Predictive Maintenance – Machine Health Demo

July 11th, 2018  |  Published in Demos

ThumbMachineHealth
Structural Health Monitoring is the process of analyzing vibrations of a machine in operation to recognize anomalies early and predict outages. To apply this method, a model of the machine to be monitored is necessary – at least in normal operation, preferably vibration characteristics over its whole lifespan. Additionally, it must be feasible to monitor vibration during live operation.
A lightweight method to implement Structural Health Monitoring retrospectively (so-called Retrofitting) is provided by “bPart” sensor nodes developed by TECO. While it is only as big as a CR2023 button cell, it carries various off-the-shelf MEMS sensors and a Bluetooth Low Energy (BLE) interface. In particular, bParts also include a light sensor, a three-axis accelerometer, a temperature sensor, a battery and a multicolor-RGB-LED for user feedback. In this demo, data collected on the bPart is transmitted via BLA to a Smartphone which analyses the vibration characteristics and compares them to the given model to dynamically calculate the “Mean Time Til Failure” (MTTF).

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