An Intelligent Anomaly Detection Method for Rotating Machinery Based on Vibration Vectors

Anomaly detection is a common technology for condition monitoring that is important for safe and reliable production in industry. In this work, we proposed an anomaly detection method for rotating machinery based on vibration vectors,whichwas inspiredby Polar plot in vibration analysis. The vibration vector, consisting of amplitude and phase, was selected as monitored indicator at different characteristic frequencies (CFs). The original vibration vectors obtained by fast Fourier transform-based order analysis (FFT-OA) were mapped from Polar to Cartesian plot for simplified calculation and better visualization. The acceptance region defined to distinguish anomalies from normal conditions was learned by support vector data description (SVDD) model with normal vibration samples. We proposed the anomaly score to indicate the degree of deviation from normal condition, and the fit degree to reduce underfitting of data description model. Two datasets, obtained from a real steam turbine and a real steam feed pump respectively, were used to validate the method under constant and variable speed operation conditions. Compared to the amplitude-based methods, the proposed method exhibited an extremely strong ability to detect anomalies in advance. Moreover, SVDD performed better than the minimum covariance determinant (MCD) and isolated forest (iForest) methods in terms of describing the acceptance region in the present work. With low timeconsuming calculation, the acceptance region can be self-evolving easily and has a prospect to the edge computing for machine condition monitoring.