The Untilisation of Information Available in a Sensorless Control System of an AC Induction Motor for Condition Monitoring

Induction motor driven mechanical transmission systems are widely utilised in many applications across numerous sectors including industry, power generation and transportation. They are however subject to common failure modes primarily associated with faults in the driven mechanical components. Notably, gearboxes, couplings and bearings can cause significant defects in both the electrical and mechanical systems. Condition monitoring (CM) undertakes a key role in the detection of potential defects in the early development stages and in turn avoiding catastrophic operational and financial consequences caused by unplanned breakdowns. Meanwhile, variable speed drives (VSDs) have been increasingly deployed in recent years to achieve accurate speed control and higher operational efficiency. Among the different speed control designs, sensorless VSDs deliver improved dynamic performance and obviate speed measurement devices. This solution however results in heightened noise levels and continual changes in the power supply parameters that potentially impede the detection of minute fault features. This study addresses the gap identified through a systematic review of the literature on the monitoring of mechanical systems utilise induction motors (IM) with sensorless VSDs. Specifically, existing techniques prove ineffective for common mechanical faults that develop in gearboxes and friction induced scenarios. The primary aim of this research centres on the development of a more effective and accurate diagnostic solution for VSD systems using the data available in a VSD. An experimental research approach is based to model and simulate VSD systems under different fault conditions and gather in-depth data on changes in electrical supply parameters: current, voltage and power. Corresponding techniques including model based methods and dynamic signature analysis methods were developed for extracting the changes from noise measurements. An observer based detection technique is developed based on speed and flux observers that are deployed to generate power residuals. Both static and dynamic techniques are
incorporated for the first time in order to detect the mechanical misalignment and lubrication degradation, each with different degrees of severities. The results of this study demonstrate that observer based approaches utilising power residual signalling can be effective in the identification of different faults in the monitoring of sensorless VSD driven mechanical systems. Specifically, the combination between dynamic and static components of the power supply parameters and control data has proved effective in separating the four types of common faults: shaft misalignment, lubricant shortage, viscosity changes and water contamination. The static data based approach outperforms the dynamic data based approach in detecting shaft misalignments under sensorless operating modes. The dynamic components of power signals, however, records superior results in the detection of different oil degradation problems. Nevertheless, static components of torque related variables, power and voltage can be used jointly in separating the three tested lubricant faults.