Many manufacturing, warehouse and distribution facilities focus on condition monitoring or predictive maintenance solutions for the larger motors, drives and gearboxes but fail to take into consideration mission-critical equipment like smaller motors, bearings, rollers, conveyors, water pumps, etc. By and large, the maintenance for these happens on an inefficient, planned schedule at best, or only after the damage is done, at worst. An unplanned motor failure can cause a line-down situation ranging anywhere from a couple of hours to several days, resulting in downtime. While unplanned maintenance is more disruptive and expensive than routine scheduled maintenance, the best condition monitoring solution is one which predicts failure or presents the deterioration rate for any equipment that has the potential to cause downtime, in time to react. Bluvision’s Advanced Condition Monitoring uses machine learning and Artificial Intelligence (A.I.) to predict failure on any motor or mechanical equipment, weeks or months before the failure happens.
Predicting the future begins with history
The sensors in BEEKs Industrial BLE beacon, collects historical vibration data and establishes a motion fingerprint of each individual motor or mechanical device. These fingerprints are collected over a short period of time where the monitored asset is operated normally and is run through all possible stages of operation. (Eg: Motor off/motor at low speed/motor at high speed, etc.)
Recognising the patterns
Bluvision’s Condition Monitoring is based on multiple events and not just when a single anomaly is detected. While evaluating the new RMS and peak-to-peak values against the training stage, policies and alerts can be created for:
- When the actual motion of a motor is different than its fingerprint. Alerts are created when the new value exceeds the modeled values.
- When the motion level is growing – trend over time. Alerts are created when there is a trend line of exceeding values over time.
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How we do it differently
Bluvision’s solution, apart from being equipment agnostic, requires minimal hardware – sensor beacons to mount on the equipment and BluFi – WiFi gateways. Each gateway can manage hundreds of sensor beacons concurrently. Our Bluzone cloud allows for user-defined alerts and for fleet management so users can check status and health of thousands of beacons at the same time.
Our Condition Monitoring solution studies the motion in all 3-axis – x, y and z. More precisely, we use RMS (Root Mean Square), peak-to-peak, which provides the entire range of motion. The machine learning calculations are performed within the individual beacons with only peak-to-peak data (Low speed @ 10 Hz and high speed @ 800 Hz) transmitted to the cloud, thereby saving battery and ensuring the user doesn’t have to go through tons of unnecessary data to analyze and detect an anomaly.
Predict the failure of motors or any mechanical equipment
Most importantly Bluvision’s equipment agnostic solution is designed to understand the motion and predict the failure of any mechanical device providing factories and enterprises the ability to optimize operational and process flow with avoidable downtime.