Bayesian Sample Size Determination of Vibration Signals in Machine Learning Approach to Fault Diagnosis of Roller Bearings
This work addresses the need for efficient data analysis in fault diagnosis for researchers, but it is incremental as it applies existing Bayesian and machine learning methods to a specific domain.
The study tackled the problem of determining the minimum sample size for vibration signals in bearing fault diagnosis by introducing an analytical formula using Bayesian analysis, resulting in a method that helps achieve good statistical stability and precision with reduced computational work.
Sample size determination for a data set is an important statistical process for analyzing the data to an optimum level of accuracy and using minimum computational work. The applications of this process are credible in every domain which deals with large data sets and high computational work. This study uses Bayesian analysis for determination of minimum sample size of vibration signals to be considered for fault diagnosis of a bearing using pre-defined parameters such as the inverse standard probability and the acceptable margin of error. Thus an analytical formula for sample size determination is introduced. The fault diagnosis of the bearing is done using a machine learning approach using an entropy-based J48 algorithm. The following method will help researchers involved in fault diagnosis to determine minimum sample size of data for analysis for a good statistical stability and precision.