Process 100x less data using analog edge processing for predictive and preventative maintenance
The Aspinity analogMLTM core delivers a more intelligent, lower-power, always-on edge processing strategy for handling the massive amounts of vibration data that are collected for accurate predictive maintenance and condition monitoring of many different types of equipment such as:
Current vibration monitoring systems digitize and process 1000’s of FFT datapoints to see if a failure has occurred, but in fact, many types of faults can be detected using just 10’s of relevant datapoints (e.g. fault-frequency/energy pairs, RMS levels, etc.). The analogML core is able to extract those important datapoints directly from raw analog sensor data and continuously monitor for faults at near-zero power. When the first sign of a failure is indicated, the analogML core wakes up the downstream digital system so additional actions can be taken, such as performing a detailed FFT for a more deeply informed decision or requesting that maintenance staff are sent to the field.
By intelligently monitoring the most important datapoints while they are still analog and waking the higher-power system only when failures are detected, the analogML core keeps the power-on and processing time of the digital system to a minimum, significantly extending the battery life of remote sensor nodes used for vibration monitoring.
Contact us for more information and to discuss how analogML can improve the power and data efficiency in your device