Abstract
In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.
Highlights
Machine learning classification matrix is used to evaluate the degradation states of bearing over time due to variation in operating parameters and environmental conditions such as load, speed, high temperature, etc
This paper has reviewed the publications from the science and engineering journals on bearing diagnostic and prognostic in the past 20 years
The paper reviews the diagnostic and prognostic mod- used in fault identification, signal processing and machine learning els for the remaining useful life estimation of the bearing
Summary
Machine learning classification matrix is used to evaluate the degradation states of bearing over time due to variation in operating parameters and environmental conditions such as load, speed, high temperature, etc. An accelerometer and unexplored challenges were provided[10].This paper gives a compre- a thermocouple were employed to acquire the vibration signals and hensive review on bearing diagnostics and prognostics. The paper reviews the different health condition indicators er bearing with fault depth of 0.1778 mm, 0.3556 mm, and 0.5334 used in the prior state of art for degradation assessment of mm. They acquired vibration data at a sampling frequency 12 kHz bearing. The paper reviews the diagnostic and prognostic mod- used in fault identification, signal processing and machine learning els for the remaining useful life estimation of the bearing.
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