Data Fusion System for Electric Motors Condition Monitoring: An Innovative Solution

Electric motors are elementary drivers for various processes in countless applications concerning several areas of modern societies. This relevance can be mainly attributed to their high efficiency and robustness. However, due to their operating conditions, electric motors are subjected to different stresses that may lead to electromechanical damages, which, if not correctly detected, can cause irreversible failures and high repair costs. Moreover, damages in the different motor components reduce the motor efficiency since they increase machine losses. In this regard, the analysis of different machine signals has aided in diagnosing these failures; however, few systems can merge the information from relevant signals to identify a wide range of faults under different operating conditions. This article presents recent advances in the electric motor condition monitoring area, which have led to the development of a proprietary data fusion system (DFS) for automatic fault diagnosis. The DFS relies on the combined analysis of currents, stray magnetic fluxes, and infrared data, which can be measured in a noninvasive way by using simple and low-cost primary sensors. The DFS integrates conventional techniques based on stationary analysis (e.g., motor current signature analysis or MCSA) and a modern, robust methodology relying on the analysis of transient currents and fluxes, which has proven to yield high reliability for the final diagnosis while requiring a low computational burden. The article shows some results obtained when applying the system to actual machines, proving the capabilities of the developed methodology.