Even though they are the same electric motors, those used in electric vehicles, industrial facilities, and power generation are considerably expensive and can bring about greater risks in terms of cost loss and ripple effects in the event of a breakdown.

Recently, the development of a safety system that collects and monitors data through sensing of industrial motors and allows AI models to analyze the data in real time to determine abnormal signs has been in the spotlight across the industry.

The trend in motor utilization is moving away from the existing on-off control method and toward a 'smart motor' that actively controls output, implements various and precise functions using control devices, and aims for organic integration between hardware and software.

In relation to this, various companies are developing and servicing AI diagnosis and maintenance solutions for motors, and Siemens recently acquired Altair Engineering, which provides PSIM, a power electronics and motor drive simulation SW, thereby strengthening its AI-based industrial SW portfolio.

In order to become a market leader, it is expected that innovation based on digital twins and simulations will accelerate, including various edge case responses that can widely capture abnormal signs in the future, as well as improved accuracy. It is expected that the fierce competition among companies in terms of technology development and M&A will intensify in the fields of industrial motors and AI.

■ AI-based motor abnormality detection and predictive maintenance solution

Smart motors with sensor fusion can store sensing data in a central system through IoT connection. Real-time data transmission, monitoring, and remote control are performed on the system, and as AI models are installed in these systems, automation and convenience are rapidly improving.

Recently, specialized companies with AI and data analysis capabilities are creating new markets by providing advanced analysis services such as failure prediction, remaining life prediction, and operation optimization by utilizing data collected from smart motors.

Siemens offers a plug-and-play type Connection Module (CM) IOT solution that can immediately build a condition monitoring system for components such as motors and pumps. Equipped with three-axis vibration, temperature, magnetic field, and acoustic sensors, CM IOT provides condition analysis of motors and equipment through a dedicated AI model, realizing predictive maintenance.

Analog Devices (ADI) has improved motor operating efficiency with its OtoSense™ smart motor sensor solution. The solution, which enables condition-based monitoring (CbM) and predictive maintenance (PdM), is an AI-based, fully turnkey hardware-software solution.

Onepredict, a domestic company, is providing an AI-based intelligent integrated industrial asset management platform through GuardiOne PDX. Here, OnePredict is up-to-date and optimizing the detection of facility abnormalities using machine learning technology for large infrastructure facilities such as large turbines, centrifugal pumps, centrifugal compressors, and large fans.

Iresan Industry has also developed and announced an AI-based diagnosis-based dynamo system and service for improving electric motor energy efficiency. It is a diagnosis system in which AI determines abnormal signs by comparing test data with normal data, and is developing and providing a machine learning-based fault detection algorithm that detects alignment, bolt fastening, etc.

■ Motor, sensor integration and IoT connection realization

Smart motors are equipped with built-in sensors that can detect and measure various data and collect motor data in real time. △Vibration sensors △Temperature sensors △Current/voltage sensors △Speed/position sensors △Magnetic field sensors, etc. are used.

Vibration sensors can detect mechanical abnormalities such as motor rotation imbalance, bearing wear, and shaft misalignment, and acceleration sensors and gyro sensors are utilized.

Temperature sensors are used to diagnose motor overload, cooling system problems, and insulation performance degradation by measuring the temperature of motor windings, bearings, etc. There are thermistors (NTC, PTC), PT100, KTY84, bearing RTD, and winding RTD.

Magnetic field sensors are used to detect changes in the magnetic field inside the motor and diagnose abnormalities in the rotor and stator, as well as loss of synchronism.

Also, current/voltage sensors measure the current flowing through the motor and the voltage applied to it to determine the load status, efficiency, potential electrical faults, etc. Speed/position sensors are used to measure the rotation speed and precise position information of the motor in real time through encoders, resolvers, hall sensors, etc. They are essential elements for precise control of the motor.