RUL prediction based on a new similarity-instance based approach

Khelif, Racha; Malinowski, Simon; Chebel-Morello, Brigitte; Zerhouni, Noureddine

doi:10.1109/isie.2014.6865006

Cited by 58 publications

(35 citation statements)

References 17 publications

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“…6 A series of researches have been carried out towards this topic. For example, Khelif et al 7 used offline data to learn linear regression model parameters and used both offline and online to convert sensory data into health indicators. Liu et al 8 applied superstatistics and information fusion to get a comprehensive health indicator and achieved a markedly better prognostic of RUL.…”

Section: Introductionmentioning

confidence: 99%

Bearing life prognosis based on monotonic feature selection and similarity modeling

Niu

Fang

Choi

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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In data-driven prognosis approach, indicator information plays an important role for reliable prediction. Although lots of researches have been carried out on prognosis algorithms, only few have paid attention on developing an effective method to select ‘good’ degradation indicators. This paper presents a novel strategy to address the problem, which mainly proposes methods of monotonic feature selection using rank mutual information, and similarity-based modeling for remaining life estimation. The proposed system is demonstrated based on open source data of bearing life cycle. The experiment results show that satisfactory prognostic performance can be obtained with advantages of simplicity, accuracy and generality.

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Section: Introductionmentioning

confidence: 99%

Bearing life prognosis based on monotonic feature selection and similarity modeling

Niu

Fang

Choi

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…These works used either experimental [30,33,34,36,37]or simulated [31,35] or actual [32] raw sensor data. [23,[25][26][27]41,42,44] or simulated [22,24,[38][39][40][41][42][43]45]…”

Section: K-nearest Neighbours Regression (Knnr) Which Belongs To Simimentioning

confidence: 99%

Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements

et al. 2019

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Reciprocating compressors are critical components in the oil and gas sector, though their maintenance cost is known to be relatively high. Compressor valves are the weakest component, being the most frequent failure mode, accounting for almost half the maintenance cost. One of the major targets in industry is minimisation of downtime and cost, while maximising availability and safety of a machine, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the diagnostics and prognostics principles, is a step towards this direction as it offers a proactive means for scheduling maintenance. Despite the fact that diagnostics is an established area for reciprocating compressors, to date there is limited information in the open literature regarding prognostics, especially given the nature of failures can be instantaneous. This work presents an analysis of prognostic performance of several methods (multiple linear regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and variability, using actual temperature only valve failure data, an instantaneous failure mode, from an operating industrial compressor. Furthermore, a variation for Remaining Useful Life (RUL) estimation based on KNNR, along with an ensemble technique merging the results of all aforementioned methods are proposed. Prior to analysis, principal components analysis and statistical process control were employed to create ! ! and ! metrics, which were proposed to be used as health indicators reflecting degradation process of the valve failure mode and are proposed to be used for direct RUL estimation for the first time. Results demonstrated that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques.

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“…The retrieval phase of the algorithm is based on a Euclidean distance measure where only the last block of the testing trajectory is considered and compared to blocks of the library. In [31], Khelif et al proposed a new similarity measure that aims at giving more weight to last observations (as they are more likely to be correlated with the degradation level), while using the total testing HI trajectory as instances.…”

Section: Related Workmentioning

confidence: 99%

Remaining useful life estimation based on discriminating shapelet extraction

Malinowski

Chebel-Morello

Zerhouni

2015

Reliability Engineering & System Safety

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In the Prognostics and Health Management (PHM) domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics as data acquisition, fusion, diagnostics, prognostics and decision are involved in this domain. This paper presents an approach to estimate the Remaining Useful Life of equipment based on shapelet extraction. This approach makes use, in an offline step, of a history of run-to-failure data to extract discriminative rul-shapelets, i.e. patterns that are correlated with the RUL of the considered equipment. A library of rul-shapelets is hence extracted at this step. Then, in an online step, these rul-shapelets are compared to testing units and the ones that match these units are used to estimate their RULs. Therefore, the RUL estimation of a testing unit is based on patterns that have been selected for their high correlation with the RUL. This approach is hence different from classical similaritybased approaches that attempt to match complete testing units (or only late instants of testing units) with training ones to estimate the RUL. The performance of our approach is assessed with a case study on the remaining useful life estimation of turbofan engines and performance is compared with other similarity-based approaches.

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RUL prediction based on a new similarity-instance based approach

Cited by 58 publications

References 17 publications

Bearing life prognosis based on monotonic feature selection and similarity modeling

Bearing life prognosis based on monotonic feature selection and similarity modeling

Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements

Remaining useful life estimation based on discriminating shapelet extraction

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