Multi-scale remaining useful life prediction using long short-term memory

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dc.contributor.authorWang, Youdao
dc.contributor.authorZhao, Yifan
dc.date.accessioned2022-12-13T15:05:44Z
dc.date.available2022-12-13T15:05:44Z
dc.date.issued2022-11-22
dc.description.abstractPredictive maintenance based on performance degradation is a crucial way to reduce maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for decision-making in predictive maintenance. Conventional model-based methods and data-driven approaches often fail to achieve an accurate prediction result using a single model for a complex system featuring multiple components and operational conditions, as the degradation pattern is usually nonlinear and time-varying. This paper proposes a novel multi-scale RUL prediction approach adopting the Long Short-Term Memory (LSTM) neural network. In the feature engineering phase, Pearson’s correlation coefficient is applied to extract the representative features, and an operation-based data normalisation approach is presented to deal with the cases where multiple degradation patterns are concealed in the sensor data. Then, a three-stage RUL target function is proposed, which segments the degradation process of the system into the non-degradation stage, the transition stage, and the linear degradation stage. The classification of these three stages is regarded as the small-scale RUL prediction, and it is achieved through processing sensor signals after the feature engineering using a novel LSTM-based binary classification algorithm combined with a correlation method. After that, a specific LSTM-based predictive model is built for the last two stages to produce a large-scale RUL prediction. The proposed approach is validated by comparing it with several state-of-the-art techniques based on the widely used C-MAPSS dataset. A significant improvement is achieved in RUL prediction performance in most subsets. For instance, a 40% reduction is achieved in Root Mean Square Error over the best existing method in subset FD001. Another contribution of the multi-scale RUL prediction approach is that it offers more degree of flexibility of prediction in the maintenance strategy depending on data availability and which degradation stage the system is in.en_UK
dc.identifier.citationWang Y, Zhao Y. (2022) Multi-scale remaining useful life prediction using long short-term memory. Sustainability, Volume 14, Issue 23, November 2022, Article number 15667en_UK
dc.identifier.issn2071-1050
dc.identifier.urihttps://doi.org/10.3390/su142315667
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18796
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectclassificationen_UK
dc.subjectC-MAPSSen_UK
dc.subjectfeature engineeringen_UK
dc.subjectRNNen_UK
dc.subjectRUL target functionen_UK
dc.titleMulti-scale remaining useful life prediction using long short-term memoryen_UK
dc.typeArticleen_UK

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