Comparative performance analysis of different types of k-nearest neighbor (k-NN) classifiers for fault diagnosis of air compressor setup

Abstract

This paper presents a method for identifying Reciprocating Air Compressor (RAC) faults using acoustic signals obtained from both healthy and unhealthy conditions. The entire procedure is carried out with Uni-directional microphones using a LABVIEW-based data collection interface and data acquisition (DAQ) hardware unit that has several ports. Accumulated one healthy and seven unhealthy signals of RAC setup processed using signal processing technique called Local Mean Decomposition (LMD). Further, six Statistical Properties (SPs) have been evaluated in order to extract fault features namely: mean (US), variance (σ s2), root square of mean (Mrms), root amplitude of mean (Mrma), absolute amplitude of mean (Mama), and Kurtosis index (Ki). Extracted fault features are classified using various types of k-NN classifiers namely: fine (f-kNN), medium (m-kNN), coarse (c-kNN) and weighted (w-kNN). It has been found that LMD along with 6 statistical properties and form different type of k-NN classifiers, the weighted k-NN classifier has a greater accuracy of 86.74%, which is quite accurate as compared to other k-NN classifiers.