Magnetic Encoder Self-Calibration Without Optical Encoder Hardware
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Solution Overview
Problem
The calibration cost of magnetic encoders is high due to the need for additional high-precision optical encoders, increasing hardware and time costs.
Innovation Solution
A self-calibration method for magnetic encoders involves low-pass filtering detection values, selecting reference points, calculating trimming values, and averaging over periods to create a trimming reference table without requiring an optical encoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-precision optical encoder is added to calibrate the magnetic encoder, then the calibration accuracy is improved, but the hardware cost and device complexity increase
Solution Approach 1:
The magnetic encoder performs self-calibration by using its own detection values and signal processing capabilities. The system collects detection values at different rotation positions, processes them through low-pass filtering and statistical analysis, and automatically generates calibration parameters without requiring external high-precision equipment. This self-service approach eliminates the need for additional optical encoders while maintaining calibration accuracy.
Solution Approach 2:
The patent creates a virtual reference system by collecting and analyzing multiple detection value samples at different rotation positions. Instead of using a physical high-precision optical encoder as reference, the system builds a statistical model from repeated measurements that serves as a virtual reference for calibration, thereby avoiding the need for expensive hardware copying.
2Measurement precision
If a high-precision optical encoder is added to calibrate the magnetic encoder, then the calibration accuracy is improved, but the hardware cost increases
Solution Approach 1:
The magnetic encoder performs self-calibration by using its own detection values and signal processing capabilities. The system collects detection values at different rotation positions, processes them through low-pass filtering and statistical analysis, and automatically generates calibration parameters without requiring external high-precision equipment. This self-service approach eliminates the need for additional optical encoders while maintaining calibration accuracy.
Solution Approach 2:
The patent uses multiple low-cost detection value samples collected during normal operation to build the calibration model. Instead of relying on expensive, long-lived high-precision optical encoders, the system accumulates statistical data from regular magnetic encoder readings, effectively using numerous inexpensive data points to replace costly hardware.
3Measurement precision
If multiple detection values are collected and processed through low-pass filtering and averaging, then the calibration accuracy is improved, but the time cost increases
Solution Approach 1:
The patent performs preliminary data collection and processing during normal motor operation. Detection values are accumulated and processed through low-pass filtering and averaging in advance, so that calibration parameters are ready when needed. This preliminary action allows the system to use existing operational data rather than requiring separate calibration time.
Solution Approach 2:
The calibration data collection occurs continuously during normal motor operation rather than requiring a separate calibration phase. The system accumulates detection values throughout the motor's operational life, processing them through filtering and averaging continuously, thereby converting idle operational time into productive calibration time without interrupting useful work.
Data Source
AI summary
This application belongs to the field of rotation angles detection, and provides a self-calibration method for a magnetic encoder, a motor, and a method for calibrating detection values of angles. The self-calibration method for the magnetic encoder includes: performing a low-pass filtering process on detection values θdet(ij) to obtain filtered values θfilt(ij); setting m reference points θref(n) within 360°, and selecting detection values θdet(ij-n) respectively closest to each reference point θref(n) in each period; selecting filtered values θfilt(ij-n) corresponding to θdet(ij-n); calculating trimming values θcal(i-n)=θfilt(ij-n)−θdet(ij-n); performing an averaging process on θcal(i-n) over p periods for each reference point θref(n) to obtain target trimming values θcal(n); and storing each of the reference points θref(n) and the target trimming values θcal(n) respectively corresponding to the each of the reference point in a one-to-one correspondence as a trimming reference table. The self-calibration method for a magnetic encoder provided in this application can reduce the calibration cost for a magnetic encoder.