Abstract. Lubrication oil plays a decisive role to maintain a reliable and efficient operation of gear transmissions. Many offline methods have been developed to monitor the quality of lubricating oils. This work focus on developing a novel online method to diagnose oil degradation based on the measurements from power supply system to the gearbox. Experimental studies based on an 10kW industrial gearbox fed by a sensorless variable speed drive (VSD) shows that measurable changes in both static power and dynamic behaviour are different with lube oils tested. Therefore, it is feasible to use the static power feature to indicate viscosity changes at low and moderate operating speeds. In the meantime, the dynamic feature can separate viscosity changes for all different tested cases.
Abstract. Conventional condition monitoring techniques such as vibration, acoustic, ultrasonic and thermal techniques require additional equipment such as sensors, data acquisition and data processing systems which are expensive and complicated. In the meantime modern sensorless flux vector controlled drives can provide many different data accessible for machine control which has not been explored fully for the purpose of condition monitoring. In this paper polynomial models are employed to describe nonlinear relationships of variables available from such drives and to generate residuals for real time fault detection and performance comparisons. Both transient and steady state system behaviours have been investigated for optimal detection performance. Amongst 27 variables available from the drive, the torque related variables including motor current, I d , I q currents and torque signals show changes due to mechanical misalignments. So only these variables are explored for developing and optimising detection schemes. Preliminary results obtained based on a motor gearbox system show that the torque feedback signal, in both the steady and transient operations, has the highest detection capability whereas the field current signal shows the least sensitivity to such faults.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.