Motor Control Unit With ML-Based Failure-Level Switching
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Solution Overview
Problem
Conventional motor devices require periodic inspection or replacement of motors to prevent abnormal stops of the motor control unit, leading to increased maintenance costs.
Innovation Solution
A motor control unit with a motor control block for feedback control of drive currents and a machine learning block to analyze input data and detect motor failure levels, allowing dynamic switching of control parameters or methods based on the failure level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic inspection or replacement of motor is performed, then reliability of motor control unit is improved, but maintenance cost increases
Solution Approach 1:
The machine learning block performs preliminary analysis of drive current data to detect failure levels before actual motor failure occurs. This allows advance identification of motors that need replacement, preventing abnormal stops without requiring frequent periodic inspections of all motors.
Solution Approach 2:
The motor control unit performs self-diagnosis by analyzing its own drive current data through the machine learning block. The system automatically detects failure levels and notifies when motor replacement is needed, eliminating the need for external periodic inspection services.
2Reliability
If periodic replacement of non-failed motor is performed, then reliability of motor control unit is improved, but loss of substance increases
Solution Approach 1:
The system performs self-diagnosis using the machine learning block to analyze drive current patterns and accurately identify which motors have actually failed. This prevents premature replacement of non-failed motors and reduces unnecessary loss of functional motors.
Solution Approach 2:
The patent replaces periodic mechanical inspection and replacement practices with a data-driven machine learning approach. By substituting physical inspection with intelligent analysis of electrical parameters, the system avoids unnecessary motor replacements while maintaining reliability.
3Measurement precision
If machine learning block analyzes drive current data, then accuracy of failure detection is improved, but device complexity increases
Solution Approach 1:
The machine learning block serves multiple functions: it analyzes drive current data, detects failure levels, determines replacement timing, and can dynamically adjust control parameters. This multi-functionality reduces the need for separate dedicated components for each function, thereby limiting the increase in device complexity.
Solution Approach 2:
The system changes control parameters dynamically based on detected failure levels. By adjusting parameters rather than adding complex hardware, the system achieves accurate failure detection and adaptive control while minimizing increases in physical device complexity.
Data Source
AI summary
A motor control unit (10) includes, for example, a motor control block (11) that performs feedback control of a drive current that flows through a motor (20) and a machine learning block (14) that analyzes input data including at least the drive current so as to detect a failure level of the motor (20). The motor control block (11) could be configured to dynamically switch a control parameter or a control method in accordance with the failure level. The input data may further include, for example, a drive voltage applied to the motor (20). Furthermore, the input data may further include, for example, at least one of vibrations and a temperature of the motor (20) or a motor device (1) mounting the motor (20) therein.