Potential effects of statistical complexity measure in improving the fault diagnosis performance under speed fluctuations

Abstract Many research studies have been performed to extract non-stationary signal properties, such as the instantaneous frequency (IF) and envelope, to diagnose machinery faults under time-varying speed conditions. However, accurate IF extraction is difficult from such complex fluctuating signals to identify faults efficiently. Therefore, this paper proposes the statistical complexity measure (SCM) as a feature that evaluates the randomness and complexity without any additional requirement, providing an outstanding result in multi-component fault detection under speed fluctuations. Primarily, the influence of SCM is proved by ranking the features with the help of the random forest algorithm. The fault classification of individual signal characteristics confirms its ranking order as well. Then, it is targeted toward the efficacy of the signal fusion approach in multi-component fault diagnosis. Focusing on the essence of SCM, it is merged with the other three signal properties based on the ranking technique to investigate which one of the feature compositions is more advantageous. The machine learning classifier, SVM-RBF, achieves 94.7%, which proves the potential effects of SCM features in industrial rotational machinery components under variable speed conditions.