A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Tao, Tao; Zio, Enrico; Zhao, Wei

doi:10.1016/j.ress.2018.04.027

Cited by 60 publications

(28 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the situation of variable operation conditions, Wu et al [11], Jiang et al [12], Rigamonti et al [13], and Yan et al [14] only predicted the RUL under variable operating conditions by putting forward innovative characteristic parameters or health indicators, but did not propose the prediction method suitable for variable operating conditions. Although Sameer et al [15] and Tao et al [16] proposed the prediction methods suitable for the variable operating condition by using the historical data and Monte Carlo simulation, the complexity of the calculation process limited the wide use of the methods. Furthermore, the similarity trajectory method (STM) [17][18][19][20][21], which is simpler to calculate, was widely used to predict the RUL of the mechanical system under variable operating conditions [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching

et al. 2020

View full text Add to dashboard Cite

The life prediction is crucial to guarantee the reliability and safety of the mechanical system. The current prediction methods predict the life only based on the historical usage pattern of the mechanical system, and do not consider the mission profile of the future working process. To realize the life prediction with considering the switching of mission profile, which is composed of different operating conditions, this paper proposes a new prediction scheme on the base of the similarity trajectory method (STM). Two main improvements are employed. First, the reference degradation models are constructed according to the predicted trend of each constant operating condition obtained by the relevance vector machine (RVM). Secondly, the life under specific mission profile is calculated through weighted aggregating the life of each constant operating condition. The proposed method is validated by a turbofan engine simulation data. The results show that the proposed method achieves an excellent predicted result, in which the predicted result is close to the actual result. In addition, the proposed method can deal with the problem of mission profile switching.

show abstract

Section: Introductionmentioning

confidence: 99%

A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The inherent drawbacks of the degradation-modeling based methods derive from two strong premises: (1) the degradation process of equipment's performance should follow a certain statistical model, such as the continuous-time Markov chain, the hidden Markov model, the hidden semi-Markov model or the Wiener process, .etc. ; (2) the statistical property of the degradation model, for example the transition probability matrix for Markov-based models, should be a priori known or estimated [20]. However, for practical instances, theoretical statistical models such as Markov chain are very hard to be verified, and the transition probability matrix is often hard to estimate or inaccessible.…”

Section: Introductionmentioning

confidence: 99%

“…Fumeo et al [31] developed an online SVR model to predict the RUL of bearings by optimizing the tradeoff between accuracy and computational efficiency. Tao et al [20] considered the dynamic multi-state operating conditions, and trained the corresponding SVR model under different operating conditions, then the RUL of the on-service equipment can be predicted under time varying operating conditions. Some other SVR-based methods were developed in [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction based on a modified SVR method with health stage division, cluster sampling and similarity matching

Yan

Zhong

Cheng

et al. 2020

Preprint

View full text Add to dashboard Cite

Remaining useful life (RUL) prediction is an advanced methodology of prognostics and health management (PHM), and is in advantage of life cycles management of equipment and maintenance cost reduction. Among the data driven methods, support vector regression (SVR) is one of the most suitable methods when there are limited failure history data for analysis. However, many uncertain factors such as individual variation and time varying operating conditions, will lead that the failure time of all equipment statistically dispersive, and with the increase of sample set, such dispersity may inevitably increase and further reflects in the model training. In consequent, the dispersity may cause two drawbacks. On the one hand, the linearity of SVR model will increase with the increase of sample set, and overfitting or underfitting tends to occur. And on the other hand, a single model only performs well in its generalization and robustness, but may lost its effectiveness that it may fail to work well for a new on-service equipment. In order to deal with the two drawbacks, this paper proposes a modified SVR method with health stage division, cluster sampling and similarity matching. Through health stage division and cluster sampling, each state of the whole degradation process can obtain the optimal parameters, then the irrelevant linearity can be reduced. In addition, since that similar input results similar output, the optimal parameters of the most similar testing sample are also suitable for the on-service equipment, and through similarity matching the most similar testing sample can be obtained, thus the drawback of a single model can be avoided. Finally, the effectiveness of the proposed method is verified systematically by a simulated dataset of fatigue crack growth and a real-world degradation dataset of GaAs-based lasers.

show abstract

“…The literature abounds with many research papers on MSSs [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. A significantly large proportion of these papers are devoted to coherent MSSs, and, in particular, to their backbone class of k-out-of-n MSSs [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Switching-Algebraic Analysis of Multi-State System Reliability

Rushdi

AL-Amoudi

2019

JERR

View full text Add to dashboard Cite

Multi-State systems are systems whose outputs are multi-valued (due to multiple levels of capacity or performance) and (possibly) whose inputs are also multi-valued (due to multiple performance levels or multiple modes of failure). These systems are a generalization of binary or dichotomous systems that have binary or two-valued outputs and inputs. The multi-state reliability model generalizes and adapts many of the concepts and techniques of the binary reliability model, and naturally ends up with sophisticated concepts and techniques of its own. This paper explores the possibility of simply analyzing a multi-state system by reformulating or encoding its inputs in terms of binary inputs and evaluating each of its multiple output levels as an individual binary output of these alternative inputs. This means that we dispense with multiple-valued logic in the analysis of a multi-state system, since this system is now analyzed solely via switching algebra (two-valued Boolean algebra). The wealth of tools and techniques of switching algebra are now used (without any modification or adaptation) in the analysis of the multi-state system (at the cost of an expanded input domain). The paper makes its point though the analysis of a standard commodity-supply system, whose multi-valued inputs are expressed in terms of physically-meaningfully binary inputs. The analysis is made possible through the use of advanced techniques for deriving probability–ready expressions together with the employment of large-size Karnaugh maps and utilization of multiplication tables, symmetric switching functions, and Boolean quotients. Though the system studied involves twelve binary input variables, its manual analysis is completed successfully herein, yielding results that exactly agree with those obtained earlier via automated methods, and are possibly less prone to the notorious effects of round-off errors.

show abstract

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Cited by 60 publications

References 36 publications

A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching

A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching

Remaining useful life prediction based on a modified SVR method with health stage division, cluster sampling and similarity matching

Switching-Algebraic Analysis of Multi-State System Reliability

Contact Info

Product

Resources

About