Abstract
Cloud-based prognostics and health management is a centralized method for monitoring the condition of individual shared vehicles and determining their maintenance schedules.In this study, we focused on monitoring the condition of brake pads and tires, as these crucial components require frequent and regular maintenance for safety. We developed a data acquisition system to transmit data from acoustic and vibration sensors to the cloud server. Useful and efficient features were extracted and selected from time and frequencydomains to assess the degradation of brake pads and tires. Moreover, based on feature extraction using the KruskalWallis method, we confirmed that diagnosing brake pad conditions with support vector machines (SVM) provides consistent result for classification of sevierities.. Our preliminary results suggest that cloud-based conditionmonitoring can be an effective approach to managing shared vehicles.
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