PROJECT TITLE :

Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors

ABSTRACT:

Intelligent fault diagnosis of machines for early recognition of faults saves business from heavy losses occurring thanks to machine breakdowns. This paper proposes a process with a generic knowledge mining model which will be used for developing acoustic signal-primarily based fault diagnosis systems for reciprocating air compressors. The process includes details of information acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, Feature Extraction, Feature Selection, and a classification approach. This method was validated by developing a real time fault diagnosis system on a reciprocating sort air compressor having eight designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.