Predictive maintenance & condition monitoring
INCREASE PRODUCTIVITY WITH SOFTWARE DRIVEN, SECURE PREDICTIVE MAINTENANCE FOR ELECTRIC MOTORS
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By Shankar Malladi, director, Embedded Software and Product Quality, Jason Griffin, director, Technology Solutions, and Sebastien Christian, product line director, Analog Devices
n today’s factories, 14 million hours of unplanned downtime happen due to system failures in the US alone, resulting in the loss of billions of dollars. In order to prevent such events, factories typically employ an expensive route-based approach where an expert gathers data to assess the health of the equipment or utilise a range of sub-optimal sensing solutions that do not reliably detect all potential failures that could occur in these systems. Now that the Industry 4.0 (also known as the Industrial Internet of Things or IIoT) wave is well under way, industrial customers are more focused on deploying solutions that increase equipment uptime, reduce operational cost, extend equipment lifetime, and improve worker productivity. Predictive maintenance solutions combine sensing technologies to gather equipment data and employ advanced analytics and algorithms to draw actionable insights into the health of equipment. As a result, this approach is expected to increase overall industrial productivity by more than 30 per cent. One common demand is for a full turnkey wireless solution that combines hardware and software and that is easy to install and use.
Industries need a solution that does not require experts to manually gather data and/or install and maintain dedicated networks.
THE SMART MOTOR SENSOR (SMS) AND HOW IT WORKS
The Smart Motor Sensor (SMS) is a flexible, out-of-the-box, end-to-end secure wireless predictive maintenance (PdM) solution (Figure 1) that combines ADI software, hardware, and domain expertise in electric motors to create a secure scalable offering for the predictive maintenance of electric motors.
The Smart Motor Sensor (SMS) works with Android and iOS mobile applications for easy setup of the sensor, visibility on deployment data, and in-app notifications and alerts on critical events. A cloud hosted dashboard provides a complete overview of machine health diagnosis and fault detection with detailed information and visualisation of each motor’s status along with AI-based analytics to detect commonly occurring faults in electric motors.
The battery-operated SMS device combines ADI’s MEMS sensors, precision converters, and signal chains. Firmware embedded in the SMS
device captures various parameters of the motor (such as vibration, temperature, speed, and magnetic flux) and sends these data securely over a Wi-Fi connection to the back-end cloud for processing. An artificial intelligence (AI) engine that runs on the cloud and is integrated into the web application analyses the data and monitors the health of the motors.
The system can predict nine different electrical and mechanical failures that commonly occur in motors and, upon detecting one, sends out push notifications or emails to inform the user about the appropriate action to take. The Smart Motor Sensor product suite is available direct to customers as an end-to-end solution or via a REST API.
SENSOR-TO-CLOUD SOFTWARE BUILDING BLOCKS
Figure 3 illustrates ADI’s sensor-to-cloud software framework, which was leveraged to build the necessary software for the SMS solution. The sensor-to-cloud secure software framework helps to meet the desire for complete solutions, without the frustration and complexity of putting together a full system to capture
Figure 1. End-to-end predictive maintenance solution for electric motors. 58 October 2025 Instrumentation Monthly
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