Motor System Simulation Models for AI Abnormality Detection
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Solution Overview
Problem
Existing factory equipment maintenance systems lack effective methods for condition-based maintenance using artificial intelligence, particularly in simulating and detecting abnormalities in motor systems.
Innovation Solution
A simulation apparatus utilizing machine learning and a simulation apparatus that includes a model storage unit, model arithmetic unit, model setting unit, display control unit, and data storage unit, capable of simulating motor systems and performing abnormality detection using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are introduced for abnormality detection, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent creates a virtual copy of the motor system through a simulation model that replicates the physical motor's behavior. This virtual model includes simplified representations of the stator, rotor, windings, and magnets, allowing abnormality detection without requiring complex physical sensors or invasive measurements. The simulation model serves as a digital twin that can be processed by machine learning algorithms to detect abnormalities in the actual motor system.
2Measurement precision
If detailed physical models are used for simulation, then simulation accuracy is improved, but calculation time increases
Solution Approach 1:
The patent divides the motor system into distinct modular components including a stator model with windings, a rotor model with permanent magnets, and a controller model. Each component can be simulated independently and then integrated, allowing for detailed physical modeling while maintaining computational efficiency through modular architecture. This segmentation enables the system to achieve high simulation accuracy without requiring complete system re-simulation for every parameter change.
Solution Approach 2:
The patent employs parameter-based modeling where the simulation model uses adjustable parameters such as winding resistance, inductance, magnet strength, and friction coefficients to represent physical motor characteristics. By changing these parameters rather than re-simulating the entire physical model, the system can achieve detailed simulation accuracy while significantly reducing calculation time. The parameters can be updated based on actual motor conditions without requiring complex re-modeling.
Data Source
AI summary
A simulation apparatus comprises a model storage unit storing a physical model of a physical system configured to generate a physical signal waveform, a sensor model, a machine learning model, and an abnormality determination model; a model arithmetic unit configured to perform arithmetic processing using the physical model, the sensor model, the machine learning model, and the abnormality determination model; and a model setting unit configured to perform setting related to each of the physical model, the sensor model, the machine learning model, and the abnormality determination model, on the basis of an input from an operation input unit.