Multiple regression analysis for change detection in multi-sensory monitoring data with application to induction motor speed condition monitoring

摘要

Multi-sensory configuration enables the collection of comprehensive information relating to the operating condition of machinery in use. However, the complex properties of multi-sensory monitoring data create serious challenges for data modelling and analysis. This paper presents a novel framework, based on multiple regression analysis (MRA), for the on-line monitoring of induction motors in order to detect changes in motor rotational speed during the course of successive operations. To utilize the synergistic information of multiple sensors, a prediction model is established based on MRA. By virtue of this model, the residual between model output and sensor observation is defined as a dynamic stability indicator, for the purpose of characterising the running status of a motor. A commonly used testing hypothesis is performed, based on the use of such an indicator for decision-making. As such, the motor speed condition can be inspected in a continuous manner on-line. The proposed approach is applied to both simulated and real-life engineering applications, where encouraging results have been demonstrated. Moreover, a comparison of this approach with representative and competing methods suggests its great potential for industry applications.