Early Change Detection in Dynamic Machine Running Status Based on a New Stability Measure

Early change detection in dynamic running status has become an important problem in the online monitoring of industrial machineries. Existing methods, such as using mean and root mean square (rms), often produce significant false alarms in the detected results. To address this problem, this article proposes a new stability indicator based on the exponential smoothing model (ESM) and the structural graph similarity measure, in order to characterize the dynamic machine running status. The proposed stability indicator benefits from two stages, i.e., ESM and graph, and thus is advantageous in terms of computational efficiency and robustness to noise. By means of the extracted stability indicator, a common null hypothesis testing is employed to make the change decision, which does not require a prior collection of anomaly data samples. The proposed method is validated on both simulated and real scenarios. Experimental results demonstrate the outperforming performance of the method over the state-of-the-art competitors, suggesting its great potential in real applications.