Online feature learning for condition monitoring of rotating machinery

Abstract Condition-based maintenance of rotating machinery requires efficient condition monitoring methods that enable early detection of abnormal operational conditions and faults. This is a challenging problem because machines are different and change characteristics over time due to wear and maintenance. The efficiency and scalability of conventional condition monitoring methods are limited by the need for manual analysis and re-configuration. The problem to extract relevant features from condition monitoring signals and thereby detect and analyze changes in such signals is a central issue, which in principle can be addressed using machine learning methods. Former work demonstrates that dictionary learning can be used to automatically derive signal features that characterize different operational conditions and faults of a rotating machine, but the use of such methods for online condition monitoring purposes is an open problem. Here we investigate online learning of features using dictionary learning. We describe dictionary distance and signal fidelity based heuristics for anomaly detection, and we study the time-propagated features and sparse approximation of vibration and acoustic emission signals in three different case studies. We present results of numerical experiments with different hyperparameters affecting the approximation accuracy, computational cost, and the adaptation rate of the learned features. We find that the learned features change slowly under normal variations of the operational conditions in comparison to the rapid adaptation observed when a fault appears (bearing defects, magnetite particles in the lubricant, or plastic deformation of steel). Furthermore, we find that a sparse signal approximation with 2.5% preserved coefficients based on a propagated dictionary is sufficient for bearing defect detection.