body simulation and validation of fault vibrations from rolling-element bearings. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 235(9), 1834–1841. https://doi.org/10.1177/1350650120977974
Strömbergsson, D., Marklund, P., Berglund, K., & Larsson, P. E. (2020). Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms. Wind Energy, 23(6), 1381–1393. https://doi.org/10.1002/we.2491
Tse, P. W., Peng, Y. H., & Yam, R. (2001). Wavelet analysis and envelope detection for rolling element bearing fault diagnosis—their effectiveness and flexibilities. Journal of Vibration and Acoustics, Transactions of the ASME, 123(3), 303–310. https://doi.org/10.1115/1.1379745
uit het Broek, M. A. J., Teunter, R. H., de Jonge, B., & Veldman, J. (2021). Joint condition-based maintenance and condition-based production optimization. Reliability Engineering and System Safety, 214. https://doi.org/10.1016/j.ress.2021.107743
Van Assche, J., & Gielen, G. (2020). Power Efficiency Comparison of Event-Driven and Fixed-Rate Signal Conversion and Compression for Biomedical Applications. IEEE Transactions on Biomedical Circuits and Systems, 14(4), 746–756. https://doi.org/10.1109/TBCAS.2020.3009027
Van, M., Kang, H. J., & hin, K. S. (2014). Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition. IET Science, Measurement and Technology, 8(6), 571–578. https://doi.org/10.1049/iet-smt.2014.0023
Wang, T., Liu, H., Guo, D., & Sun, X. M. (2023). Continual deep residual reservoir computing for remaining useful life prediction. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2023.3271661
Wei, S., Wang, D., Wang, H., & Peng, Z. (2021). Time-Varying Envelope Filtering for Exhibiting Space Bearing Cage Fault Features. IEEE Transactions on Instrumentation and Measurement, 70. https://doi.org/10.1109/TIM.2020.3033061
Weltin-Wu, C., & Tsividis, Y. (2013). An event-driven clockless level-crossing ADC with signal-dependent adaptive resolution. IEEE Journal of Solid-State Circuits, 48(9), 2180–2190. https://doi.org/10.1109/JSSC.2013.2262738
Yeardley, A. S., Ejeh, J. O., Allen, L., Brown, S. F., & Cordiner, J. (2022). Integrating machine learning techniques into optimal maintenance scheduling. Computers and Chemical Engineering, 166. https://doi.org/10.1016/j.compchemeng.2022.107958
Yu, J., Huang, J., Liu, C., & Xia, B. (2022). Fault Feature of Gearbox Vibration Signals Based on Morphological Filter Dynamic Convolution Autoencoder. IEEE Sensors Journal, 22(23), 22931–22942. https://doi.org/10.1109/JSEN.2022.3213783
Zabin, M., Choi, H. J., & Uddin, J. (2023). Hybrid deep transfer learning architecture for industrial fault diagnosis using Hilbert transform and DCNN–LSTM. Journal of Supercomputing, 79(5), 5181–5200. https://doi.org/10.1007/s11227-022-04830-8
Zhang, D., Chen, Y., Guo, F., Karimi, H. R., Dong, H., & Xuan, Q. (2021). A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings. IEEE Transactions on Instrumentation and Measurement, 70. https://doi.org/10.1109/TIM.2020.3043873
Zhang, K., Xu, Y., Liao, Z., Song, L., & Chen, P. (2021). A novel Fast Entrogram and its applications in rolling bearing fault diagnosis. Mechanical Systems and Signal Processing, 154. https://doi.org/10.1016/j.ymssp.2020.107582
Zhang, X., Zhao, B., & Lin, Y. (2021). Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review. In IEEE Access (Vol. 9, pp. 155598–155608). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3128669
Zhang, Y., Dora, S., Martinez-Garcia, M., & Bhattacharyaand, S. (2022). Machine Hearing for Industrial Acoustic Monitoring using Cochleagram and Spiking Neural Network. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2022-July, 1047–1051. https://doi.org/10.1109/AIM52237.2022.9863412
Zuo, L., Xu, F., Zhang, C., Xiahou, T., & Liu, Y. (2022). A multi-layer spiking neural network-based approach to bearing fault diagnosis. Reliability Engineering and System Safety, 225. https://doi.org/10.1016/j.ress.2022.108561
Section
Technical Research Papers
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.