Health Status Assessment of Passenger Ropeway Bearings Based on Multi-Parameter Acoustic Emission Analysis
Abstract
Highlights
- The health status of passenger ropeway bearings is studied using multi-parameter AE analysis.
- Resonant AE sensors outperform broadband sensors in defect detection.
- The laboratory research results have been successfully applied to field testing of passenger ropeway rolling bearings for two years.
- A novel LLM-based approach achieves automated bearing wear detection.
- Defective bearings exhibit periodic RMS peaks and elevated mean values.
- Field tests confirm AE’s effectiveness in detecting early-stage bearing damage.
- The pre-trained Paligemma LLM model demonstrates superior accuracy in wear feature identification.
- Offers a practical solution for preventive maintenance in passenger ropeway systems.
- Demonstrates the successful transition from laboratory research to practical field applications.
Abstract
1. Introduction
2. Materials and Methods
3. Results and Discussion
3.1. Result in the Laboratory
3.1.1. Waveform and Spectrum Analysis of Rolling Bearings
3.1.2. Data Analysis for AE Characteristics of Rolling Bearings
Traditional Analysis of AE Parameters
- (1)
- One-rotating-circle signals
- (2)
- Ten rotating circle signals
Statistical Analysis of AE Parameters
3.2. Result in the Field
3.2.1. Results of AE Testing on Ropeway I
AE Parameter Analysis
Statistical Analysis
3.2.2. Results of AE Testing on Ropeway II
AE Parameter Analysis
Statistical Analysis
3.3. LLM-Based Bearing Wear Detection
3.3.1. Effect of the Image Size on the LLM Model
3.3.2. Effect of the Noise Intensity on the LLM Model
3.3.3. Validation and Comparison of LLM-Based Bearing Wear Detection
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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AE Parameter | Distribution | Concentration | AE Parameter | Distribution | Concentration |
---|---|---|---|---|---|
Amp (dB) | 45~53 | 45~51 | Dur (μs) | 1~650 | 1~110 |
Energy (eu) | 1~60 | 1~35 | Rise time (μs) | 1~150 | 1~50 |
Counts | 1~95 | 1~10 | RMS (μV) | 2~3 | 2~2.5 |
AE Parameter | Distribution | Concentration | AE Parameter | Distribution | Concentration |
---|---|---|---|---|---|
Amp (dB) | 45~90 | 45~60 | Dur (μs) | 1~5500 | 1~1000 |
Energy (eu) | 1~6000 | 1~500 | Rise time (μs) | 1~350 | 1~50 |
Counts | 1~25 | 1~10 | RMS (μV) | 2~11 | 2~6 |
AE Parameter | 2022 | 2024 | AE Parameter | 2022 | 2024 |
---|---|---|---|---|---|
Amp (dB) | 45~66 | 45~62 | Dur (μs) | 1~6000 | 1~5000 |
Energy (eu) | 1~700 | 1~650 | Rise time (μs) | 1~3000 | 1~2500 |
Counts | 1~350 | 1~400 | RMS (μV) | 3~4.5 | 4~5 |
AE Parameter | 2022 | 2024 | AE Parameter | 2022 | 2024 |
---|---|---|---|---|---|
Amp (dB) | 45~62 | 45~68 | Dur (μs) | 1~7000 | 1~5000 |
Energy (eu) | 1~8000 | 1~6000 | Rise time (μs) | 1~2000 | 1~1500 |
Counts | 1~300 | 1~250 | RMS (μV) | 2~6 | 6~12 |
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Share and Cite
Zhang, J.; Shen, Y.; Wu, Z.; Shen, G.; Yuan, Y.; Hu, B. Health Status Assessment of Passenger Ropeway Bearings Based on Multi-Parameter Acoustic Emission Analysis. Sensors 2025, 25, 4403. https://doi.org/10.3390/s25144403
Zhang J, Shen Y, Wu Z, Shen G, Yuan Y, Hu B. Health Status Assessment of Passenger Ropeway Bearings Based on Multi-Parameter Acoustic Emission Analysis. Sensors. 2025; 25(14):4403. https://doi.org/10.3390/s25144403
Chicago/Turabian StyleZhang, Junjiao, Yongna Shen, Zhanwen Wu, Gongtian Shen, Yilin Yuan, and Bin Hu. 2025. "Health Status Assessment of Passenger Ropeway Bearings Based on Multi-Parameter Acoustic Emission Analysis" Sensors 25, no. 14: 4403. https://doi.org/10.3390/s25144403
APA StyleZhang, J., Shen, Y., Wu, Z., Shen, G., Yuan, Y., & Hu, B. (2025). Health Status Assessment of Passenger Ropeway Bearings Based on Multi-Parameter Acoustic Emission Analysis. Sensors, 25(14), 4403. https://doi.org/10.3390/s25144403