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Prevent costly breakdowns with predictive maintenance strategies.

Are you looking for ways to improve the reliability and efficiency of your equipment motors?

Industrial equipment, such as compressors, spindles, or water pumps, produces different vibrations during operation. Some vibrations may be perfectly normal. Others may be the sign of malfunctioning.

A typical conditioning monitoring approach would be to constantly monitor equipment using sensors, which send data to a remote server for analysis and responses.

On the other hand, predictive maintenance strategies at the edge perform data analysis and decision making locally on the edge devices. This can be especially important in situations where equipment failure could have serious consequences, such as in the case of safety-critical systems. It also can save on bandwidth and network costs since data does not need to be sent to and from the cloud, reducing the dependency on the availability and performance of this infrastructure.

Ultimately, predictive maintenance strategies aim at detecting anomalies early to avoid breakdowns.

Thanks to edge AI technology, we can recognize normal and abnormal behavioral patterns based on the information provided by sensors, allowing for real-time anomaly detection. Edge AI technology can detect the most subtle anomalies, providing more accurate and reliable results. The advanced machine learning algorithms integrated in equipment can be trained on device and instantly adapt to real-world conditions. The same algorithm can be deployed on several machines and instantly learn typical machine vibrations for identifying any anomalies.

By detecting anomalies early, edge AI can prevent costly downtimes and help companies to optimize their operations and make more informed decisions.

On-device learning allows every machine to detect anomalies accurately

With the NanoEdge AI Studio software from STMicroelectronics, embedded developers can easily create edge AI solutions, without needing specific data science skills or expertise in this field.

Embedded developers can generate an optimized machine learning library for their edge AI project in a few steps, based on a minimal amount of data.

In this predictive maintenance scenario, we can create an optimized anomaly detection model in the NanoEdge AI Studio and load it on different types of hardware. Thanks to on-device learning, the edge AI solution adapts its model to the machine.

Don’t let anomalies go undetected! Read more in our real-world use case. [LINK]

 Article by STMicreoelectronics

Pedro Mier

Pedro Mier holds a degree in Telecommunications Engineer ing from the Polytechnic University of Catalonia, MBA from ESADE and PADE from IESE. He is currently President of AMETIC (Association of Electronics, Information Technology and Telecommunications Companies of Spain), Shareholder and Chairman of the Board of Directors of TRYO Aerospace & Electronics, Board Member of the Premo Group and Committee of CTTC. member of Space Angels Network and Member of the Sc ientific Advisory