Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases
Abstract
:1. Introduction
2. Methodology
2.1. Decomposition of Sound Frequency Signals
- Determine the partial maxima and minima of the original signal . Then, use a cubic spline to connect the maxima to form an envelope and connect the minima to form another envelope. Aggregate and average the two envelopes to obtain the mean envelope . Subtract from to obtain the vector :
- Check if meets the constraints for the IMFs. If it does, return to Step (1), and take as the original signal for the second sifting process to obtain :
- After the sifting process is repeated k times, the original signal meets the constraints and becomes the IMF vector :
- Excessive sifting eliminates the original physical meaning. Hence, the following conditions are set for convergence to ensure that the IMFs maintain the original vibration amplitude and physical meaning:
- The number of zero-crossing points must be equal to that of the partial extrema (i.e., the partial maxima and partial minima), and the standard deviation (SD) should be between 0.2 and 0.3:
- If one of the conditions is met, the sifting process is complete, and the first IMF vector is obtained. is the shortest cycle of the entire set of signals:
- Subtract from to obtain the complementary function :
- If contains a longer cycle vector, repeat steps 1–5 to continue sifting and decompose it into n (cardinal number) IMF vectors :
- If cannot be decomposed into IMF vectors, the sifting process is suspended. The final is the mean trend. All IMF vectors are aggregated with the mean trend to obtain the original signal . Combine Equations (6) and (7) to obtain
2.2. Recombination and Monitoring of Signals
2.3. Apply Hotelling T2 and Linear Discriminant Analysis to Sound Frequency Monitoring
2.4. Validation
2.4.1. Case Study I: Simulation Experiment
2.4.2. Case Study II: Electrocardiography Data
3. Results and Discussion
3.1. Case Study I
3.2. Case Study II
4. Conclusions
- The sound and vibration frequency signals were complex and unstable, but EMD removed unexpected signals and detected abnormal ones. Even when abnormal frequencies were placed at different time points, EMD was more effective than directly converting the original signal into an MSE profile. This demonstrates that monitoring a specific sound frequency indeed improves the identification of abnormal sound frequencies. The proposed method could also be applied to ECG signals.
- Good model fitting was obtained by converting the MSE profile into a fourth-order polynomial model. Although a complex model has a better fit than the third-order polynomial model, the latter is still advantageous owing to its fewer parameters and highly accessible LDA classification structure.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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3-order polynomial model | 0.9241 |
4-order polynomial model | 0.9603 |
5-order polynomial model | 0.9594 |
1-sine model | 0.8513 |
2-sine model | 0.9214 |
3-sine model | 0.9011 |
Three-sectioned 2-stage polynomial model | 0.9712 |
Different Types of Abnormal Sound Frequency | Transforming Pattern | Accuracy |
---|---|---|
Abnormal sound for high frequency | Construct the profile to use EMD procedure via the LDA classification of high frequency | 96.28% |
Construct the profile to use original signal via the LDA classification of high frequency | Unidentifiable | |
Abnormal sound for intermediate frequency | Construct the profile to use EMD procedure via the LDA classification of intermediate frequency | 94.48% |
Construct the profile to use original signal via the LDA classification of intermediate frequency | Unidentifiable | |
Abnormal sound for low frequency | Construct the profile to use EMD procedure via the LDA classification of low frequency | 95.15% |
Construct the profile to use original signal via the LDA classification of low frequency | Unidentifiable |
Sample | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
0.9902 | 0.9886 | 0.9903 | 0.9910 | 0.9874 | 0.9897 | 0.9911 | 0.9901 | 0.9788 | 0.9888 | |
0.9872 | 0.9813 | 0.9869 | 0.9896 | 0.9813 | 0.9815 | 0.9897 | 0.9899 | 0.9704 | 0.9817 | |
Sample | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
0.9909 | 0.9910 | 0.9913 | 0.9874 | 0.9901 | 0.9895 | 0.9899 | 0.9885 | 0.9913 | 0.9905 | |
0.9889 | 0.9887 | 0.9896 | 0.9827 | 0.9891 | 0.9808 | 0.9832 | 0.9803 | 0.9881 | 0.9843 | |
Sample | 21 | 22 | 23 | 24 | ||||||
0.9889 | 0.990 | 0.9876 | 0.9914 | |||||||
0.9834 | 0.9811 | 0.9809 | 0.9897 |
Type of Signals | Profile Transformation | Accuracy Rate |
---|---|---|
Normal ECG signals | Profile transformation using EMD | 97.92% |
Profile transformation using original signal | 71.42% | |
Irregular heartbeat | Profile transformation using EMD | 92.20% |
Profile transformation using original signal | 65.52% |
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Jen, C.-H.; Wang, C.-C. Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases. Processes 2021, 9, 1510. https://doi.org/10.3390/pr9091510
Jen C-H, Wang C-C. Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases. Processes. 2021; 9(9):1510. https://doi.org/10.3390/pr9091510
Chicago/Turabian StyleJen, Chih-Hung, and Chien-Chih Wang. 2021. "Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases" Processes 9, no. 9: 1510. https://doi.org/10.3390/pr9091510
APA StyleJen, C.-H., & Wang, C.-C. (2021). Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases. Processes, 9(9), 1510. https://doi.org/10.3390/pr9091510