Abstract
Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed.
Highlights
1.1 BackgroundIn modern industry, machines develop towards being more complicated, and intelligent, and are subject to growingly demanding operation conditions
local mean decomposition (LMD) is compared with empirical mode decomposition (EMD) and the results show the superiority of the LMD approach
Cui et al [168] proposed a novel approach based on switching unscented Kalman filter (SUKF) for bearing remaining useful life (RUL) prediction
Summary
Machines develop towards being more complicated, and intelligent, and are subject to growingly demanding operation conditions. CBM ( called predictive maintenance) is a maintenance procedure that takes maintenance actions based on the information indicated in the condition of machinery instead of regular time interval, which achieves objectives of cost reduction and reliability improvement. The key of a CBM program is the maintenance decisionmaking where maintenance actions are recommended through diagnosis and prognosis. Fault diagnosis aims at identifying the fault mode of the machinery after detection and prognostics commonly oriented towards identifying and quantifying the fault. The latter is capable of predicting the process of degradation. Diagnostics can be a complementary tool to provide maintenance decision support when prediction approach fails and a fault occurs
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