  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.
