DeepAI AI Chat
Log In Sign Up

Bayesian Sample Size Determination of Vibration Signals in Machine Learning Approach to Fault Diagnosis of Roller Bearings

by   Siddhant Sahu, et al.

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.


Bayesian inference for the Net Promoter Score

The Net Promoter Score is a simple measure used by several companies as ...

Assurance for sample size determination in reliability demonstration testing

Manufacturers are required to demonstrate products meet reliability targ...

Do We Really Sample Right In Model-Based Diagnosis?

Statistical samples, in order to be representative, have to be drawn fro...

Bayesian sample size determination for diagnostic accuracy studies

The development of a new diagnostic test ideally follows a sequence of s...

Sample Size Planning for Classification Models

In biospectroscopy, suitably annotated and statistically independent sam...

Sample size determination via learning-type curves

This paper is concerned with sample size determination methodology for p...