Developing Health Management Strategies Using Power Constrained Hardware

Abstract

Condition based maintenance with prognostics (CBM+) is an area of research that interests many in the industrial, energy, and defense sectors. Interest in this concept is focused on lowering overall cost of operations, while also increasing equipment availability and mission readiness. Many applications, however, include power constraints and extensive lifecycle requirements that pose a challenge for existing embedded sensing systems. In some cases these systems can be expected to operate for years to decades without access to wired electricity or reliable energy harvesting sources. In this study a battery powered sensor node is presented that collects operational (pressure, acceleration, position) and environmental (temperature) information to identify and track faults seeded into an instrumented hydraulic test stand. The experimental setup is described in this paper, along with the range of baseline, damage cases, and severities imposed upon the system. Machine learning algorithms are developed specifically to leverage features that can be processed at the sensor node, then applied using low-power, computationally-limited microcontrollers. Several classifiers are considered in this analysis, including random forest and classification trees. The results discussed include prediction accuracies, training and testing requirements, as well as physical power consumption measured using actual hardware. Findings indicate that small sized random forest algorithms (up to 5 trees) can be implemented at the node and provide lower error rates; however they operate with the higher computing times and power requirements when compared to other machine learning techniques. Conversely, classification trees provide a good trade-off in accuracy and computing time, prolonging the operational life of the sensor node given a finite capacity battery as the power source.