Predictive maintenance & condition monitoring
ADI’s in-depth knowledge in electric motors has been used to create a digital blueprint of an electric motor’s optimal behaviour, across all ranges of loads, rotation speeds, and temperatures. However, each motor once sold deviates from this initial model. Its environment, usage, and maintenance operations make it unique.
Figure 4. SMS cloud components from a secure software framework.
After installation on a motor, ADI OtoSense SMS initially acquires data to learn how this motor differs from the blueprint and creates a unique digital twin for the motor the SMS edge device is attached to. From there, OtoSense SMS compares the data coming from this motor to its digital twin running in the exact same operating conditions. If there is a substantial difference in behaviour between the physical motor and its digital twin, OtoSense SMS analyses this difference to determine the most probable root cause of this discrepancy and informs the user about the existence of a fault, its origin, and the action to perform to resolve it.
Figure 5. Multi-tenancy ensuring customer data segregation.
Figure 6. Mobile applications architecture.
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October 2025 Instrumentation Monthly
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