Deep Forest based Multivariate Classification for Diagnostic Health Monitoring
作者单位：IEEE College of Automation Engineering Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing University of Aeronautics and Astronautics
会议名称：《第30届中国控制与决策会议》
会议日期：2018年
学科分类：100208[医学-临床检验诊断学] 0810[工学-信息与通信工程] 1002[医学-临床医学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理] 10[医学]
基 金：supported by National Nature Science Foundation under Grant 61673206 Equipment Pre-research National Defense Science Technology Key Laboratory Foundation under Grant 61422080307 Collaborative Innovation Major Project of Production and Research of Guangzhou Technology under Grant 201604016038
关 键 词：PHM Diagnostic health monitoring Deep forest K-means Feature selection
摘 要：Diagnostic health monitoring without prior knowledge is still a hard problem in the Prognostic and Health Management(PHM) field. A traditional approach is to construct one-dimensional Synthesized Health Index(SHI) and set the thresholds of different system health stages referred to empirical values. However, there is still a difficult problem to be solved on choosing appropriate SHI and thresholds for a complicated system, which are the key factors seriously influencing the result of diagnostic health monitoring. Compared with this approach, a new method of deep forest is proposed to achieve multivariate classification for diagnostic health monitoring. Firstly, an unsupervised feature selection method based on spearman’s correlation is improved to remove redundant features automatically. Then, the k-means algorithm is introduced to acquire stage knowledge of degradation process, therefore achieving supervised ***, deep forest method is adopted to train the data samples to obtain the classification model of system health for online diagnostic health monitoring. The verification results on NASA data sets demonstrate that the proposed diagnostic health monitoring scheme is effective and feasible.