Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

Abstract

Air compressors have become critical equipment in different industrial applications such as metallurgy, mining, machinery manufacturing, petrochemical industry, transportation, etc. However, because of their complex structure and often harsh working environment, air compressors inevitably face a variety of faults and failures during their operation. Therefore, intelligent diagnostic techniques are crucially important for early fault recognition and detection to avoid industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is proposed based on several approaches, mainly: Maximal overlap discrete wavelet packet transform (MODWPT) and time domain features for feature extraction, weighted superposition attraction (WSA) for feature selection and random forest (RF), ensemble tree (ET) K-nearest neighbors (KNN) as classifiers. The proposed approach is applied to real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states. According to our approach, the data signals are decomposed by MODWPT into several nodes. Then, the time domain features are calculated for each node to construct the feature matrix for each air compressor health state. After that, WSA is applied to every matrix in the feature selection step. Finally, KNN, ET and RF are used to calculate the classification accuracy and give the confusion matrix. Compared with the robust empirical mode decomposition (REMD), the experimental results prove the effectiveness of the proposed approach to detect, identify and classify all air compressor faults.

Enhancing air compressors multi fault classification using new criteria for Harris Hawks optimization algorithm in tandem with MODWPT and LSSVM classifier

“…However, air compressors can develop faults and breakdown, as can any mechanical system, which can lead to reduced performance and expensive downtime. 1 In order to maintain peak performance, increase the compressor's lifespan, and reduce maintenance costs, problems in the compressor must be quickly identified and corrected. Visual inspections, manual monitoring, and routine maintenance programs are examples of traditional approaches that are labor-and time-intensive.…”

“…These reducing technologies ensure that the compressor stays in excellent working condition for a longer period of time by identifying deficiencies early, which prevents the emergence of more serious and expensive problems. 1 The use of signal processing techniques has made it possible to quickly and precisely identify compressor faults. 6 These methods entail examining the acoustic signal from the compressor to spot distinct patterns or features connected to various kinds of faults.…”

“…However, air compressors can develop faults and breakdown, as can any mechanical system, which can lead to reduced performance and expensive downtime. 1…”

“…These reducing technologies ensure that the compressor stays in excellent working condition for a longer period of time by identifying deficiencies early, which prevents the emergence of more serious and expensive problems. 1…”