RESEARCH PAPER
Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings
1
Center for Risk Analysis and Environmental Modeling – CEERMA Department of Production Engineering Universidade Federal de Pernambuco – UFPE Av. Prof. Moraes Rego 1235 – University City Recife – PE – Brazil – 50670-901
2
Department of Production Engineering Universidade Federal de Pernambuco – UFPE Av. Prof. Moraes Rego 1235 – University City Recife – PE – Brazil – 50670-901
3
marcio@ceerma.org
4
Center for Risk Analysis and Environmental Modeling – CEERMA
Publication date: 2019-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(4):610–618
KEYWORDS
big datavibration signalbearingsremaining useful lifeempirical mode decompositionwaveletstransformsupport vector machineparticle swarm optimization
ABSTRACT
The useful life time of equipment is an important variable related to system prognosis, and its accurate estimation leads to several
competitive advantage in industry. In this paper, Remaining Useful Lifetime (RUL) prediction is estimated by Particle Swarm
optimized Support Vector Machines (PSO+SVM) considering two possible pre-processing techniques to improve input quality: Empirical Mode Decomposition (EMD) and Wavelet Transforms (WT). Here, EMD and WT coupled with SVM are used to
predict RUL of bearing from the IEEE PHM Challenge 2012 big dataset. Specifically, two cases were analyzed: considering the
complete vibration dataset and considering truncated vibration dataset. Finally, predictions provided from models applying both
pre-processing techniques are compared against results obtained from PSO+SVM without any pre-processing approach. As conclusion, EMD+SVM presented more accurate predictions and outperformed the other models
REFERENCES (60)
1.
Allen J. Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE Transactions on Acoustics, Speech and Signal Processing 1977; 25(3): 235-238, https://doi.org/10.1109/TASSP.....
2.
Ambhore N, Kamble D, Chinchanikar S, Wayal. V. Tool condition monitoring system: A review. Materials Today: Proceedings 2015; 2(4-5):3419-3428, https://doi.org/10.1016/j.matp....
3.
Bakhoday-Paskyabi M, Valinejad A, Azodi H. D. Numerical solution of regularised long ocean waves using periodised scaling functions. Pramana 2019; 92(5): 71, https://doi.org/10.1007/s12043....
4.
Boškoski P, Gasperin M, Petelin D, Juricic D. Bearing fault prognostics using Rényi entropy based features and Gaussian process models, Mechanical Systems and Signal Processing 2015; 52-53: 327-337, https://doi.org/10.1016/j.ymss....
5.
Bousdekis A, Magoutas B, Apostolou D. Mentzas G.Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. Journal of Intelligent Manufacturing 2015; 29(6) 1303-1316, https://doi.org/10.1007/s10845....
6.
Bratton D. Kennedy J. Defining a Standard for Particle Swarm Optimization. 2007 IEEE Swarm Intelligence Symposium 2007; 120-127, https://doi.org/10.1109/SIS.20....
7.
Chang L, Chung Y, Lin C, Chen J, Kuo C, Chen S. Mechanical Vibration Fault Detection for Turbine Generator Using Frequency Spectral Data and Machine Learning Model : Feasibility Study of Big Data Analysis. Sensors and Materials 2018; 30(4): 821-832, https://doi.org/10.18494/SAM.2....
8.
Chen J, Li Z, Pan J, Chen G, Zi Y, Yuan J, Chen B, He Z. Wavelet transform based on inner product in fault diagnosis of rotating machinery:A review. Mechanical Systems and Signal Processing 2016; 70-71: 1-35, https://doi.org/10.1016/j.ymss....
9.
Chen X, Ding M, Wang T, Ding M, Wang J, Chen J, Yan J. Analysis and prediction on the cutting process of constrained damping boring bars based on PSO-BP neural network model. Journal of Vibroengineering 2017; 19(2): 878-893, https://doi.org/10.21595/jve.2....
10.
Chun-Lin L. A Tutorial of the Wavelet Transform. Taipei: National Taiwan University, 2010.
11.
Daubechies I. Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics 1993; 666-669, https://doi.org/10.1137/1.9781....
12.
Droguett E, Lins I, Moura M, Zio E, Jacinto C. Variable selection and uncertainty analysis of scale growth rate under pre-salt oil wells conditions using support vector regression. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2014; 229(4): 319-326, https://doi.org/10.1177/174800....
13.
Eftekhar A, Toumazou C, Drakakis E. M. Empirical Mode Decomposition: Real-Time Implementation and Applications. Journal of Signal Processing Systems 2013; 73(1): 43-58, https://doi.org/10.1007/s11265....
14.
El-Thalji I, Jantunen E. A summary of fault modelling and predictive health monitoring of rolling element bearings. Mechanical Systems and Signal Processing 2015; 60: 252-272, https://doi.org/10.1016/j.ymss....
15.
Fumeo E, Oneto L, Anguita D. Condition based maintenance in railway transportation systems based on big data streaming analysis. Procedia Computer Science 2015; 53: 437-446, https://doi.org/10.1016/j.proc....
16.
García Nieto P. J, García-Gonzalo E, Sánchez Lasheras F, Juezc de Cos. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering and System Safety 2015; 138: 219-231, https://doi.org/10.1016/j.ress....
17.
Genovese L. Videau V, Ospici M, Deutsch T, Goedecker S, Méhaut J. Daubechies wavelets for high performance electronic structure calculations: The BigDFT project. Comptes Rendus Mécanique 2011; 339: 149-164, https://doi.org/10.1016/j.crme....
18.
Guohua G, Yu Z, Guanghuang D, Yongzhong Z. Intelligent Fault Identification Based On Wavelet Packet Energy Analysis and SVM. International Conference on Control, Automation, Robotics and Vision 2006; 1(3): 1-5, https://doi.org/10.1109/ICARCV....
19.
Huang B, Jin C, Di Y, Lee J. Review of Data-Driven Prognostics and Health Management Techniques: Lessions Learned From Phm Data Challenge Competitions. Machine Failure Prevention Technology 2017.
20.
Huang N. E, Shen Z, Long S, Wu M, Shih H, Zheng Q, Yen N, Tung C, Liu H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 1998; 903-995, https://doi.org/10.1098/rspa.1....
21.
Huang N. E, Wu Z. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics 2008; 46(2): 1-23, https://doi.org/10.1029/2007RG....
22.
Huang S, Chang J, Huang Q, Chen Y. Monthly streamflow prediction using modified EMD-based support vector machine. Journal of Hydrology 2014; 511: 764-775, https://doi.org/10.1016/j.jhyd....
23.
Kumar P, Foufoula-Georgiou E. Wavelet analysis for geophysical applications. Reviews of Geophysics 1997; 35(4), https://doi.org/10.1029/97RG00....
24.
Lee J. J, Yun C. B. Damage diagnosis of steel girder bridges using ambient vibration data. Engineering Structures 2006, https://doi.org/10.1016/j.engs....
25.
Liao L, Köttig F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability 2014, https://doi.org/10.1109/TR.201....
26.
Lins I, Araujo M, Moura M, Silva M, Droguett E. Prediction of sea surface temperature in the tropical Atlantic by support vector machines. Computational Statistics and Data Analysis 2013; 61: 187-198, https://doi.org/10.1016/j.csda....
27.
Lins I, Moura M, Droguett E. Failure prediction of oil wells by support vector regression with variable selection, hyperparameter tuning and uncertainty analysis. Chemical Engineering Transactions 2013; 33: 817-822.
28.
Liu Z, Wang L, Zhang Y, Chen C. A SVM controller for the stable walking of biped robots based on small sample sizes. Applied Soft Computing 2016; 38: 738-753, https://doi.org/10.1016/j.asoc....
29.
Lybeck N, Marble S, Morton B. Validating Prognostic Algorithms: A Case Study Using Comprehensive Bearing Fault Data, Aerospace Conference 2007; 1-9, https://doi.org/10.1109/AERO.2....
30.
Mallat S. A Wavelet Tour of Signal Processing. A Wavelet Tour of Signal Processing 2009.
31.
Mallat S. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989, https://doi.org/10.1109/34.192....
32.
Mao W, He J, Tang J, Li Y. et al. Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Advances in Mechanical Engineering 2018; 10(12), https://doi.org/10.1177/168781....
33.
McKee K. K, Forbes G, Mazhar I, Entwistle R, Hodkiewicz M, Howard I. A vibration cavitation sensitivity parameter based on spectral and statistical methods. Expert Systems with Applications 2015; 42(1): 67-78, https://doi.org/10.1016/j.eswa....
34.
Morlet J, Arens G, Fourgeau E, Giardet D. Wave propagation and sampling theory-Part II: Sampling theory and complex waves. Geophysics 1982; 47(2): 222-236, https://doi.org/10.1190/1.1441....
35.
Nectoux P, Gouriveau R, Medjaher K, Ramasso E, Chebel-Morello B, Zerhouni N, Varnier C. PRONOSTIA : An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management 2012; 1-8.
36.
Nikolaou N. G, Antoniadis I. A. Rolling element bearing fault diagnosis using wavelet packets NDT & E International 2002; 35(3): 197-205, https://doi.org/10.1016/S0963-....
37.
Patil M. A, Tagade P, Hariharan K, Kolake S, Song T, Yeo T, Doob S. A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation. Applied Energy 2015; 159: 285-297, https://doi.org/10.1016/j.apen....
38.
Prabhakar S, Mohanty A. R, Sekhar A. S. Application of discrete wavelet transform for detection of ball bearing race faults. Tribology International 2002, https://doi.org/10.1016/S0301-....
39.
Rafiee J, Rafiee M. A, Tse P. W. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 2010, https://doi.org/10.1016/j.eswa....
40.
Rai A, Upadhyay S. H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International 2016; 289-306, https://doi.org/10.1016/j.trib....
41.
Randall R. B, Antoni J. Rolling element bearing diagnostics-A tutorial. Mechanical Systems and Signal Processing 2011; 25(2): 485-520, https://doi.org/10.1016/j.ymss....
42.
Ren L, Sun Y, Cui J, Zhang, L. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems 2018; 48: 71-77, https://doi.org/10.1016/j.jmsy....
43.
Rohlmann A, Schmidt H, Gast U, Kutzner I, Damm P, Bergmann G. In vivo measurements of the effect of whole body vibration on spinal loads. European Spine Journal 2014, https://doi.org/10.1007/s00586....
44.
Saha B, Goebel K, Christophersen J. Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Contro 2009; 31(3-4): 293-308, https://doi.org/10.1177/014233....
45.
Si X. S, Wang W, Hu C, Zhou D. Remaining useful life estimation - A review on the statistical data driven approaches. European Journal of Operational Research 2011; 213(1): 1-14, https://doi.org/10.1016/j.ejor....
46.
Sikorska J. Z, Hodkiewicz M, Ma L. Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing 2011; 25: 1803-1836, https://doi.org/10.1016/j.ymss....
47.
Soualhi A, Medjaher K. Zerhouni N. Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement 2015; 64(1): 52-62, https://doi.org/10.1109/TIM.20....
48.
Souto Maior C. B, Moura M, Lins L. Droguett, Diniz H. E. Remaining Useful Life Estimation by Empirical Mode Decomposition and Support Vector Machine. IEEE Latin America Transactions 2016; 14(11): 4603-4610, https://doi.org/10.1109/TLA.20....
49.
Standardization. ISO 10816-7: Mechanical vibration - Evaluation of machine vibration by measurements on non-rotating parts. Part 7: Rotodynamic pumps for industrial applications, including measurements on rotating shafts. Switzerland: ISO. 2009.
50.
Sutharssan T, Stoyanov S. Bailey C, Rosunally Y. Prognostics and health monitoring of high power LED. Micromachines 2012; 3: 78-100, https://doi.org/10.3390/mi3010....
51.
Sutrisno E, Oh H, Vasan A, Pecht M. Estimation of remaining useful life of ball bearings using data driven methodologies. 2012 IEEE Conference on Prognostics and Health Management 2012; 2: 1-7, https://doi.org/10.1109/ICPHM.....
52.
Tandon N, Choudhury A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International 1999; 32(8): 469-480, https://doi.org/10.1016/S0301-....
53.
Torres M. E, Colominas M, Schlotthauer G, Flandrin P. A complete ensemble empirical mode decomposition with adaptive noise. IEEE International Conference on Acoustics, Speech and Signal Processing 2011, https://doi.org/10.1109/ICASSP....
54.
Vachtsevanos G, Lewis F, Roemer M, Hess A. Biqing Wu t al. Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Intelligent Fault Diagnosis and Prognosis for Engineering Systems 2007, https://doi.org/10.1002/978047....
55.
Vapnik V. The Nature of Statistical Learning Theory. New York: Springer, 2000, https://doi.org/10.1007/978-1-....
56.
Wang L. Support Vector Machines : Theory and Applications. 2005, https://doi.org/10.1007/b95439.
57.
Widodo A, Yang B. S. Machine health prognostics using survival probability and support vector machine. Expert Systems with Applications 2011; 38(7): 8430-8437, https://doi.org/10.1016/j.eswa....
58.
Wright S. J. Primal-Dual Interior-Point Methods. Primal-Dual Interior-Point Methods 2011.
59.
Wu Z, Huang N. E. Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method. Advances in Adaptive Data Anal 2009; 1-41, https://doi.org/10.1142/S17935....
60.
Yan R, Gao R, X. Chen X. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 2014, 96(Part A): 1-15, https://doi.org/10.1016/j.sigp....
CITATIONS (21):
1.
Automatic drowsiness detection for safety-critical operations using ensemble models and EEG signals
Plínio Ramos, Caio Maior, Márcio Moura, Isis Lins
Process Safety and Environmental Protection
Plínio Ramos, Caio Maior, Márcio Moura, Isis Lins
Process Safety and Environmental Protection
2.
A comparison between computer vision- and deep learning-based models for automated concrete crack detection
da Sales, Chagas das, Maior Souto, Negreiros Cláudia, Lins Didier
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
da Sales, Chagas das, Maior Souto, Negreiros Cláudia, Lins Didier
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
3.
Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review
Zhibin Zhao, Jingyao Wu, Tianfu Li, Chuang Sun, Ruqiang Yan, Xuefeng Chen
Chinese Journal of Mechanical Engineering
Zhibin Zhao, Jingyao Wu, Tianfu Li, Chuang Sun, Ruqiang Yan, Xuefeng Chen
Chinese Journal of Mechanical Engineering
4.
Combining BERT with numerical variables to classify injury leave based on accident description
Plínio Ramos, July Macedo, Caio Maior, Márcio Moura, Isis Lins
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Plínio Ramos, July Macedo, Caio Maior, Márcio Moura, Isis Lins
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
5.
Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases
Caio Maior, João Santana, Isis Lins, Márcio Moura, Khanh Le
PLOS ONE
Caio Maior, João Santana, Isis Lins, Márcio Moura, Khanh Le
PLOS ONE
6.
Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm
Xiao Li, Songyang An, Yuanyuan Shi, Yizhe Huang
Machines
Xiao Li, Songyang An, Yuanyuan Shi, Yizhe Huang
Machines
7.
Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings
Łukasz Witanowski, Łukasz Breńkacz, Natalia Szewczuk-Krypa, Marta Dorosińska-Komor, Bartosz Puchalski
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Łukasz Witanowski, Łukasz Breńkacz, Natalia Szewczuk-Krypa, Marta Dorosińska-Komor, Bartosz Puchalski
Eksploatacja i Niezawodnosc - Maintenance and Reliability
8.
Technology development and commercial applications of industrial fault diagnosis system: a review
Chengze Liu, Andrzej Cichon, Grzegorz Królczyk, Zhixiong Li
The International Journal of Advanced Manufacturing Technology
Chengze Liu, Andrzej Cichon, Grzegorz Królczyk, Zhixiong Li
The International Journal of Advanced Manufacturing Technology
9.
Identifying low-quality patterns in accident reports from textual data
July Macedo, Plinio Ramos, Caio Maior, Márcio Moura, Isis Lins, Romulo Vilela
International Journal of Occupational Safety and Ergonomics
July Macedo, Plinio Ramos, Caio Maior, Márcio Moura, Isis Lins, Romulo Vilela
International Journal of Occupational Safety and Ergonomics
10.
Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020
Mohd Tahir, Abd Mahamad, Sharifah Saon, Saravanaraj Sathasivam, Hussein Ameen
Mohd Tahir, Abd Mahamad, Sharifah Saon, Saravanaraj Sathasivam, Hussein Ameen
11.
A Survey on Data-Driven Predictive Maintenance for the Railway Industry
Narjes Davari, Bruno Veloso, Gustavo Costa, Pedro Pereira, Rita Ribeiro, João Gama
Sensors
Narjes Davari, Bruno Veloso, Gustavo Costa, Pedro Pereira, Rita Ribeiro, João Gama
Sensors
12.
Bayesian prior distribution based on generic data and experts’ opinion: A case study in the O&G industry
Caio Maior, July Macêdo, Isis Lins, Márcio Moura, Rafael Azevedo, João Santana, Silva da, Marcus Magalhães
Journal of Petroleum Science and Engineering
Caio Maior, July Macêdo, Isis Lins, Márcio Moura, Rafael Azevedo, João Santana, Silva da, Marcus Magalhães
Journal of Petroleum Science and Engineering
13.
Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model
Gao Zhiyong, Li Jiwu, Wang Rongxi
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Gao Zhiyong, Li Jiwu, Wang Rongxi
Eksploatacja i Niezawodnosc - Maintenance and Reliability
14.
Real-time classification for autonomous drowsiness detection using eye aspect ratio
Caio Maior, Márcio Moura, João Santana, Isis Lins
Expert Systems with Applications
Caio Maior, Márcio Moura, João Santana, Isis Lins
Expert Systems with Applications
15.
Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes
Shuang Sun, Krzysztof Przystupa, Ming Wei, Han Yu, Zhiwei Ye, Orest Kochan
Eksploatacja i Niezawodność – Maintenance and Reliability
Shuang Sun, Krzysztof Przystupa, Ming Wei, Han Yu, Zhiwei Ye, Orest Kochan
Eksploatacja i Niezawodność – Maintenance and Reliability
16.
Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning
Caio Maior, Lavínia Araújo, Isis Lins, Márcio Moura, Enrique Droguett
IEEE Access
Caio Maior, Lavínia Araújo, Isis Lins, Márcio Moura, Enrique Droguett
IEEE Access
17.
Technology selection and ranking: Literature review and current applications in oil & gas industry
Lavínia Araújo, Caio Maior, Isis Lins, Márcio Moura
Geoenergy Science and Engineering
Lavínia Araújo, Caio Maior, Isis Lins, Márcio Moura
Geoenergy Science and Engineering
18.
Developing and applying OEGOA-VMD algorithm for feature extraction for early fault detection in cryogenic rolling bearing
Bin Wang, Yanbao Guo, Zheng Zhang, Deguo Wang, Junqiang Wang, Yuansheng Zhang
Measurement
Bin Wang, Yanbao Guo, Zheng Zhang, Deguo Wang, Junqiang Wang, Yuansheng Zhang
Measurement
19.
Using experts’ opinion for Bayesian prior reliability distribution of on-demand equipment: A case study of a novel sliding sleeve valve for open-hole wells
July Macedo, Caio Maior, Isis Lins, Rafael Azevedo, Márcio Moura, Silva da, Nóbrega Silva, Guilherme Vitale, Ricardo Vasques
Reliability Engineering & System Safety
July Macedo, Caio Maior, Isis Lins, Rafael Azevedo, Márcio Moura, Silva da, Nóbrega Silva, Guilherme Vitale, Ricardo Vasques
Reliability Engineering & System Safety
20.
Fatigue life assessment for incremental innovation of novel O&G equipment using a calibrated finite element and Monte Carlo samplings
Caio Maior, Eduardo Menezes, Márcio Moura, Isis Lins, Silva da, Marcus Magalhães, Guilherme Ribeiro, Ricardo Vasques
Journal of the Brazilian Society of Mechanical Sciences and Engineering
Caio Maior, Eduardo Menezes, Márcio Moura, Isis Lins, Silva da, Marcus Magalhães, Guilherme Ribeiro, Ricardo Vasques
Journal of the Brazilian Society of Mechanical Sciences and Engineering
21.
Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems
Zhang Yiming, Xie Fang, Olena Hordiichuk-Bublivska, Halyna Beshley, Mykola Beshley
Energies
Zhang Yiming, Xie Fang, Olena Hordiichuk-Bublivska, Halyna Beshley, Mykola Beshley
Energies
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.