Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings
Abstract
:1. Introduction
- Interturn short-circuits (ITSC);
- Short-circuits between the coils in one phase;
- Phase-to-ground short-circuits;
- Phase-to-phase short-circuits.
- Fault diagnosis based on mathematical models;
- Fault diagnosis based on analysis (processing) of diagnostic signals;
- Fault diagnosis based on artificial intelligence (AI) techniques.
- (1)
- Development of a printed circuit board with LESR 6-NP current transducers for measuring the PMSM stator phase currents, which are used as diagnostic signals.
- (2)
- A detailed description of the process of configuration of the stator phase current signal acquisition and processing using a low-cost microcontroller.
- (3)
- Detailed analysis of the effect of ITSCs in the PMSM stator winding on the waveform and FFT spectrum of the stator phase current space vector module.
- (4)
- Implementation of an ML-based method (KNN model), for condition monitoring and classification of PMSM stator winding faults, on a low-cost microcontroller.
- (5)
- Preparation of the concept and experimental verification of the low-cost microcontroller-based system for condition monitoring of the PMSM stator windings.
- (6)
- Critical analysis of the effectiveness of the developed low-cost PMSM stator winding condition monitoring system.
2. Key Parameters of the Developed Stator Phase Current Measurement PCB
3. Experimental Setup Components and Configuration of the Data Acquisition
3.1. Development Board and Microcontroller Used
3.2. Motor Test Bench
3.3. Configuration and Verification of the Stator Phase Current Signals Measurement and Acquisition
4. PMSM Stator Winding Fault Diagnosis Method and Results
4.1. PMSM Stator Winding Fault Symptom Extraction
4.2. Automation of PMSM Stator Winding Fault Diagnosis
4.2.1. K-Nearest Neighbors
- xi, yi—elements of the A and B feature vectors, respectively;
- n—feature space dimension.
Algorithm 1: The pseudocode of the KNN algorithm |
Data: D = {Xi,ci}, for i = 1 to N, where X i = (x1, x2…, xm) is an m-element input vector that belongs to class ci, N is the number of elements contained in the dataset. Data: A = (a1, a2…, am) new data to be classified Result: class to which the new input vector A belongs Initialize distances[N] ← {0}; for Xi in D do di ← d(Xi, A); distances [i] ← di; end Sort distances {di, for i = 1 to N} in ascending order; Get the first K cases closer to A (with the smallest distance), ; class ← most frequent class in ; |
4.2.2. Results
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Name of the Parameter | Symbol | Units | |
---|---|---|---|
Power | PN | 2500 | [W] |
Torque | TN | 16 | [Nm] |
Speed | nN | 1500 | [r/min] |
Stator phase voltage | UsN | 325 | V |
Stator current | IsN | 6.6 | [A] |
Frequency | fsN | 100 | [Hz] |
Pole pairs number | pp | 4 | [−] |
Number of stator turns | Nst | 2 × 125 | [−] |
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Parameter | Value |
---|---|
Supply voltage | 4.75…5.25 V |
Bandwidth (±1dB) | 300 kHz |
Primary current, measuring range | ±20 A |
Creepage/Clearance distance | 7.55 mm |
Reference voltage = Common mode Voltage UEref range | 0.5…2.75 V |
Output voltage UOut | 0.25…4.75 V |
Sensitivity SN | 104.2 mV/A |
Maximum error | 0.45% |
Feature | Details |
---|---|
Core | ARM Cortex-M7 (32-bit) |
Operating clock frequency | Up to 280 MHz |
FPU | Double-precision |
Flash memory | 2 Mbytes |
SRAM memory | 1.4 Mbytes |
DMA | 5 × 16-channel |
ADC | 2 × 16-bit |
DAC | 3 × 12-bit |
Timers | 2 × 32 bit, 15 × 16 bit |
Communication peripherals | 4 × I2C, 5 × USART, 6 × SPI, 2 × CAN, 2 × SAI |
Measuring PCB Pin ID | Measuring PCB Pin Function | µC Pin ID | µC Pin Function |
---|---|---|---|
+3.3 V | 3.3 V supply input | 3V3 | 3.3 V supply output |
+5 V | 5 V supply input | 5V | 5 V supply output |
OUT_U | LEM’s output voltage proportional to the current in phase U (A) | PA7 | ADC2 module input of channel 7 (ADC2_INP7) |
OUT_V | LEM’s output voltage proportional to the current in phase V (B) | PA6 | ADC2 module input of channel 3 (ADC2_INP3) |
OUT_W | LEM’s output voltage proportional to the current in phase W (C) | PF14 | ADC2 module input of channel 6 (ADC2_INP6) |
GND | Ground | GND | Ground |
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Pietrzak, P.; Wolkiewicz, M.; Kotarski, J. Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings. Electronics 2024, 13, 2975. https://doi.org/10.3390/electronics13152975
Pietrzak P, Wolkiewicz M, Kotarski J. Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings. Electronics. 2024; 13(15):2975. https://doi.org/10.3390/electronics13152975
Chicago/Turabian StylePietrzak, Przemyslaw, Marcin Wolkiewicz, and Jan Kotarski. 2024. "Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings" Electronics 13, no. 15: 2975. https://doi.org/10.3390/electronics13152975
APA StylePietrzak, P., Wolkiewicz, M., & Kotarski, J. (2024). Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings. Electronics, 13(15), 2975. https://doi.org/10.3390/electronics13152975