Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine
Abstract
:1. Introduction
Case Engine
2. Fault Diagnostics
Gas Turbine Diagnostics Approaches
3. Diagnostics Development
3.1. Procedure
3.2. Selected Diagnostics Set Measurements
3.3. Fault Detection and Isolation Patterns
3.4. Sensor Noise Incorporation
3.5. Data Normalization
- y is the scaled value,
- Xmax is the maximum scaling range,
- Xmin is the minimum scaling range,
- x is the value to be scaled,
- Ymax is the maximum value of the parameter to be scaled, and
- Ymin is the minimum value of the parameter to be scaled.
3.6. Tested Machine Learning Algorithms/Candidate
3.7. Fault Detection and Isolation Results and Discussion
3.7.1. Single Fault Detection and Isolation
- Pre-set Network: Neural Network
- Number of neurons in the hidden layer: 11
- Training Data Observation: 819 (75%)
- Testting Data Observation: 274 (25%)
- Training and Validation: 4-K-fold cross validation
- Training algorithm: Levenberg Marquardt
- Predictors variables: 10
- Response Classes: 13
- Activation function: Relu
3.7.2. Double Fault Detection and Isolation
- Machine learning technique: Neural Network
- Number of neurons in the hidden layer: 28
- Training Data Observation: 15,372 (75%)
- Testing Data Observation: 5124 (25%)
- Training and Validation: 4-K-fold cross-validation
- Predictors variables: 10
- Response Classes: 61
- Activation function: ReLU
3.8. Fault Identification Model Results
3.8.1. Single Fault Identification at 100% Load
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 17
- Training Data Observation: 765
- Validation Data Observation: 164
- Test Data Observation: 164
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 11
- Activation Function: ReLU
- Performance: Mean Square Error
- Data Division: Random
3.8.2. Double Fault Identification at 100% Load
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 21
- Training Data Observation: 14,346
- Validation Data Observation: 3075
- Test Data Observation: 3075
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 11
- Activation Function: ReLU
- Performance: Mean Square Error
- Data Division: Random
3.9. Comparison of Diagnostics Results with Available Literature
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
AANN | Autoassociative neural network |
AI | Artificial intelligence |
ANN | Artificial neural network |
BBN | Bayesian belief network |
CBM | Compound annual growth rate |
CC | Combustion chamber |
DCF | Double component fault |
DD | Data driven |
DL | Deep learning |
DOD | Domestic object damage |
FDI | Fault detection, isolation |
FF | Fuel flow |
FL | Fuzzy logic |
FOD | Foreign object damage |
GA | Genetic algorithm |
GPA | Gas-path analysis |
GT | Gas turbine |
HPC | High-pressure compressor |
HPT | High-pressure turbine |
LPC | Low-pressure compressor |
LPT | Low-pressure turbine |
MB | Model-based |
MLPNN | Multilayer perceptron neural network |
MRO | Maintenance repair and overhaul |
N1 | Low-pressure spool speed |
N2 | High-pressure spool speed |
P24 | Low-pressure compressor exit pressure |
P3 | High-pressure compressor exit pressure |
P43 | High-pressure turbine exit pressure |
P47 | Low-pressure turbine exit pressure |
PT | Power turbine |
SVM | Support vector machine |
T24 | Low-pressure compressor exit temperature |
T3 | High-pressure compressor exit temperature |
T5 | Power turbine exit temperature |
Appendix A. Fault Pattern
Fault Description | Fault ID | Fault Magnitude | Ambient Temperature Range | Observation |
---|---|---|---|---|
Clean | 0 | 0% | −233.15 K to 313.15 K at 4 increments | 84 |
LPC Fouling | 1 | 25%, 50%, 75% and 100% | 84 | |
HPC Fouling | 2 | 84 | ||
HPT Fouling | 3 | 84 | ||
LPT Fouling | 4 | 84 | ||
PT Fouling | 5 | 84 | ||
LPC Erosion | 6 | 84 | ||
HPC Erosion | 7 | 84 | ||
HPT Erosion | 8 | 84 | ||
LPT Erosion | 9 | 84 | ||
PT Erosion | 10 | 84 | ||
VIGV Updrift | 11 | 1.65°, 3.25°, 4.875°, 6.5° | 84 | |
VIGV Downdrift | 12 | −1.65°, −3.25°, −4.875°, −6.5° | 84 |
Fault Description | Fault ID | Fault Magnitude | Ambient Temperature Range | Observation |
---|---|---|---|---|
Clean | 0 | 0% | −233.15 K to 313.15 K at 4 increment | 336 |
LPC Fouling & HPC Fouling | 1 | 25%, 50%, 75% and 100% | 336 | |
LPC Fouling & HPC Erosion | 2 | 336 | ||
LPC Erosion & HPC Fouling | 3 | 336 | ||
LPC Erosion & HPC Erosion | 4 | 336 | ||
LPC Fouling & HPT Fouling | 5 | 336 | ||
LPC Fouling & HPT Erosion | 6 | 336 | ||
LPC Erosion & HPT Fouling | 7 | 336 | ||
LPC Erosion & HPT Erosion | 8 | 336 | ||
LPC Fouling & LPT Fouling | 9 | 336 | ||
LPC Fouling & LPT Erosion | 10 | 336 | ||
LPC Erosion & LPT Fouling | 11 | 336 | ||
LPC Erosion & LPT Erosion | 12 | 336 | ||
LPC Fouling & PT Fouling | 13 | 336 | ||
LPC Fouling & PT Erosion | 14 | 336 | ||
LPC Erosion & PT Fouling | 15 | 336 | ||
LPC Erosion & PT Erosion | 16 | 336 | ||
HPC Fouling & HPT Fouling | 17 | 336 | ||
HPC Fouling & HPT Erosion | 18 | 336 | ||
HPC Erosion & HPT Fouling | 19 | 336 | ||
HPC Erosion & HPT Erosion | 20 | 336 | ||
HPC Fouling & LPT Fouling | 21 | 336 | ||
HPC Fouling & LPT Erosion | 22 | 336 | ||
HPC Erosion & LPT Fouling | 23 | 336 | ||
HPC Erosion & LPT Erosion | 24 | 336 | ||
HPC Fouling & PT Fouling | 25 | 336 | ||
HPC Fouling & PT Erosion | 26 | 336 | ||
HPC Erosion & PT Fouling | 27 | 336 | ||
HPC Erosion & PT Erosion | 28 | 336 | ||
HPT Fouling & LPT Fouling | 29 | 336 | ||
HPT Fouling & LPT Erosion | 30 | 336 | ||
HPT Erosion & LPT Fouling | 31 | 336 | ||
HPT Erosion & LPT Erosion | 32 | 336 | ||
HPT Fouling & PT Fouling | 33 | 336 | ||
HPT Fouling & PT Erosion | 34 | 336 | ||
HPT Erosion & PT Fouling | 35 | 336 | ||
HPT Erosion & PT Erosion | 36 | 336 | ||
LPT Fouling & PT Fouling | 37 | 336 | ||
LPT Fouling & PT Erosion | 38 | 336 | ||
LPT Erosion & PT Fouling | 39 | 336 | ||
LPT Erosion & PT Erosion | 40 | 336 | ||
LPC Fouling & VIGV Updrift | 41 | 1.65°, 3.25°, 4.875°, 6.5° | 336 | |
HPC Fouling & VIGV Updrift | 42 | 336 | ||
HPT Fouling & VIGV Updrift | 43 | 336 | ||
LPT Fouling & VIGV Updrift | 44 | 336 | ||
PT Fouling & VIGV Updrift | 45 | 336 | ||
LPC Fouling & VIGV Downdrift | 46 | −1.65°, −3.25°, −4.875°, −6.5° | 336 | |
HPC Fouling & VIGV Downdrift | 47 | 336 | ||
HPT Fouling & VIGV Downdrift | 48 | 336 | ||
LPT Fouling & VIGV Downdrift | 49 | 336 | ||
PT Fouling & VIGV Downdrift | 50 | 336 | ||
LPC Erosion & VIGV Updrift | 51 | 1.65°, 3.25°, 4.875°, 6.5° | 336 | |
HPC Erosion & VIGV Updrift | 52 | 336 | ||
HPT Erosion & VIGV Updrift | 53 | 336 | ||
LPT Erosion & VIGV Updrift | 54 | 336 | ||
PT Erosion & VIGV Updrift | 55 | 336 | ||
LPC Erosion & VIGV Downdrift | 56 | −1.65°, −3.25°, −4.875°, −6.5° | 336 | |
HPC Erosion & VIGV Downdrift | 57 | 336 | ||
HPT Erosion & VIGV Downdrift | 58 | 336 | ||
LPT Erosion & VIGV Downdrift | 59 | 336 | ||
PT Erosion & VIGV Downdrift | 60 | 336 |
Appendix B. Single Fault Detection and Isolation
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 15
- Training Data Observation: 819 (75%)
- Testing Data Observation: 274 (25%)
- Training and Validation: 4-K-fold Cross-validation
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 13
- Activation Function: ReLU
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 12
- Training Data Observation: 819 (75%)
- Testing Data Observation: 274 (25%)
- Training and Validation: 4-K-fold Cross-validation
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 13
- Activation Function: ReLU
Appendix C. Double Fault Detection and Isolation
- Machine Learning Technique: Neural Network
- Number of Neurons in the Hidden Layer: 32
- Training Data Observation: 15,372 (75%)
- Training Data Observation: 5124 (25%)
- Training and Validation: 4-K-fold Cross-validation
- Predictors Variables: 10
- Response Classes: 61
- Activation Function: ReLU
- Machine Learning Technique: Neural Network
- Number of Neurons in the Hidden Layer: 30
- Training Data Observation: 15,372 (75%)
- Training Data Observation: 5124 (25%)
- Training and Validation: 4-K-fold Cross-validation
- Predictors Variables: 10
- Response Classes: 61
- Activation Function: ReLU
Appendix D. Fault Identification Pattern
NO | Fault Description | Predictor | Response |
---|---|---|---|
1 | Clean | P24 T24 P3 T3 P43 P47 T5 FF N1 N2 | All components are zero |
2 | LPC Fouling | ||
3 | HPC Fouling | ||
4 | HPT Fouling | ||
5 | LPT Fouling | ||
6 | PT Fouling | ||
7 | LPC Erosion | ||
8 | HPC Erosion | ||
9 | HPT Erosion | ||
10 | LPT Erosion | ||
11 | PT Erosion | ||
12 | VIGV Updrift | ||
13 | VIGV Downdrift |
NO | Fault Description | Predictor | Response |
---|---|---|---|
1 | Clean | P24 T24 P3 T3 P43 P47 T5 FF N1 N2 | All components are zero |
2 | LPC Fouling & HPC Fouling | ||
3 | LPC Fouling & HPC Erosion | ||
4 | LPC Erosion & HPC Fouling | ||
5 | LPC Erosion & HPC Erosion | ||
6 | LPC Fouling & HPT Fouling | ||
7 | LPC Fouling & HPT Erosion | ||
8 | LPC Erosion & HPT Fouling | ||
9 | LPC Erosion & HPT Erosion | ||
10 | LPC Fouling & LPT Fouling | ||
11 | LPC Fouling & LPT Erosion | ||
12 | LPC Erosion & LPT Fouling | ||
13 | LPC Erosion & LPT Erosion | ||
14 | LPC Fouling & PT Fouling | ||
15 | LPC Fouling & PT Erosion | ||
16 | LPC Erosion & PT Fouling | ||
17 | LPC Erosion & PT Erosion | ||
18 | HPC Fouling & HPT Fouling | ||
19 | HPC Fouling & HPT Erosion | ||
20 | HPC Erosion & HPT Fouling | ||
21 | HPC Erosion & HPT Erosion | ||
22 | HPC Fouling & LPT Fouling | ||
23 | HPC Fouling & LPT Erosion | ||
24 | HPC Erosion & LPT Fouling | ||
25 | HPC Erosion & LPT Erosion | ||
26 | HPC Fouling & PT Fouling | ||
27 | HPC Fouling & PT Erosion | ||
28 | HPC Erosion & PT Fouling | ||
29 | HPC Erosion & PT Erosion | ||
30 | HPT Fouling & LPT Fouling | ||
31 | HPT Fouling & LPT Erosion | ||
32 | HPT Erosion & LPT Fouling | ||
33 | HPT Erosion & LPT Erosion | ||
34 | HPT Fouling & PT Fouling | ||
35 | HPT Fouling & PT Erosion | ||
36 | HPT Erosion & PT Fouling | ||
37 | HPT Erosion & PT Erosion | ||
38 | LPT Fouling & PT Fouling | ||
39 | LPT Fouling & PT Erosion | ||
40 | LPT Erosion & PT Fouling | ||
41 | LPT Erosion & PT Erosion | ||
42 | LPC Fouling & VIGV Updrift | ||
43 | HPC Fouling & VIGV Updrift | ||
44 | HPT Fouling & VIGV Updrift | ||
45 | LPT Fouling & VIGV Updrift | ||
46 | PT Fouling & VIGV Updrift | ||
47 | LPC Fouling & VIGV Downdrift | ||
48 | HPC Fouling & VIGV Downdrift | ||
49 | HPT Fouling & VIGV Downdrift | ||
50 | LPT Fouling & VIGV Downdrift | ||
51 | PT Fouling & VIGV Downdrift | ||
52 | LPC Erosion & VIGV Updrift | ||
53 | HPC Erosion & VIGV Updrift | ||
54 | HPT Erosion & VIGV Updrift | ||
55 | LPT Erosion & VIGV Updrift | ||
56 | PT Erosion & VIGV Updrift | ||
57 | LPC Erosion & VIGV Downdrift | ||
58 | HPC Erosion & VIGV Downdrift | ||
59 | HPT Erosion & VIGV Downdrift | ||
60 | LPT Erosion & VIGV Downdrift | ||
61 | PT Erosion & VIGV Downdrift |
Appendix E. Single Fault Identification
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 20
- Training Data Observation: 765
- Validation Data Observation: 164
- Test Data Observation: 164
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 11
- Activation function: ReLU
- Performance: Mean Square Error
- Data Division: Random
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 19
- Training Data Observation: 765
- Validation Data Observation: 164
- Test Data Observation: 164
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 11
- Activation Function: ReLU
- Performance: Mean Square Error
- Data Division: Random
Appendix F. Single Fault Identification
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 25
- Training Data Observation: 14,346
- Validation Data Observation: 3075
- Test Data Observation: 3075
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 11
- Activation Function: ReLU
- Performance: Mean Square Error
- Data Division: Random
- Pre-set Network: Neural Network
- Number of Neurons in the Hidden Layer: 23
- Training Data Observation: 14,346
- Validation Data Observation: 3075
- Test Data Observation: 3075
- Training Algorithm: Levenberg Marquardt
- Predictors Variables: 10
- Response Classes: 11
- Activation Function: ReLU
- Performance: Mean Square Error
- Data Division: Random
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GT Physical Fault | Compressor and Turbine Performance Change Indication | Reference |
---|---|---|
Fouling | ↓ Compressor and turbine Γ ↓ Pressure ratio (PR) ↓ Compressor and turbine η | [6,7,8,9,10,11,12,13,14] |
Erosion | ↓ Compressor Γ ↑ Turbine Γ ↓ Compressor pressure ratio ↓ Compressor and turbine η | [8,15,16,17,18] |
Corrosion | ↓ Compressor Γ and η ↑ Turbine Γ ↓ Turbine η | [5,16,19,20,21] |
Blade tip clearance | ↓ Compressor and turbine η ↓ Compressor and turbine Γ | [5,22,23] |
Foreign and domestic object damage | ↓ Compressor and turbine η ↑/↓ Compressor and turbine Γ ↓ Compressor pressure ratio | [9,16,24] |
Parameters | Description |
---|---|
P24 | LPC exit pressure |
T24 | LPC exit temperature |
P3 | HPC exit pressure |
T3 | HPC exit temperature |
P43 | HPT exit pressure |
P47 | LPT exit pressure |
T5 | PT exit temperature |
FF | Fuel flow rate |
N1 | Low-pressure spool speed |
N2 | High-pressure spool speed |
Sensor | P24 | T24 | P3 | T3 | P43 | P47 | T5 | FF | N1 | N2 |
---|---|---|---|---|---|---|---|---|---|---|
±σ (%) | 0.25 | 0.4 | 0.25 | 0.4 | 0.25 | 0.25 | 0.4 | 0.5 | 0.05 | 0.05 |
No | Candidate Algorithms | Description | Category |
---|---|---|---|
1 | Fine Tree |
| Decision Trees |
2 | Medium Tree |
| |
3 | Coarse Tree |
| |
4 | Kernel Naïve Bayes |
| Naïve Bayes Classifiers |
5 | Linear Support Vector Machine |
| Support Vector Machine |
6 | Quadratic Support Vector Machine |
| |
7 | Cubic Support Vector Machine |
| |
8 | Fine Gaussian Support Vector Machine |
| |
9 | Medium Gaussian Support Vector Machine |
| |
10 | Coarse Gaussian Support Vector Machine |
| |
11 | Fine K-Nearest Neighbor |
| Nearest Neighbor Classifiers |
12 | Medium K-Nearest Neighbor |
| |
13 | Coarse K-Nearest Neighbor |
| |
14 | Cosine K-Nearest Neighbor |
| |
15 | Cubic K-Nearest Neighbor |
| |
16 | Weighted K-Nearest Neighbor |
| |
17 | Support Vector Machine Kernel |
| Kernel Approximation Classifier |
18 | Logistic Regression Kernel |
| |
19 | Boosted Trees Ensemble |
| Ensemble Classifiers |
20 | Bagged Trees Ensemble |
| |
21 | Subspace Discriminant Ensemble |
| |
22 | Subspace KNN Ensemble |
| |
23 | RUSBoosted Trees Ensemble |
| |
24 | Narrow Neural Network |
| Neural Network |
25 | Medium Neural Network |
| |
26 | Wide Neural Network |
| |
27 | Bi-layered Neural Network |
| |
28 | Tri-layered Neural Network |
|
Candidate Algorithms | Accuracy [%] | Rank |
---|---|---|
Narrow Neural Network | 98.62 | 1 |
Cubic Support Vector Machine | 98.44 | 2 |
Tri-layered Neural Network | 97.89 | 3 |
Wide Neural Network | 97.80 | 4 |
Bi-layered Neural Network | 97.80 | 5 |
Medium Neural Network | 97.71 | 6 |
Fine KNN | 97.06 | 7 |
Quadratic SVM | 96.88 | 8 |
Weighted KNN | 96.79 | 9 |
Bagged Trees | 96.70 | 10 |
Fine Gaussian SVM | 95.60 | 11 |
Subspace KNN | 95.14 | 12 |
Linear SVM | 93.95 | 13 |
Boosted Trees Ensemble | 93.77 | 14 |
Medium Gaussian SVM | 93.68 | 15 |
Medium KNN | 93.68 | 16 |
Cubic KNN | 93.40 | 17 |
SVM Kernel | 93.40 | 18 |
Cosine KNN | 92.94 | 19 |
Fine Tree | 92.85 | 20 |
Logistic Regression Kernel | 89.37 | 21 |
Coarse Gaussian SVM | 83.79 | 22 |
RUSBoosted Trees | 82.60 | 23 |
Medium Tree | 82.50 | 24 |
Subspace Discriminant | 81.59 | 25 |
Kernel naïve bays | 79.76 | 26 |
Coarse KNN | 72.25 | 27 |
Coarse Tree | 35.80 | 28 |
Algorithms Type | Accuracy [%] | Rank |
---|---|---|
Narrow Neural Network | 98.62 | 1 |
Candidate Algorithms | Accuracy [%] | Rank |
---|---|---|
Medium Neural Network | 98.12 | 1 |
Wide Neural Network | 97.31 | 2 |
Cubic SVM | 97.27 | 3 |
Fine KNN | 96.86 | 4 |
Bagged Trees | 96.64 | 5 |
Quadratic SVM | 96.27 | 6 |
Weighted KNN | 96.25 | 7 |
Narrow Neural Network | 94.41 | 8 |
Fine Gaussian SVM | 93.96 | 9 |
Subspace KNN | 93.94 | 10 |
Bilayered Neural Network | 90.55 | 11 |
Cubic KNN | 90.14 | 12 |
Medium KNN | 90.06 | 13 |
Linear SVM | 90.05 | 14 |
Cosine KNN | 89.31 | 15 |
SVM Kernel | 87.52 | 16 |
Medium Gaussian SVM | 86.04 | 18 |
Trilayered Neural Network | 79.92 | 19 |
Logistic Regression Kernel | 75.46 | 20 |
Coarse Gaussian SVM | 70.23 | 21 |
Coarse KNN | 67.67 | 22 |
Kernel Naïve Bayes | 61.99 | 23 |
Subspace Discriminant | 58.20 | 24 |
Fine Tree | 54.71 | 25 |
Boosted Trees | 38.25 | 26 |
RUSBoosted Trees | 27.29 | 27 |
Medium Tree | 26.67 | 28 |
Coarse Tree | 8.02 | 29 |
Algorithms Type | Accuracy [%] | Rank |
---|---|---|
Medium Neural Network | 98.12 | 1 |
Single Faults Detection and Isolation | Double Faults Detection and Isolation | |||||
---|---|---|---|---|---|---|
80% Load | 90% Load | 100% Load | 80% Load | 90% Load | 100% Load | |
Validation accuracy | 99.6% | 99.8% | 99.8% | 99.4% | 99.3% | 99.5% |
Test accuracy | 98.9% | 98.2% | 98.9% | 98.7% | 98.4% | 98.1% |
Single Faults Detection and Isolation | Double Faults Detection and Isolation | |||||
---|---|---|---|---|---|---|
80% Load | 90% Load | 100% Load | 80% Load | 90% Load | 100% Load | |
Training accuracy | 99.14% | 98.08% | 96..79% | 96.86% | 96.79% | 96.35% |
Validation accuracy | 98.88% | 97.87% | 96.75% | 96.82% | 96.75% | 96.31% |
Test accuracy | 97.67% | 96.77% | 96.63% | 96.74% | 96.63% | 95.98% |
All accuracy | 98.86% | 97.87% | 96.76% | 96.83% | 96.76% | 96.28% |
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Salilew, W.M.; Gilani, S.I.; Lemma, T.A.; Fentaye, A.D.; Kyprianidis, K.G. Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine. Machines 2023, 11, 832. https://doi.org/10.3390/machines11080832
Salilew WM, Gilani SI, Lemma TA, Fentaye AD, Kyprianidis KG. Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine. Machines. 2023; 11(8):832. https://doi.org/10.3390/machines11080832
Chicago/Turabian StyleSalilew, Waleligne Molla, Syed Ihtsham Gilani, Tamiru Alemu Lemma, Amare Desalegn Fentaye, and Konstantinos G. Kyprianidis. 2023. "Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine" Machines 11, no. 8: 832. https://doi.org/10.3390/machines11080832
APA StyleSalilew, W. M., Gilani, S. I., Lemma, T. A., Fentaye, A. D., & Kyprianidis, K. G. (2023). Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine. Machines, 11(8), 832. https://doi.org/10.3390/machines11080832