Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles
Abstract
:1. Introduction
2. Methodology
2.1. Data Source
2.2. Data Preprocessing
2.3. Data Analysis
2.3.1. K-Means Implementation
2.3.2. SOM Implementation
2.3.3. Linear Discriminant Analysis
2.3.4. PCA Implementation
3. Results and Discussion
3.1. Whole Dataset Fuel Economy and Emissions
3.2. Clustered Dataset Fuel Economy and Emissions
3.2.1. K-Means Clustering
3.2.2. Self-Organizing Maps Clustering
3.3. Performance of K-Means and SOM Clustering
3.4. Bivariate Analysis on CO vs. Fuel Economy
3.5. Other Notable Variable Correlations
3.6. Unsupervised Learning Uncovers an Impending Anomaly
3.7. CO vs. Fuel Economy Anomaly in the Big Picture of LDVs Market
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Description | Data Type |
---|---|---|
Fuel Economy | Fuel economy in miles per gallon (MPG) | Numeric, continuous |
Displacement | Engine volume displacement in liters (L) | Numeric, continuous |
PWR | Power-to-Weight ratio of the vehicle in horsepower/pound (hp/lb) | Numeric, continuous |
Axle Ratio | The number of revolutions the output shaft or driveshaft needs to make to spin the axle one complete turn | Numeric, continuous |
THC | Exhaust total hydrocarbons (THC) in grams/mile (g/mi) | Numeric, continuous |
CO2 | Exhaust carbon dioxide in grams/mile (g/mi) | Numeric, continuous |
CO | Exhaust carbon monoxide in grams/mile (g/mi) | Numeric, continuous |
NOx | Exhaust NOx in grams/mile (g/mi) | Numeric, continuous |
Actual | Predicted Count | Predicted Rate | ||||
---|---|---|---|---|---|---|
Cluster | 1 | 2 | 3 | 1 | 2 | 3 |
1 | 1270 | 87 | 0 | 0.936 | 0.064 | 0.000 |
2 | 26 | 2115 | 2 | 0.012 | 0.986 | 0.001 |
3 | 2 | 0 | 76 | 0.038 | 0.000 | 0.962 |
Total Count: 3580 | Percent Misclassified: 3.32% | Entropy R-square: 0.848 |
Actual | Predicted Count | ||||||
Cluster | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
1 | 63 | 0 | 0 | 2 | 0 | 1 | 0 |
2 | 0 | 800 | 24 | 7 | 25 | 7 | 15 |
3 | 0 | 93 | 913 | 0 | 105 | 1 | 0 |
4 | 0 | 45 | 0 | 476 | 47 | 0 | 4 |
5 | 0 | 71 | 7 | 0 | 399 | 0 | 1 |
6 | 0 | 8 | 0 | 17 | 1 | 100 | 0 |
7 | 0 | 3 | 30 | 0 | 13 | 0 | 304 |
Actual | Predicted Rate | ||||||
Cluster | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
1 | 0.955 | 0.000 | 0.000 | 0.030 | 0.000 | 0.015 | 0.000 |
2 | 0.000 | 0.911 | 0.027 | 0.008 | 0.028 | 0.008 | 0.017 |
3 | 0.000 | 0.084 | 0.821 | 0.000 | 0.094 | 0.001 | 0.000 |
4 | 0.000 | 0.079 | 0.000 | 0.832 | 0.082 | 0.000 | 0.007 |
5 | 0.000 | 0.149 | 0.015 | 0.000 | 0.834 | 0.000 | 0.002 |
6 | 0.000 | 0.063 | 0.000 | 0.135 | 0.008 | 0.794 | 0.000 |
7 | 0.000 | 0.009 | 0.086 | 0.000 | 0.037 | 0.000 | 0.869 |
Total Count: 3580 | Percent Misclassified: 14.72% | Entropy R-Square: 0.753 |
Test Procedure Code | Test Procedure Description |
---|---|
2 |
|
3 |
|
11 |
|
21 |
|
31 |
|
90 |
|
95 |
|
Fuel Type Code | Fuel Type Description |
---|---|
19 |
|
26 |
|
27 |
|
30 |
|
38 |
|
61 |
|
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Share and Cite
Fortela, D.L.B.; Fremin, A.C.; Sharp, W.; Mikolajczyk, A.P.; Revellame, E.; Holmes, W.; Hernandez, R.; Zappi, M. Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles. Clean Technol. 2023, 5, 418-435. https://doi.org/10.3390/cleantechnol5010021
Fortela DLB, Fremin AC, Sharp W, Mikolajczyk AP, Revellame E, Holmes W, Hernandez R, Zappi M. Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles. Clean Technologies. 2023; 5(1):418-435. https://doi.org/10.3390/cleantechnol5010021
Chicago/Turabian StyleFortela, Dhan Lord B., Ashton C. Fremin, Wayne Sharp, Ashley P. Mikolajczyk, Emmanuel Revellame, William Holmes, Rafael Hernandez, and Mark Zappi. 2023. "Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles" Clean Technologies 5, no. 1: 418-435. https://doi.org/10.3390/cleantechnol5010021