Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction

作     者：Berghout, Tarek Benbouzid, Mohamed Mouss, Leila-Hayet 

作者机构：Univ Batna 2 Lab Automat & Mfg Engn Batna 05000 Algeria Univ Brest Inst RechercheDupuy Lome UMR CNRS 6027 F-29238 Brest France Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China 

出 版 物：《ENERGIES》 (能源)

年 卷 期：2021年第14卷第8期

页      面：2163-2163页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：bearings prognosis remaining useful life data-driven knowledge-driven transfer learning labels information exploiting labels denoising autoencoder convolutional LSTM 

摘      要：Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long-short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.