Hardware-Efficient System State Detection for Embedded Condition Monitoring

Abstract

Condition Monitoring of devices and control systems allows for a decrease in maintenance costs while requiring less computational effort than machine learning methods. To provide the option to include a monitoring circuit on a sensor or small edge device, and thus further lower computational and communication overhead, this work presents a minimized hardware implementation of a condition monitoring algorithm called TCAM and compares it to a more traditional implementation of the algorithm. Furthermore, a trade-off analysis examines the effect of sample bit widths on gate count and accuracy. For the selected case study, the gate count has been decreased by 57 % and the needed memory size by roughly 86 % while still yielding system state detection results at the same level of accuracy. For an example hydraulic monitoring application with four input signals monitored and a 9 bit numeric representation delivering reasonable accuracy, TCAM can be implemented with 1918 logic gates and 3092 bit RAM.