The Prognostic and Health Management (PHM) becomes a research topic in its own right and tends to be more and more visible within the scientific community such as in Nasa Society, which has provided datasets for experiments. The purpose of this paper is to evaluate the performance of a data-driven prognostic technique used for predicting Remaining Useful Life (RUL). The methodological support of the proposed approach integrates all data-driven prognostic sequential steps merged in offline and online part. To design the predictive degradation model on the offline part, the Relevance Vector Machine (RVM) algorithm was applied. On the online part, prediction of the RUL is based on the Similarity-Based Interpolation (SBI) algorithm. The different steps of the methodology are described and their implementation undertaken through a case study involving the degradation dataset of turbofan engines from the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). Finally, results are compared with other techniques applied on the same dataset.
Aye S, Heyns P. An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. 2017;485–98.
2.
Babu G, Zhao P, Li X. Deep convolutional neural network-based regression approach for estimation of remaining useful life. n International Conference on Database Systems for Advanced Applications Springer. 2016;
3.
Benkedjouh T, Medjaher K, Zerhouni N, Rechak S. Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence. 2013;(7):1751–60.
4.
Benkedjouh T, Medjaher K, Zerhouni N, Rechak S. Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing. 2015;213–23.
5.
Byington C, Watson M, Roemer M, Galie T. Prognostic enhancements to gas turbine diagnostic systems. 2001;
6.
Maio D, Tsui F, Zio KL, E. Combining relevance vector machines and exponential regression for bearing residual life estimation. 2012;405–27.
7.
Elsheikh A, Yacout S, Ouali M. Bidirectional handshaking LSTM for remaining useful life prediction. Neurocomputing journal. 2019;148–56.
8.
Ghorbani S, Salahshoor K. Estimating Remaining Useful Life of Turbofan Engine Using Data-Level Fusion and Feature-Level Fusion. Journal of Failure Analysis and Prevention. 2020;(1):323–32.
9.
Goebel K, Saha B, Saxena A. A comparison of three data-driven techniques for prognostics. 2008;119–31.
10.
Huang H, Wang HK, Li YF, Zhang L, Liu Z. Support vector machine-based estimation of remaining useful life: current research status and future trends. Journal of Mechanical Science and Technology. 2015;(1):151–63.
11.
Hu J, Tse P. A relevance vector machine-based approach with application to oil sand pump prognostics. Sensors. 2013;12663–86.
12.
Jayasinghe L, Samarasinghe T, Yuen C, Ge S. Temporal convolutional memory networks for remaining useful life estimation of industrial machinery. 2018;
13.
Khelif R, Malinowski S, Chebel-Morello B, Zerhouni N. RUL prediction based on a new similarity-instance based approach. 2014;2463–8.
14.
Khelif R, Chebel-Morello B, Malinowski S, Laajili E, Fnaiech F, Zerhouni N. Direct Remaining Useful Life Estimation Based on Support Vector Regression. IEEE Transactions Industrial Electronics. 2017;2276–85.
15.
Lebold M, Thurston M. Open standards for condition-based maintenance and prognostic systems. 2001;
16.
Li H, Zhao W, Zhang Y, Zio E. Remaining useful life prediction using multi-scale deep convolutional neural network. Applied Soft Computing. 2020;106–13.
17.
Li X, Ding Q, Sun J. Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety. 2018;1–11.
18.
Lim P, Goh C, Tan K. A time window neural network-based framework for Remaining Useful Life estimation. 2016;1746–53.
19.
Malhotra P, Tv V, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G. Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder, 1st. ACM SIGKDD Workshop on Machine Learning for PHM. 2016;
20.
Ordóñez C, Lasheras FS, Roca-Pardiñas J, De C, Juez F. A hybrid ARIMA-SVM model for the study of the remaining useful life of aircraft engines. Journal of Computational and Applied Mathematics. 2019;184–91.
21.
Rabiei E, Droguett E, Modarres M. A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks. Advances in Mechanical Engineering. 2016;1–19.
22.
Saha B, Goebel K. Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. 2008;
23.
Saha S, Saha B, Saxena A, Goebel K. Distributed prognostic health management with Gaussian process regression. 2010;1–8.
24.
Saidi L, Ali B, Fnaiech J, F. Application of higher order spectral features and support vector machines for bearing faults classification. ISA transactions. 2015;193–206.
25.
Saxena A, Goebel K, Simon D, Eklund N. Damage propagation modeling for aircraft engine run-to-failure simulation. 2008;1–9.
26.
Saxena A, Goebel K. Turbofan Engine Degradation Simulation Data Set. NASA Ames Prognostics Data Repository. 2008;
27.
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. 2014;(1):52–62.
28.
Trappey C, Trappey A, Ma L, Tsao W. Data driven modeling for power transformer lifespan evaluation. Journal of Systems Science and Systems Engineering. 2014;(1):80–93.
29.
Trinh HC, Kwon Y. An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction. Machines. 2018;(3):35.
30.
Tzikas D, Wei L, Likas A, Yang Y, Galatsanos N. A tutorial on relevance vector machines for regression and classification with application. EURASIP News Letter. 2006;(2):4.
31.
Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems. 1997;281–7.
32.
Voisin A, Medina-Oliva G, Monnin M, Léger J, Iung B. Fleet-wide diagnostic and prognostic assessment. In Annual Conference of the Prognostics and Health Management Society, New Orleans, USA. 2013;
33.
Wang P, Youn B, Hu C. Generic probabilistic framework for structural health prognostics and uncertainty management. 2012;622–37.
34.
Wang P, Tamilselvan P, Twomey J, Youn B. Prognosis-informed wind farm operation and maintenance for concurrent economic and environmental benefits. International Journal of Precision Engineering and Manufacturing. 2013;(6):1049–56.
35.
Wang T, Yu J, Siegel D, Lee J. A similarity-based prognostics approach for remaining useful life estimation of engineered systems. 2008;1–6.
36.
Williams CK, Rasmussen C. Gaussian processes for machine learning. 2006;
37.
Xi Z, Jing R, Wang P, Hu C. A copula-based sampling method for data-driven prognostics. Journal of Reliability Engineering and System Safety. 2014;72–82.
38.
Zheng S, Ristovski K, Farahat A, Gupta C. Long short-term memory network for remaining useful life estimation. 2017;88–95.
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