Enhanced feature extraction for machinery condition monitoring using recurrence plot and quantification measure.

Machinery condition monitoring is a crucial application to enable condition-based predictive maintenance, realizing smart manufacturing in the context of the Industry 4.0 revolution. Machine learning that is built upon data correlation has recently become mainstream for machinery fault diagnosis. To boost the fault diagnosis performance of the machine learning methods, extracting high-quality fault-related features from raw time-series response signals is the key. In this research, we propose a signal processing-based feature extraction method, the so-called recurrence plot (RP), to evaluate the hidden dynamic characteristics of the vibration signals collected from the machinery system that is subject to fault. To further examine the properties of RP, the recurrence quantification analysis (RQA) is employed to quantitatively measure the patterns in the RP. The main strength of the proposed methodology lies in its excellent capability in handling the nonstationary signals with strong system nonlinearity and ambient noise, thereby yielding high-quality features with a high degree of separation in terms of the fault condition. A novel quantitative metric based on the silhouette is established to thoroughly assess the feature quality, providing systematic guidance for machinery condition monitoring. Comprehensive case studies using publicly accessible gear and bearing fault datasets are carried out to validate the proposed methodology. The enhanced performance of the proposed methodology also is highlighted by comparing it with other benchmark methods. [ABSTRACT FROM AUTHOR]