Car Sensors Health Monitoring by Verification Based on Autoencoder and Random Forest Regression
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909598725701632 |
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| author | Torkhesari, Sahar Yousefimehr, Behnam Ghatee, Mehdi |
| author_facet | Torkhesari, Sahar Yousefimehr, Behnam Ghatee, Mehdi |
| contents | Driver assistance systems provide a wide range of crucial services, including closely monitoring the condition of vehicles. This paper showcases a groundbreaking sensor health monitoring system designed for the automotive industry. The ingenious system leverages cutting-edge techniques to process data collected from various vehicle sensors. It compares their outputs within the Electronic Control Unit (ECU) to evaluate the health of each sensor. To unravel the intricate correlations between sensor data, an extensive exploration of machine learning and deep learning methodologies was conducted. Through meticulous analysis, the most correlated sensor data were identified. These valuable insights were then utilized to provide accurate estimations of sensor values. Among the diverse learning methods examined, the combination of autoencoders for detecting sensor failures and random forest regression for estimating sensor values proved to yield the most impressive outcomes. A statistical model using the normal distribution has been developed to identify possible sensor failures proactively. By comparing the actual values of the sensors with their estimated values based on correlated sensors, faulty sensors can be detected early. When a defective sensor is detected, both the driver and the maintenance department are promptly alerted. Additionally, the system replaces the value of the faulty sensor with the estimated value obtained through analysis. This proactive approach was evaluated using data from twenty essential sensors in the Saipa's Quick vehicle's ECU, resulting in an impressive accuracy rate of 99\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Car Sensors Health Monitoring by Verification Based on Autoencoder and Random Forest Regression Torkhesari, Sahar Yousefimehr, Behnam Ghatee, Mehdi Artificial Intelligence Machine Learning 68T05 I.2.1 Driver assistance systems provide a wide range of crucial services, including closely monitoring the condition of vehicles. This paper showcases a groundbreaking sensor health monitoring system designed for the automotive industry. The ingenious system leverages cutting-edge techniques to process data collected from various vehicle sensors. It compares their outputs within the Electronic Control Unit (ECU) to evaluate the health of each sensor. To unravel the intricate correlations between sensor data, an extensive exploration of machine learning and deep learning methodologies was conducted. Through meticulous analysis, the most correlated sensor data were identified. These valuable insights were then utilized to provide accurate estimations of sensor values. Among the diverse learning methods examined, the combination of autoencoders for detecting sensor failures and random forest regression for estimating sensor values proved to yield the most impressive outcomes. A statistical model using the normal distribution has been developed to identify possible sensor failures proactively. By comparing the actual values of the sensors with their estimated values based on correlated sensors, faulty sensors can be detected early. When a defective sensor is detected, both the driver and the maintenance department are promptly alerted. Additionally, the system replaces the value of the faulty sensor with the estimated value obtained through analysis. This proactive approach was evaluated using data from twenty essential sensors in the Saipa's Quick vehicle's ECU, resulting in an impressive accuracy rate of 99\%. |
| title | Car Sensors Health Monitoring by Verification Based on Autoencoder and Random Forest Regression |
| topic | Artificial Intelligence Machine Learning 68T05 I.2.1 |
| url | https://arxiv.org/abs/2505.00876 |