An improved prognostics model with its application to the remaining useful life of turbofan engine

Abstract

Machinery prognostics play a crucial role in upgrading machinery service and optimizing machinery operation and maintenance schedule by forecasting the remaining useful life (RUL) of the monitored equipment, which has become more and more popular in recent years. The safety of aviation is one of the issues that people are most concerned about in the field of transportation, since it might cause disastrous loss of life and property once accident happened. The turbofan engine is an important part of the aircraft that provides thrust for plane. With aging, the turbofan engine becomes prone to failures. As a result, it would be worth studying prognostics in turbofan engine to improve the reliability of machinery and reduce unnecessary maintenance cost. Recently, a data-driven prognostics modeling strategy called the classification of predictions strategy (CPS) was proposed, in which the continuous signal and the discrete modes of an actual system come together to achieve RUL estimation. However, machine health states measured from classification rarely have just one potential situation, and this strategy cannot determine whether the fault occurs or not by a certain probability which comes closer to reality. Moreover, since there is no information and prior knowledge of prognostics application, it is hard to obtain the probability of various situations from raw measured data. Hence, based on previous work, this paper proposes an improved prognostics modeling method named the classification of predictions strategy with decision probability (CPS-DP), whose key innovations mainly include three parts: (1) decision probability process (DPP) where each step of multi-step prediction obeys geometric distribution and can judge whether the failure state occurs using the decision probability; (2) decision probability calculation (DPC) algorithm, which is first proposed by this paper and can calculate the values of decision probability without prior knowledge of prognostics application; and (3) withdrawal mechanism optimizer (WMO), which is specially designed to compensate for the shortcomings of DPP and further enhance the performance of the prognostics model. In brief, first, CPS is used to build a basic prognostics model to acquire RUL estimation results, in which the information applied to find the probability has been contained. Later, the mean of RUL estimation errors is figured from the results, which is further employed to calculate the probability using DPC algorithm. Then, CPS-DP can be achieved by means of integrating two parts: DPP and CPS. Furthermore, to further improve the performance, WMO is utilized to optimize CPS-DP with rolling back predictions. Ultimately, an enhanced prognostic model based on CPS-DP is set up through uniting CPS, DPP, and WMO. To validate the proposed method, experimental results on the turbofan engine in 2008 prognostics and health management competition are investigated.

Keywords

data-driven prognostics turbofan engine classification of predictions strategy remaining useful life geometric distribution

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References

Tsui

Chen

Zhou

, et al. Prognostics and health management: a review on data driven approaches. Math Probl Eng 2015; 2015: 1–17.

Camerini

Coppotelli

Bendisch

. Fault detection in operating helicopter drivetrain components based on support vector data description. Aerospace Sci Tech 2018; 73: 48–60.

Xiang

Qin

Zhu

, et al. Long short-term memory neural network with weight amplification and its application into gear remaining useful life prediction. Eng Appl Artif Intelligence 2020; 91: 103587.

Uckun

Goebel

Lucas

PJF

. Standardizing research methods for prognostics. In: International conference on prognostics and health management. Denver, CO, USA, 12 December 2008. IEEE, 2008, pp. 1–10.

Papa

Russo

Lamboglia

, et al. Health structure monitoring for the design of an innovative UAS fixed wing through inverse finite element method (iFEM). Aerospace Sci Tech 2017; 69: 439–448.

Verhagen

Curran

. Toward a methodology of requirements definition for prognostics and health management system to support aircraft predictive maintenance. Aerosp Sci Technol 2020; 102: 1–20.

Lee

Zhao

, et al. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications. Mech Syst Signal Process 2014; 42: 314–334.

Saxena

Celaya

Saha

, et al. Evaluating prognostics performance for algorithms incorporating uncertainty estimates. In: IEEE aerospace conference, Big Sky, MT, USA, 6–13 March 2010. IEEE, 2010, pp. 1–11.

Zhang

Chen

, et al. A light gradient boosting machine for remainning useful life estimation of aircraft engines. In: 21st international conference on intelligent transportation systems, Maui, HI, USA, 10 December 2018, ITSC, 2018, pp. 3562–3567.

10.

Azadeh

Asadzadeh

Salehi

, et al. Condition-based maintenance effectiveness for series-parallel power generation system-A combined Markovian simulation model. Reliability Eng Syst Saf 2015; 142: 357–368. DOI: 10.1016/j.ress.2015.04.009

11.

Cheng

Peng

, et al. RLCP: A reinforcement learning method for health stage division using change points. In: 2018 IEEE international conference on prognostics and health management (ICPHM), Seattle, WA, USA, 11–13 June 2018, 2018, pp. 1–6.

12.

Schwabacher

Goebel

. A Survey of Artificial Intelligence for Prognostics. In: Working Notes of 2007 AAAI Fall Symposium: AI for Prognostics. Arlington, Virginia, USA, 2007, pp. 108–115.

13.

Tinga

Loendersloot

. Aligning PHM, SHM and CBM by understanding the physical system failure behaviour. In: Second European conference of the prognostics and health management society, Nantes, France, July 2014, pp. 162–171.

14.

Pecht

Jie Gu

. Physics-of-failure-based prognostics for electronic products. Trans Inst Meas Control 2009; 31: 309–322.

15.

Heimes

. Recurrent neural networks for remaining useful life estimation. In: International conference on prognostics and health management, Denver, CO, USA, 6–9 October. 2008, IEEE, 2008, pp. 1–6.

16.

Che

Wang

, et al. Combining multiple deep learning algorithms for prognostic and health management of aircraft. Aerosp Sci Technol 2019; 94: 1–9.

17.

Heng

Zhang

Tan

ACC

, et al. Rotating machinery prognostics: State of the art, challenges and opportunities. Mech Syst Signal Process 2009; 23: 724–739.

18.

Pulido

Zamarreño

Merino

, et al. State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems. Eng Appl Artif Intelligence 2019; 79: 67–86.

19.

. Aircraft engine health prognostics based on logistic regression with penalization regularization and state-space-based degradation framework. Aerospace Sci Tech 2017; 68: 345–361.

20.

De Giorgi

Quarta

. Hybrid multigene genetic programming-artificial neural networks approach for dynamic performance prediction of an aeroengine. Aerosp Sci Technol 2020; 103: 1–12.

21.

Fink

Wang

Svensén

, et al. Potential, challenges and future directions for deep learning in prognostics and health management applications. Eng Appl Artif Intell 2020; 92: 1–15.

22.

Cai

, et al. Optimal dropbox deployment algorithm for data dissemination in vehicular networks. IEEE Trans Mobile Comput 2018; 17: 632–645.

23.

Khelif

Chebel-Morello

Malinowski

, et al. Direct remaining useful life estimation based on support vector regression. IEEE Trans IN Electron 2017; 64: 2276–2285.

24.

Zhang

Lim

Qin

, et al. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans Neural Netw Learn Syst 2017; 28: 2306–2318.

25.

Lim

Goh

Tan

. A time window neural network based framework for remaining useful life estimation. In: International joint conference on neural networks, Denver, CO, USA, 6–9 October 2016, IJCNN, pp. 1746–1753.

26.

Zheng

Ristovski

Farahat

, et al. Long short-term memory network for remaining useful life estimation. In: IEEE international conference on prognostics and health management (ICPHM), Dallas, TX, USA, 19–21 June 2017. IEEE, 2017, pp. 88–95.

27.

Ding

Sun

J-Q

. Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Eng Syst Saf 2018; 172: 1–11.

28.

Atamuradov

Medjaher

Dersin

, et al. Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. Int J Progn Health Manag 2017; 8: 1–31.

29.

Ramasso

Gouriveau

. Remaining useful life estimation by classification of predictions based on a neuro-fuzzy system and theory of belief functions. IEEE Trans Rel 2014; 63: 555–566.

30.

Ramasso

Rombaut

Zerhouni

. Joint prediction of continuous and discrete states in time-series based on belief functions. IEEE Trans Cybern 2013; 43: 37–50.

31.

Dragomir

Gourtveau

Zerhouni

, et al. Framework for a distributed and hybrid prognostic system. IFAC Proc Volumes 2007; 40: 431–436.

32.

Javed

Gouriveau

Zerhouni

. A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering. IEEE Trans Cybern 2015; 45: 2626–2639.

33.

Saxena

Goebel

Simon

, et al. Damage propagation modeling for aircraft engine run-to-failure simulation. In: International conference on prognostics and health management, Denver, CO, USA, October 2008. IEEE, 2008, pp. 1–9.

34.

Javed

Gouriveau

Zerhouni

. SW-ELM: a summation wavelet extreme learning machine algorithm with a priori parameter initialization. Neurocomputing 2014; 123: 299–307.

35.

Javed

Gouriveau

Zemouri

, et al. Features selection procedure for prognostics: an approach based on predictability. IFAC Proc Volumes 2012; 45: 25–30.

36.

Javed

Gouriveau

Zerhouni

. Novel failure prognostics approach with dynamic thresholds for machine degradation. In: IECON 2013 - 39th annual conference of the IEEE industrial electronics society, Vienna, Austria, 10–13 Novomber 2013. IEEE, pp. 4404–4409.

37.

Huang

G-B

Zhu

Q-Y

Siew

C-K

. Extreme learning machine: Theory and applications. Neurocomputing 2006; 70: 489–501.

38.

Nguyen

Widrow

. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: IJCNN international joint conference on neural networks, San Diego, CA, USA, 17–21 June 1990. IEEE, pp. 21–26.

39.

Oussar

Dreyfus

. Initialization by selection for wavelet network training. Neurocomputing 2000; 34: 131–143.

40.

Gao

Luo

, et al. IMA health state evaluation using deep feature learning with quantum neural network. Eng Appl Artif Intelligence 2018; 76: 119–129.

41.

Chiu

. Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 1994; 2: 267–278.

42.

R-P

Mukaidono

. A maximum-entropy approach to fuzzy clustering. In: Proceedings of 1995 IEEE international conference on fuzzy systems. The international joint conference of the fourth IEEE international conference on fuzzy systems and the second international fuzzy engineering symposium, Yokohama, Japan, 20–24 March 1995. IEEE, pp. 2227–2232.

43.

Doan

Liong

Karunasinghe

DSK

. Derivation of effective and efficient data set with subtractive clustering method and genetic algorithm. J Hydroinformatics 2005; 7: 219–233.

44.

Bataineh

Naji

Saqer

. A comparison study between various fuzzy clustering algorithms. Jordan J Mech Ind Eng 2011; 5: 335–343.

45.

Gouriveau

Zerhouni

. Connexionist-systems-based long term prediction approaches for prognostics. IEEE Trans Rel 2012; 61: 909–920.

46.

Casella

Berger

. Statistical Inference. 2nd ed.. Australia; Pacific Grove, CA: Thomson Learning, 2002.

47.

Ramasso

Gouriveau

. Prognostics in switching systems: Evidential Markovian classification of real-time neuro-fuzzy predictions. In: Prognostics and system health management conference, Macao, Macao, 12–14 January. 2010. IEEE, 2010, pp. 1–10.

48.

Sateesh Babu

Zhao

X-L

. Deep convolutional neural network based regression approach for estimation of remaining useful life. In: Database Systems for Advanced Applications. Cham: Springer International Publishing, pp. 214–228.

49.

Louen

Ding

Kandler

. A new framework for remaining useful life estimation using support vector machine classifier. In: Conference on control and fault-tolerant systems (SysTol), Nice, France, 9–11 October 2013

IEEE, 2013, pp. 228–233.

50.

Peng

Wang

, et al. A modified echo state network based remaining useful life estimation approach. In: IEEE conference on prognostics and health management, Denver, CO, USA, 18–21 June 2012. IEEE, 2012, pp. 1–7.

51.

Cheng

, et al. Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Trans 2020; 97: 241–250.

52.

Ramasso

. Investigating computational geometry for failure prognostics. Int J Progn Health Manag 2014; 5: 1–18.

53.

Saxena

Celaya

Balaban

, et al. Metrics for Evaluating Performance of Prognostic Techniques. USA: Denver, CO, 2008, pp. 1–17.