A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data
Abstract
1. Introduction
1.1. Research Background and Motivation
1.2. Related Work
1.3. Research Objectives
2. Methodology
2.1. Denoising Autoencoder (DAE)
2.2. Feature Extraction
2.2.1. Mean
2.2.2. Root Mean Square (RMS)
2.2.3. Standard Deviation (STD)
2.2.4. Kurtosis
2.2.5. Skewness
2.3. One-Class Support Vector Machine
3. Preliminary Study
3.1. Dataset
3.2. Result of Noise Reduction
3.3. Result of Feature Extraction
3.4. Result of Classification
4. Case Study
4.1. Result of Noise Generation
4.2. Result of Noise Reduction (Highpass)
4.3. Result of Feature Extaction
4.4. Result of Classification (Highpass)
4.5. Final Comparative Analysis of Results
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Gaussian Noise | Actual Answer (Unit: Case) | Running Rate | Failure Rate | |||
---|---|---|---|---|---|---|
Normal | Failure | |||||
Classification results by feature | Mean | Normal | 7303 | 497 | 93.63% | 100% |
Failure | 0 | 7800 | ||||
RMS | Normal | 7292 | 508 | 93.49% | 100% | |
Failure | 0 | 7800 | ||||
Standard Deviation | Normal | 7300 | 500 | 93.59% | 98.69% | |
Failure | 102 | 7698 | ||||
Skewness | Normal | 7313 | 487 | 93.76% | 95.77% | |
Failure | 330 | 7470 | ||||
Kurtosis | Normal | 7254 | 546 | 93% | 26.22% | |
Failure | 5755 | 2045 |
Wavelet Transform | Actual Answer (Unit: Case) | Running Rate | Failure Rate | |||
---|---|---|---|---|---|---|
Normal | Failure | |||||
Classification results by feature | Mean | Normal | 7565 | 235 | 96.99% | 1.41% |
Failure | 7690 | 110 | ||||
RMS | Normal | 7066 | 733 | 90.6% | 9.63% | |
Failure | 7049 | 751 | ||||
Standard Deviation | Normal | 7177 | 683 | 91.24% | 51.86% | |
Failure | 3755 | 5045 | ||||
Skewness | Normal | 7307 | 493 | 93.68% | 10.92% | |
Failure | 6948 | 852 | ||||
Kurtosis | Normal | 7323 | 477 | 93.88% | 11.63% | |
Failure | 6893 | 907 |
High-Pass Noise | Actual Answer (Unit: Case) | Running Rate | Failure Rate | |||
---|---|---|---|---|---|---|
Normal | Failure | |||||
Classification results by feature | Mean | Normal | 7331 | 469 | 94% | 100% |
Failure | 0 | 7800 | ||||
RMS | Normal | 7173 | 627 | 92% | 100% | |
Failure | 0 | 7800 | ||||
Standard Deviation | Normal | 7029 | 771 | 90.1% | 100% | |
Failure | 0 | 7800 | ||||
Skewness | Normal | 7331 | 469 | 94% | 68.6% | |
Failure | 2453 | 5347 | ||||
Kurtosis | Normal | 7411 | 389 | 95% | 99.5% | |
Failure | 37 | 7763 |
Gaussian Noise | Precision | Recall | F1-Score | F1-Score Average | |
---|---|---|---|---|---|
Feature | Mean | 0.936 | 1 | 0.967 | 0.9074 |
RMS | 0.935 | 1 | 0.966 | ||
Standard Deviation | 0.936 | 0.986 | 0.96 | ||
Skewness | 0.938 | 0.957 | 0.947 | ||
Kurtosis | 0.93 | 0.558 | 0.697 |
Wavelet Transform | Precision | Recall | F1-Score | F1-Score Average | |
---|---|---|---|---|---|
Feature | Mean | 0.986 | 0.496 | 0.66 | 0.6456 |
RMS | 0.904 | 0.501 | 0.644 | ||
Standard Deviation | 0.587 | 0.3657 | 0.62 | ||
Skewness | 0.896 | 0.513 | 0.652 | ||
Kurtosis | 0.89 | 0.515 | 0.652 |
High-Pass Noise | Precision | Recall | F1-Score | F1-Score Average | |
---|---|---|---|---|---|
Feature | Mean | 0.94 | 1 | 0.97 | 0.9367 |
RMS | 0.92 | 1 | 0.958 | ||
Standard Deviation | 0.901 | 1 | 0.948 | ||
Skewness | 0.94 | 0.749 | 0.834 | ||
Kurtosis | 0.95 | 0.995 | 0.972 |
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Jang, J.-g.; Lee, S.-s.; Hwang, S.-Y.; Lee, J.-c. A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data. Appl. Sci. 2025, 15, 6523. https://doi.org/10.3390/app15126523
Jang J-g, Lee S-s, Hwang S-Y, Lee J-c. A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data. Applied Sciences. 2025; 15(12):6523. https://doi.org/10.3390/app15126523
Chicago/Turabian StyleJang, Jun-gyo, Soon-sup Lee, Se-Yun Hwang, and Jae-chul Lee. 2025. "A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data" Applied Sciences 15, no. 12: 6523. https://doi.org/10.3390/app15126523
APA StyleJang, J.-g., Lee, S.-s., Hwang, S.-Y., & Lee, J.-c. (2025). A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data. Applied Sciences, 15(12), 6523. https://doi.org/10.3390/app15126523