Multi-Fault Diagnosis of Screw Compressors with Ensemble Learning: A Data-Driven Approach for Condition Monitoring

This paper presents a systematic, data-driven approach to the multi-fault diagnosis of screw compressors, with a focus on belt and oil level conditions. The paper proposes a systematic approach to data pre-processing and model training. Key steps include feature extraction using root mean square and kurtosis to extract statistical parameters. For feature selection, an embedded method based Elastic Net is used, and selected sensors are generalised for fault detection. This is followed by data augmentation using the Synthetic Minority Oversampling Technique to address imbalanced datasets acquired during operation. The study demonstrates that a single vibration sensor mounted on the motor can effectively detect both fault conditions alongside the existing current sensor. Individual models are then trained using the selected and augmented features with a Random Forest classifier, achieving 0.92 accuracy for both faults. Meanwhile, the ensemble learning-based voting classifier achieved 0.90 accuracy for multi-fault classification. This robust and scalable approach is well suited to industrial applications when developing a multi-fault diagnosis approach, and can be extended to develop low-cost sensor concepts.