A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
1.2.1. Acoustic Signal Source-Based Fault Detection and Diagnosis
1.2.2. Vibration Signal Source-Based Fault Detection and Diagnosis
1.2.3. Other Data-Oriented Approaches for Fault Detection and Diagnosis
1.3. Contribution
2. Problem Definition
3. Methodology for Performance Evaluation
3.1. Deep Neural Network (DNN)
3.2. Data Processing
3.3. Performance Evaluation Metrics
4. Time Domain
4.1. Time-Domain Feature Extraction
4.2. Time-Domain Analysis
5. Frequency-Domain Analysis
5.1. Data Preprocessing in the Frequency Domain
5.2. Frequency-Domain Analysis Method
5.3. Frequency-Domain Feature Extraction
5.4. Analysis in the Frequency Domain
6. Results
6.1. Dataset Configuration
6.2. Experiment Results
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Main Engine Model Type | Max Power (kW) | Number of Cylinders | Normal Continuous Rating (NCR) |
---|---|---|---|
Doosan Engine G70ME-C9.2 TII (MAN B&W Licensed) | 29,120 kW | 8 | 83 RPM |
Layer | Details |
---|---|
Input layer | N nodes |
First hidden layer | 50 nodes/ReLU |
Second hidden layer | 80 nodes/ReLU |
Third hidden layer | 120 nodes/ReLU |
Fourth hidden layer | 120 nodes/ReLU |
Fifth hidden layer | 120 nodes/ReLU |
Sixth hidden layer | 80 nodes/ReLU |
Seventh hidden layer | 50 nodes/ReLU |
Output layer | 2 nodes/sigmoid |
Loss Function | Optimizer | Epoch |
---|---|---|
Cross entropy | Adam (learning rate = 1 × 104) | 4000 |
Feature | Formulas |
---|---|
(Mean-ideal RPM) | |
(Variance-ideal RPM) | |
Skewness | |
Kurtosis | |
(Max-ideal RPM) | |
(Min-ideal RPM) | |
(Median-ideal RPM) |
Acc. (%)\DS Rate | 1× | 2× | 5× | 10× | 20× | 50× | 100× |
Time | 98.99 | 96.49 | 97.37 | 96.49 | 97.37 | 61.4 | 53.48 |
Freq (max) | 85.96 | 90.36 | 89.47 | 91.23 | 92.98 | 95.61 | 86.92 |
Freq (sum) | 93.86 | 91.23 | 89.47 | 87.72 | 94.94 | 96.49 | 87.85 |
Acc. (%)\DS Rate | 1× | 2× | 5× | 10× | 20× | 50× | 100× |
Time | 98.99 | 96.49 | 97.37 | 96.49 | 97.37 | 61.4 | 53.48 |
Freq (max) | 85.96 | 90.36 | 89.47 | 91.23 | 92.98 | 95.61 | 86.92 |
Freq (sum) | 93.86 | 91.23 | 89.47 | 87.72 | 94.94 | 96.49 | 87.85 |
Time-Freq (sum) | 100.00 | 100.00 | 100.00 | 100.00 | 99.12 | 99.12 | 96.26 |
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Je-Gal, H.; Lee, S.-J.; Yoon, J.-H.; Lee, H.-S.; Yang, J.-H.; Kim, S. A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine. J. Mar. Sci. Eng. 2023, 11, 1577. https://doi.org/10.3390/jmse11081577
Je-Gal H, Lee S-J, Yoon J-H, Lee H-S, Yang J-H, Kim S. A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine. Journal of Marine Science and Engineering. 2023; 11(8):1577. https://doi.org/10.3390/jmse11081577
Chicago/Turabian StyleJe-Gal, Hong, Seung-Jin Lee, Jeong-Hyun Yoon, Hyun-Suk Lee, Jung-Hee Yang, and Sewon Kim. 2023. "A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine" Journal of Marine Science and Engineering 11, no. 8: 1577. https://doi.org/10.3390/jmse11081577