Regression to fuzziness method for estimation of remaining useful life in power plant components

Alamaniotis, Miltiadis; Grelle, Austin; Tsoukalas, Lefteri H.

doi:10.1016/j.ymssp.2014.02.014

Cited by 27 publications

(15 citation statements)

References 19 publications

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“…[33][34][35], or to compare performance of several algorithms [36,37]. 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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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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“…However, the regression model is difficult to describe the stochastic characteristics of the degradation process, and the RUL estimation problem with three-source variability is not considered. In addition, Alamaniotis et al [8] modeled the degradation system with expert knowledge and historical experience to compensate for the lack of historical data, but the model depended on the expert's experience with the degradation system. Consequently, the study of the RUL estimation using degradation models with three-source variability for small-sample systems is still very limited.…”

Section: Related Workmentioning

confidence: 99%

“…As the key of PHM technology, the RUL estimation has received extensive attention from the academic community in recent years. According to the review by [7] and references therein, the current RUL estimation approaches are mainly classified as knowledge-based approaches [8], [9], physical model-based approaches [10], [11], and data-driven approaches [12], [13]. Knowledge-based approaches aim to predict the RUL of the concerned system by modeling an expert system or a fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Prognostic Approach for Newly Developed System With Three-Source Variability

Wang

et al. 2019

IEEE Access

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Remaining useful life (RUL) estimation is the key of prognostics and health management (PHM) technology and is an effective way to ensure the safe and reliable operation of equipment. Aiming at the lack of historical data and prior information for the newly developed small-sample systems, an adaptive RUL estimation method based on the expectation maximization (EM) algorithm is proposed with threesource variability. First, a degradation model based on a Wiener process is established to incorporate threesource variability and dynamic sampling interval, and the analytical solution of RUL distribution is derived in the sense of the first hitting time. Second, an adaptive parameter estimation method based on the EM algorithm is proposed to update the model parameters by using the condition monitoring (CM) data from one working system running up to the current moment. Finally, a practical example of a gyroscope in an inertial navigation system is provided to substantiate the effectiveness and superiority of the proposed method. The results indicate that the proposed method can efficiently improve the accuracy of the RUL estimation. INDEX TERMS Remaining useful life, three-source variability, expectation maximization, dynamic sampling interval.

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“…• Pointwise similarity measure Pointwise similarity determines a degree of closeness of test pattern and reference. The pointwise difference δ(i, j) between the two patterns is computed by Equations (4) and (5), i = 1,2,…,10, j = 1,2,…,6, l = 1,2,…,5, g = 4,3,…,0.…”

Section: • Fault Detectionmentioning

confidence: 99%

“…and Artificial Intelligence (AI) techniques (neural networks, fuzzy systems, etc.) [5][6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Residual Useful Life Estimation by a Data-Driven Similarity-Based Approach

Jian

2016

Qual. Reliab. Engng. Int.

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This paper is to provide a general data-driven, similarity-based approach for residual useful life estimation for industrial components or systems. Degradation signal data from failure dynamic systems are used to create a database of reference trajectory patterns. Variable weights are assigned to associated points before pointwise similarity computation. Distance score and weight are computed by calculating the fuzzy similarity between test trajectory pattern and reference trajectory patterns. Remaining useful life then could be predicted through aggregating those failure references' residual lifetimes in a weighted sum and updated as time goes by. The potentiality of this approach is illustrated on a problem of contact resistance degradation data. The results indicate that there is no significant difference between Gaussian function and General bell shaped function.

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Regression to fuzziness method for estimation of remaining useful life in power plant components

Cited by 27 publications

References 19 publications

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

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

An Adaptive Prognostic Approach for Newly Developed System With Three-Source Variability

Residual Useful Life Estimation by a Data-Driven Similarity-Based Approach

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