Research on Unsupervised Learning-based Health Indicator Construction Methods
In response to the problem of relying on expert knowledge in health indicator construction methods,this paper proposed a method for constructing health indices based on unsupervised database,integrated with multi-scale residual convolutional neural network with contextualized encoder-decoder ar-chitecture and squared Euclidian distance(MSR-CNED-SE).Furthermore,it utilized deep learning models to predict remaining useful life.The reliability of the proposed method was validated on a compre-hensive bearing life dataset.Additionally,the effects of different similarity and deep learning networks on health indicators were studied.The results indicate that health indicators constructed using the squared Euclidean distance as a similarity measure are more effective in identifying the onset of degradation.Mo-reover,the Bi-LSTM network exhibits better stability and reliability under different prediction scenarios.
health indicatorunsupervised learningremaining useful life predictionsquared Euclide-an distanceBi-LSTM network
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