AI-based predictive maintenance

| Information and Communication Technology

AI-based analytics interrogate machine monitoring sensors

Artificial Intelligence based analytics system links sensors for monitoring machines to predict maintenance requirements.

Engineering company Quartzelec has partnered with Canadian NanoPrecise to deploy AI and IoT based analytics systems from NanoPrecise to diagnose machine-health by recording the real-time RPM, sound, vibration, temperature, humidity, magnetic flux, and other key parameters to detect anomalies in near real time, in-order to predict the Remaining Useful Life (RUL) of machine components. The system empowers user/operators to carefully manage operational decisions that may cause major damage or unplanned downtime.

The company’s Lifeview system also offer specific monitoring capabilities for large, high voltage motors and generators, for those in critical drive positions or remote locations.

When fitted, the NanoPrecise sensors can detect and report even small changes in machine operation, before any impact to operation and production occurs. Interconnectivity is provided by either encrypted WiFi, built around 802.11 protocols, or for more remotely located machines via secure cellular and Bluetooth technology.

According to Paul Oliver of Quartzelec, Lifeview is a key component in reducing total cost of ownership over the lifetime of a machine and an integral element of any reliability centred maintenance (RCM) and repair strategy.

“By focusing on early detection, our customers can cost effectively implement preventative maintenance and so avoid costly and sometimes catastrophic machine failures,” he concludes.

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