Synchronous Motor Fault Detection via Sensor Ground Loss
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Solution Overview
Problem
Existing fault detection methods for synchronous motors in motor vehicles fail to reliably detect the loss of connection between rotation angle sensors and ground, leading to incorrect operation assumptions and potential safety hazards.
Innovation Solution
A method and motor controller that ascertain rotation angle data and additional data, such as torque requirements and speed changes, to detect faults by meeting specific criteria, including constant rotation angle data and predetermined difference or rate of change thresholds, thereby identifying the loss of connection between the rotation angle sensor and ground.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing fault detection methods are used for synchronous motors, then the system structure remains simple, but the reliability of fault detection is insufficient and connection loss cannot be reliably detected
Solution Approach 1:
The motor controller uses its existing processing unit and available sensor data (rotation angle, torque, speed) to perform fault detection without requiring separate detection hardware. The system serves itself by utilizing already-present components and data sources to achieve reliable fault detection.
Solution Approach 2:
The processing unit of the motor controller performs multiple functions: normal motor control and fault detection. By making the control unit universal, the patent avoids adding separate detection equipment while improving reliability through integrated multi-functional operation.
2Measurement precision
If additional hardware is added to improve fault detection capability, then the measurement precision and reliability improve, but the device complexity and cost increase
Solution Approach 1:
The system uses existing sensors and processing capabilities to achieve precise fault detection without additional measurement hardware. The processing unit analyzes data already being collected for motor control, eliminating the need for separate detection equipment.
Solution Approach 2:
The patent replaces potential additional hardware-based detection systems with a software/algorithm-based approach using the existing processing unit. This substitution of physical detection equipment with computational analysis maintains precision while avoiding hardware complexity.
3Reliability
If the rotation angle sensor connection is lost, then the sensor cannot detect rotor position, but existing methods cannot distinguish this from normal operation variations
Solution Approach 1:
The system continuously monitors the relationship between rotation angle data, torque requirements, and speed. By establishing feedback loops that check for inconsistencies between these parameters, the system can detect when the rotation angle sensor connection is lost, as the rotation angle data will show abnormal patterns compared to expected operational relationships.
Solution Approach 2:
The patent establishes predetermined criteria and thresholds for normal operation before faults occur. When connection loss happens, the system compares real-time data against these pre-established benchmarks and immediately identifies deviations, preventing incorrect operation assumptions by having reference values ready in advance.
4Measurement precision
If fault detection criteria are made more stringent to improve accuracy, then the measurement precision improves, but the productivity and response time may decrease
Solution Approach 1:
The system performs a focused analysis on specific key parameters (rotation angle consistency, torque-speed relationship) rather than examining all possible operational variables. This partial action approach maintains high detection accuracy by concentrating computational resources on the most diagnostic parameters while avoiding the time cost of comprehensive analysis.
Solution Approach 2:
The patent transforms multiple operational parameters into a simplified fault assessment by comparing rotation angle data against predetermined thresholds and relationships. This parameter transformation converts complex multi-variable analysis into straightforward threshold comparisons, maintaining precision while enabling rapid detection.
Data Source
AI summary
A method for detecting a fault in an electrical machine, e.g., a synchronous motor, a motor control unit for controlling an electrical machine, and a motor arrangement having such a motor control unit are disclosed. According to the method, rotation angle data are determined, which data are dependent on a rotation angle of a rotor of the electrical machine in the absence of the fault. In addition, additional data are determined and allow conclusions to be drawn on the fault. A fault is detected if the rotation angle data satisfy a first criterion and the additional data satisfy a second criterion.