A new supervised multi-head self-attention autoencoder for health indicator construction and similarity-based machinery RUL prediction

作     者：Qin, Yi Yang, Jiahong Zhou, Jianghong Pu, Huayan Mao, Yongfang 

作者机构：Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ Coll Automat Chongqing 400044 Peoples R China 

出 版 物：《ADVANCED ENGINEERING INFORMATICS》 (高级工程信息学)

年 卷 期：2023年第56卷第1期

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [52175075, 62033001] Chongqing Research Program of Basic Research and Frontier Exploration [cstc2021ycjh-bgzxm0157] "Shuguang Program" Shanghai Education Development Foundation [18SG36] Shanghai Education Devel- opment Foundation Shanghai Municipal Education Commission 

主　　题：Health indicator RUL prediction Autoencoder Self-attention Rotating machinery 

摘      要：Remaining useful life (RUL) prediction plays a significant role in the prognostic and health management (PHM) of rotating machineries. A good health indicator (HI) can ensure the accuracy and reliability of RUL prediction. However, numerous existing deep learning-based HI construction approaches rely heavily on the prior knowledge, and they are difficult to capture the key information in the process of machinery degradation from raw signals, thereby affecting the performance of RUL prediction. To tackle the aforementioned problem, a new supervised multi-head self-attention autoencoder (SMSAE) is proposed for extracting the HI that effectively reflects the degraded state of rotating machinery. By embedding the multi-head self-attention (MS) module into autoencoder and imposing the constraint of power function-type labels on the hidden variable, SMSAE can directly extract the HIs from raw vibration signals. As the current HI evaluation indexes don t consider the global monotonicity and variation law of HI, two improved monotonicity and robustness indexes are designed for the better evaluation of HI. With the proposed HI, a two-stage residual life prediction framework based on similarity is developed. Extensive experiments have been performed on an actual wind turbine gearbox bearing dataset and a well-known open commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The comparative results verify that the constructed SMSAE HI has better comprehensive performance than the typical HIs, and the proposed prediction method is competitive with the state-of-the-art methods.