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In industry 4.0, predictive maintenance(PM) is one of the most importantapplications pertaining to the Internet of Things(IoT). Machine learning isused to predict the possible failure of a machine before the actual eventoccurs. However, the main challenges in PM are (a) lack of enough data fromfailing machines, and (b) paucity of power and bandwidth to transmit sensordata to cloud throughout the lifetime of the machine. Alternatively, edgecomputing approaches reduce data transmission and consume low energy. In thispaper, we propose Anomaly Detection based Power Saving(ADEPOS) scheme usingapproximate computing through the lifetime of the machine. In the beginning ofthe machines life, low accuracy computations are used when the machine ishealthy. However, on the detection of anomalies, as time progresses, the systemis switched to higher accuracy modes. We show using the NASA bearing datasetthat using ADEPOS, we need 8.8X less neurons on average and based onpost-layout results, the resultant energy savings are 6.4 to 6.65X

ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing