“Fault diagnosis in air compressor system using advanced signal processing (LMD and EMD) and discriminant analysis-based (LDA and QDA) machine learning techniques”

This study proposes an advanced methodology for fault detection and prediction in air compressor (AC) systems using acoustic signal analysis under both healthy and faulty operating conditions. Audio data were acquired using a unidirectional microphone interfaced with an NI 9234 data acquisition module and an NI 9172 chassis. The collected signals were processed using recent non-traditional techniques, namely Local Mean Decomposition (LMD) and Empirical Mode Decomposition (EMD), to extract detailed fault-related characteristics. To identify the most dominant fault among seven faulty conditions, a Bubble Cloud (B-Cloud) analysis was employed using 15 statistical indicators (SIs) as input features. These indicators were subsequently classified using discriminant-analysis-based machine learning algorithms, including Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). The experimental results reveal that LMD provides superior signal decomposition performance compared to EMD due to its enhanced capability in isolating intrinsic oscillatory components. Among all SIs, the Kurtosis index proved to be the most sensitive and reliable feature for fault discrimination, particularly when combined with LMD outputs. Furthermore, LDA achieved the highest classification accuracy of 88.88%, outperforming QDA, and demonstrating its suitability for real-time fault prediction. Overall, the proposed framework offers a robust, accurate, and efficient solution for identifying critical fault conditions in AC systems, supporting improved predictive maintenance and system reliability.