Prognosis and Health Management in electric drives applications implemented in existing systems with limited data rate

Abstract

Importance of the condition monitoring and predictive maintenance in motion systems is growing up as motion systems quantum and their complexity (number of axes, performance parameters) increases with increasing the automation of huge range of human activities and manufacturing processes. Probability of failures increases with the system complexity. Many faults and indication of their propagation in the electric drives would require additional sensors or hardware, higher bandwidth and sampling frequencies of feedback sensors, high computing power etc. for development of sophisticated methods to detect specific faults with good sensitivity, robustness and reliability under any operating condition. This paper presents an approach to the condition monitoring and prognosis applicable into the existing systems. These methods use the information available in the traditional electric drives-especially the information from the individual sensors in a voltage source inverter (VSI) and/or an electric motor. Condition indicators for these methods are based on application specific operating states or actions, which generates typical patterns in the signals. The condition monitoring is based on observing the deviations of these patterns between the healthy system and the system with fault propagating. The implementation strategy is described in the paper and some demonstration examples are shown as well.

Electric Drive Health Assessment

“…Table 1 below highlights the common failure or degradation mechanisms for electric drives and the cause of failure. (Klima et al 2019), (Sasidharan and Isha 2016). Despite good design and detailed preventive maintenance approach, it is difficult for operators to predict drives' time to failure due to ineffective data analysis and absence of real-time or online condition-based maintenance.…”

Pushing Mechatronic Applications to the Limits via Smart Motion Control

“…BB3 module is capable to check continuously the performance of a mechatronic device and to inform on its actual and future trends [24,25,45]. BB3 exploits measured data of vibrations and acoustic emission and provides the machine health monitoring via a set of smart algorithms.…”