Poincaré Images Extracted from Vibration Signals are Useful Features for Fault Classification in a Reciprocating Compressor

Fault classification is an essential tool for Prognostics and Health Management (PHM), whose goal is the assessment of the health condition of a machine and its components. In this research, we propose using Poincaré Images (PI) as features for fault classification using Convolutional Neural Networks (CNN). The approach is validated for classifying 17 valve faults in a reciprocating compressor (RC). The proposed approach attained an averaged classification accuracy of 94.97% which represents an improvement of about 15% concerning using discrete features extracted from the Poincaré plot for classifying faults with Random Forest models. Additionally, results show that the classification performance with PI is comparable to using spectrograms as alternative input images to CNN-based fault classifiers.