Open AccessNew statistics for simultaneously machine incipient fault detection and monotonic degradation assessment
Author(s) -
Bingchang Hou,
Yikai Chen,
Yanqing Deng,
Yuting Wang,
Dong Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1983/1/012112
Subject(s) - kurtosis , monotonic function , fault (geology) , parameterized complexity , computer science , degradation (telecommunications) , fault detection and isolation , statistics , reliability engineering , algorithm , artificial intelligence , mathematics , engineering , telecommunications , mathematical analysis , seismology , actuator , geology
Machine condition monitoring (MCM) has become an important tool to avoid sudden machine breakdown and gaining more economic profits. Tasks including early fault detection and monotonic degradation assessment are important in MCM. For the incipient fault detection, statistics such as kurtosis, Gini index are widely utilized, but they cannot give an accurately incipient fault detection time, and many fluctuations may exhibit. For the monotonic degradation assessment, root-mean-square are commonly used, however, it is sensitive to energy, and cannot show distinct degradation tendency in an early fault state. Those drawbacks have limited the development of practical MCM algorithms. To address those issues, this paper proposed four parameterized statistics for simultaneously early fault detection and monotonic degradation assessment. The four parameterized statistics can be health indicators and simplify the MCM algorithms, which can be beneficial to the practical MCM applications.