Remaining useful life estimation based on stochastic deterioration models: A comparative study

Son, Khanh Le; Fouladirad, Mitra; Barros, Anne; Levrat, Éric; Iung, Benoît

doi:10.1016/j.ress.2012.11.022

Cited by 226 publications

(44 citation statements)

References 37 publications

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“…Furthermore, these long-term degradation predictions could be potentially used for predicting the remaining useful life. This would further complement the existing methods applied in this field [28], [23], [18], [47].…”

Section: Introductionmentioning

confidence: 87%

“…However, while the goal of regression is to determine a mapping between the input and output, based on a finite number of observed input-output mappings, the goal of filtering is to make sequentially an inference about a dynamic system, based on the evolution of the state in time and a model relating the noisy measurements to the state [14]. Furthermore, several stochastic processes, such as gamma processes [8], [47] have been proposed for predicting degradation processes.…”

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: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

Fink

Zio

Weidmann

2014

Reliability Engineering & System Safety

View full text Add to dashboard Cite

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“…In this study, we use the same indicator as (Le, 2016) which was originally proposed by (Le Son, Fouladirad, Barros, Levrat, & Iung, 2013). It is constructed from a selection of 7 of the 21 sensors.…”

Section: Application To the Ieee 2008 Phm Challengementioning

confidence: 99%

Remaining useful life estimation methods for predictive maintenance models: defining intervals and strategies for incomplete data

Delmas¹,

Sallak²,

Schön³

et al. 2018

Proceedings of the 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability

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Abstract. Predictive maintenance allows the drawbacks of corrective and preventive maintenance to be overcome by estimating the Remaining Useful Life (RUL) of components. This value helps undertake required tasks at the right time: when the component is deteriorated but right before a failure occurs. We propose an imprecise RUL estimation method with the use of Hidden Markov Models (HMM). HMMs have been used for prognosis in several studies. Here, we fit the training data by a polynomial and then we frame it with 2 other polynomials of the same degree. This results in 3 distinct trained HMMs: the first is used for the RUL estimation and the other 2 provide the lower and higher bounds. Finally, we propose a strategy that can be adopted when dealing with incomplete or imprecise data. This work has been applied to the IEEE 2008 PHM challenge which was a competition aiming at evaluating prognosis methods.

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“…Some newer references about predictive maintenance can be found in Refs. [10][11][12]. No matter what is the goal of predictive maintenance, three key steps must be followed for its implementation: (1) data acquisition, (2) data processing and (3) maintenance decision-making.…”

Section: Predictive Maintenancementioning

confidence: 99%

Predictive Maintenance Based on Control Charts Applied at Thermoelectric Power Plant

Kisić¹,

Đurović²,

Petrović³

2017

Recent Improvements of Power Plants Management and Technology

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In this chapter, innovative predictive maintenance technique is described with the aim of highlighting the benefits of predictive maintenance compared to time-based maintenance. The proposed technique is applied to a specific problem that occurs when time-based maintenance is applied on grinding tables of the coal mill, in coal-grinding subsystem at the thermoelectric power plant 'TEKO', Kostolac, Serbia. Time-based maintenance provides replacement of grinding tables after certain number of working hours, but depending on the quality of the coal and grinding table itself, this replacement sometimes needs to be made before or after planned replacement. The consequences of such maintenance are great material losses incurred because of frequent shutdowns of the entire coal-grinding subsystem, as well as the possibility that the failure occurs before replacement. Innovative predictive maintenance technique described in the chapter is used for solution of this problem.

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Remaining useful life estimation based on stochastic deterioration models: A comparative study

Cited by 226 publications

References 37 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

Remaining useful life estimation methods for predictive maintenance models: defining intervals and strategies for incomplete data

Predictive Maintenance Based on Control Charts Applied at Thermoelectric Power Plant

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