Experimental investigations on fault diagnosis of ball bearing with solid contamination

Abstract

newline Rolling bearings have diverse applications in industrial sectors and fields which need rotational motion The rapid demands for bearings have paved way for the question on their reliability to avoid malfunctioning and failure The assessment of their reliability has given a new dimension to the condition Monitoring CM of equipment and tools The recent studies show that the overall demand of the CM market is expected to reach 2 45 billion USD by 2020 The demand and supply have made the field of CM lucrative as well as sensitive In general CM relies on the advanced sensor technologies and signal processing methods to diagnose machinery and their related faults Especially to anticipate Bearings Condition Monitoring BCM various sensors like accelerometer Acoustic Emission AE ultrasonic thermal imaging microphone and Motor Current Signature Analysis MCSA and so on are widely used prevalently Out of these sensing methods vibration based sensing finds usage of around 87 Scopus index database for reliable fault diagnosis and detection Recently microphone sensors have given a new dimension to the CM due to their significant improvement in the sensitivity and bandwidth as well as their coherence with vibration The goal of the present research is to analyse vibration and acoustic signals from the bearings using statistical and signal processing parameters like Root Mean Square RMS Kurtosis and Peak-to-Peak Three signal processing methods are proposed to identify the faults related to bearings newline newline