标题：Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window
作者：Wang, Teng; Lu, Guo-Liang; Liu, Jie; Yan, Peng
作者机构：[Wang, T] Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE, School of Mechanical Engineering, Shandong University, Jinan, 250 更多
通讯作者地址：[Lu, GL]Shandong Univ, Sch Mech Engn, Key Lab High Efficiency & Clean Mech Manufacture, Jinan 250061, Shandong, Peoples R China.
关键词：Machine monitoring;Change detection;Long-term monitoring;Adaptive threshold
摘要：Detection of structural changes from an operational process is a major goal in machine condition monitoring.Existing methods for this purpose are mainly based on retrospective analysis,resulting in a large detection delay that limits their usages in real applications.This paper presents a new adaptive real-time change detection algorithm,an extension of the recent research by combining with an incremental sliding-window strategy,to handle the multi-change detection in long-term monitoring of machine operations.In particular,in the framework,Hilbert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RKHS) for change detection;then,a new adaptive threshold strategy can be developed when making change decision,in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold.Through experiments on a range of real testing data which was collected from an experimental rotating machinery system,the excellent detection performances of the algorithm for engineering applications were demonstrated.Compared with state-of-the-art methods,the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration,thus is promising in modern industries.