Role of Signal Processing, Modeling and Decision Making in the Diagnosis of Rolling Element Bearing Defect: A Review

被引:73
作者
Kumar, Anil [1 ]
Kumar, Rajesh [2 ]
机构
[1] Amity Univ, Noida 201303, India
[2] St Longowal Inst Engn & Technol, Dept Mech Engn, Precis Metrol Lab, Longowal 148106, India
关键词
Vibration; Signal processing; Modeling; Artificial intelligence; Prognosis; FAULT FEATURE-EXTRACTION; DISCRETE WAVELET TRANSFORM; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; REMAINING USEFUL LIFE; ROTATING MACHINERY; ROLLER BEARING; ACOUSTIC-EMISSION; VIBRATION SIGNALS; PACKET TRANSFORM;
D O I
10.1007/s10921-018-0543-8
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
A significant development in condition monitoring techniques has been observed over the years. The scope of condition monitoring has been shifted from defect identification to its measurement, which was later on extended to automatic prediction of defect. This development is possible because of advancement in the area of signal processing. A number of signal processing and decision making techniques are available each having their own merits and demerits. A specific technique can be most appropriate for a given task, however, it may not be suitable or efficient for a different task. This paper reviewed recent and traditional research, and development in area of defect diagnosis, defect modelling, defect measurement and prognostics. Also it highlights the merit and demerit of various signal processing techniques. This paper is written with the objective to serve as guide map for those who work in the field of condition monitoring.
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页数:29
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