Integrated Bayesian framework for remaining useful life prediction

Mosallam, Ahmed; Medjaher, Kamal; Zerhouni, Noureddine

doi:10.1109/icphm.2014.7036361

Cited by 23 publications

(29 citation statements)

References 17 publications

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“…IBL application in prognostics is relatively new (beginning of the 2000s) [11][12][13][14][15]. IBL re-utilizes the experience gained from solving similar instances to solve new problem instances.…”

Section: Related Workmentioning

confidence: 99%

“…The final RUL estimate of the given engine was obtained by aggregating the RULs of similar training instance via a similarity weighted average. Mosallam et al in [13] modeled the monitoring data as trajectories that characterize the lifecycle of the component using principal component analysis. For the problem component, the most similar trajectory is retrieved based on the Euclidean distance, and its RUL is directly considered as the RUL for the new component.…”

Section: Related Workmentioning

confidence: 99%

“…At the retrieval step, the similarity is either set by using a vector of attributes that characterizes the instance instead of the instance data [12], or by using a feature vector of only the latest measurements [11,15]. To address this, [13,14] considered the complete history of observations with a point-wise Euclidean distance between the test and train trajectories. These latter truly fully used the up-to-date condition monitoring data.…”

Section: Related Workmentioning

confidence: 99%

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RUL prediction based on a new similarity-instance based approach

Khelif

Malinowski

Chebel-Morello

et al. 2014

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

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Prognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety, reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of a degrading component/ system, i.e. to predict the time after which a component/system will no longer be able to meet its operating requirements. RUL prediction is a challenging task that requires special attention when modeling the prognostics approach. This paper proposes a RUL prediction approach based on Instance Based Learning (IBL) with an emphasis on the retrieval step of the latter. The method is divided into two steps: an offline and an online step. The purpose of the offline phase is to learn a model that represents the degradation behavior of a critical component using a history of run-to-failure data. This modeling step enables us to construct a library of health indicators (HI) from run-tofailure data. These HI's are then used online to estimate the RUL of components at an early stage of life, by comparing their HI's to the ones of the library built in the offline phase. Our approach makes use of a new similarity measure between HIs. The proposed approach was tested on real turbofan data set and showed good performance compared to other existing approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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RUL prediction based on a new similarity-instance based approach

Khelif

Malinowski

Chebel-Morello

et al. 2014

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

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“…Failure points [21,23,30,34,39,40]. If this is satisfied, estimated RUL is expected to be accurate [73].…”

Section: Fault Initiationmentioning

confidence: 96%

“…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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A Hybrid Prognostics Approach for Motorized Spindle-Tool Holder Remaining Useful Life Prediction

Han

Wang

Qiu

et al. 2020

Advances in Asset Management and Condition Monitoring

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The quality and efficiency of high-speed machining are restricted by the matching performance of the motorized spindle-tool holder. In high speed cutting process, the mating surface is subjected to alternating torque, repeated clamping wear and centrifugal force, which results in serious degradation of mating performance. Therefore, for the purpose of the optimum maintenance time, periodic evaluation and prediction of remaining useful life (RUL) should be carried out. Firstly, the mapping model between the current of the motorized spindle and matching performance was extracted, and the degradation characteristics of spindle-tool holder were emphatically analyzed. After the original current is de-noised by an adaptive threshold function, the extent of degradation was identified by the amplitudes of wavelet packet entropy. A hybrid prognostics combining Relevance Vector Machine (RVM) i.e AI-model with power regression i,e. statistical model was proposed to predict the RUL. Finally, the proposed scheme was verified based on a motorized spindle reliability test platform. The experimental results show that the current signal processing method based on wavelet packet and entropy can reflect the change of the degradation characteristics sensitively. Compared with other two similar models, the hybrid model proposed can accurately predict the RUL. This model is suitable for complex and high reliability equipment when Condition Monitoring (CM) data is scarcer.

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Integrated Bayesian framework for remaining useful life prediction

Cited by 23 publications

References 17 publications

RUL prediction based on a new similarity-instance based approach

RUL prediction based on a new similarity-instance based approach

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

A Hybrid Prognostics Approach for Motorized Spindle-Tool Holder Remaining Useful Life Prediction

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