Application of neural networks in forecasting engine systems reliability

Xu, Kai; Xie, Min; Tang, Loon Ching; Ho, Sze Ling

doi:10.1016/s1568-4946(02)00059-5

Cited by 221 publications

(134 citation statements)

References 18 publications

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“…The benchmark dataset was first introduced by Xu et al [58] and has been used to evaluate the performance of numerous algorithms ( [13], [61], etc.). The benchmark study by Xu et al [58] includes two datasets for reliability evaluations with neural networks: the first dataset consists of an ordered series of time-to-failure (TTF) values of a turbocharger system and corresponding computed reliability values; the second dataset is made of a series of miles between failures.…”

Section: Dataset and Previous Studiesmentioning

confidence: 99%

“…The benchmark study by Xu et al [58] includes two datasets for reliability evaluations with neural networks: the first dataset consists of an ordered series of time-to-failure (TTF) values of a turbocharger system and corresponding computed reliability values; the second dataset is made of a series of miles between failures. The first time series shows a gradual development, whereas the second dataset is determined by very short fluctuating cycles.…”

Section: Dataset and Previous Studiesmentioning

confidence: 99%

“…In the first approach, only the series of computed reliability values is predicted ( [58], [11], [40], [13], [24], [34]). Xu et al [58] also included the time-to-failure values as an explanatory variable but when they did so, the results became worse and this approach was not further pursued. In the second approach, Zio et al [61], Moura et al [38] and Rocco S [43] predicted the time series of time-to-failure data and subsequently used the predicted values to calculate the reliability values using Equation 8.…”

Section: Dataset and Previous Studiesmentioning

confidence: 99%

“…In both approaches, lagged data from the original dataset were used for predicting the next step (or the next two steps): predictions at the previous steps were not used as input to the next prediction step. Xu et al [58] justified this by saying that multi-step ahead predictions would lead to accumulated errors.…”

Section: Dataset and Previous Studiesmentioning

confidence: 99%

“…The prediction is based solely on lagged values of the time series, without considering any additional influencing parameters. However, before it is applied to the case study the performance of the MLMVN algorithm is tested on a benchmark study from the field of reliability prediction [58] using two different approaches to compare it to the performance of other algorithms applied to similar types of problems.…”

Section: Introductionmentioning

confidence: 99%

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Predicting component reliability and level of degradation with complex-valued neural networks

Fink

Zio

Weidmann

2014

Reliability Engineering & System Safety

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. Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering and System Safety, Elsevier, 2014, 121, pp.198-206 AbstractIn this paper, multilayer feedforward neural networks based on multi-valued neurons (MLMVN), a specific type of complex valued neural networks, are proposed to be applied to reliability and degradation prediction problems, formulated as time series. MLMVN have demonstrated their ability to extract complex dynamic patterns from time series data for mid-and long-term predictions in several applications and benchmark studies. To the authors' knowledge, it is the first time that MLMVN are applied for reliability and degradation prediction. MLMVN is applied to a case study of predicting the level of degradation of railway track turnouts using real data. The performance of the algorithms is first evaluated using benchmark study data. The results obtained in the reliability prediction study of the benchmark data show that MLMVN outperforms other machine learning algorithms in terms of prediction precision and is also able to perform multi-step ahead predictions, as opposed to the previously best performing benchmark studies which only performed up to two-step ahead predictions. For the railway turnout application, MLMVN confirm the good performance in the long-term prediction of degradation and does not show accumulating errors for multi-step ahead predictions.

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Section: Dataset and Previous Studiesmentioning

confidence: 99%

Section: Dataset and Previous Studiesmentioning

confidence: 99%

Section: Dataset and Previous Studiesmentioning

confidence: 99%

Section: Dataset and Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting component reliability and level of degradation with complex-valued neural networks

Fink

Zio

Weidmann

2014

Reliability Engineering & System Safety

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Parameter estimation methods for the Weibull‐Pareto distribution

Dey¹,

Alzaatreh²,

Ghosh

2019

Comp and Math Methods

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Recently there has been a growing interest to study various estimation procedures to estimate the model parameters of the Weibull Pareto distribution (WPD). It is observed that the proposed distribution can be used quite effectively to model skewed data. They also proposed a modification of the maximum likelihood method (MML) to estimate the parameters of the WPD. The proposed methods showed a better performance from the standard maximum likelihood (ML) method. However, when the shape parameter c>>1, the modified ML method showed unforgettable large bias and standard deviation. In this article, we address some new properties and propose different methods of estimation of the unknown parameters of a WPD from the frequentist point of view. We briefly describe different frequentist approaches, namely, moments estimators, L‐moments estimators, percentile‐based estimators, least squares estimators, method of maximum product of spacings estimators, method of Cramér‐von‐Mises estimators, method of Anderson‐Darling estimators. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and compare them with modified maximum likelihood estimation from the work of Alzaatreh et al (2013a). A real data set have been analyzed for illustrative purposes. Finally, bootstrap confidence intervals are obtained for the parameters of the model based on a real data set.

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A particle swarm‐optimized support vector machine for reliability prediction

Lins

Moura

Zio

et al. 2011

Quality & Reliability Eng

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System reliability depends on inherent mechanical and structural aging factors as well as on operational and environmental conditions, which could enhance (or smoothen) such factors. In practice, the involved dependences may burden the modeling of the reliability behavior over time, in which traditional stochastic modeling approaches may likely fail. Empirical prediction methods, such as support vector machines (SVMs), become a valid alternative whenever reliable time series data are available. However, the prediction performance of SVMs depends on the setting of a number of parameters that influence the effectiveness of the training stage during which the SVMs are constructed based on the available data set. The problem of choosing the most suitable values for the SVM parameters can be framed in terms of an optimization problem aimed at minimizing a prediction error. In this work, this problem is solved by particle swarm optimization (PSO), a probabilistic approach based on an analogy with the collective motion of biological organisms. SVM in liaison with PSO is then applied to tackle reliability prediction problems based on time series data of engineered components. Comparisons of the obtained results with those given by other time series techniques indicate that the PSO + SVM model is able to provide reliability predictions with comparable or great accuracy. Copyright © 2011 John Wiley & Sons, Ltd.

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Application of neural networks in forecasting engine systems reliability

Cited by 221 publications

References 18 publications

Predicting component reliability and level of degradation with complex-valued neural networks

Predicting component reliability and level of degradation with complex-valued neural networks

Parameter estimation methods for the Weibull‐Pareto distribution

A particle swarm‐optimized support vector machine for reliability prediction

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