Predictive maintenance is extremely important to fleet owners. On-duty automobile engine failures add cost of extra towing, gas and labor expenses which can add up to millions of dollars every year. Early knowledge of upcoming failures helps reduce these expenses. Thus, companies invest considerably in fault detection and diagnosis (FDD) systems to reduce unnecessary costs. Artificial Intelligence (AI) is getting increasingly used in the data driven signal based FDD industry because it requires less labor and equipment. It also results in higher productivity since it can operate continuously. This research offers Artificial Intelligence based solutions to detect and diagnose the degradation of three Internal Combustion Engine (ICE) parts which may cause on-duty failures: lead-acid accessory battery, spark plugs, and Exhaust Gas Recirculation (EGR) valve. Since the goal behind most FDD systems is cost
reduction, it is important to reduce the cost of the FDD test. Therefore, all the FDD solutions proposed in this research are based on three types of built-in sensors: battery voltage sensor, knock sensors and speed sensor. Furthermore, the engine database, the Machine Learning (ML) and Deep Learning (DL) models, and the virtual operating machines were all stored and operated in the cloud.
In this research, eight Machine Learning (ML) and Deep Learning (DL) models are proposed to detect degradations in three vehicle parts mentioned above. Additionally, novel advanced pre-processing approaches were designed to enhance the performance of the models. All the developed models showed excellent detection accuracies while classifying engine data obtained under artificially and physically induced fault conditions. Since some variant data samples could not be detected due to experimental flaws, defective sensors and changes in temperature and humidity, novel pre-processing methods were proposed for Long Short-Term Memory Networks (LSTM-RNN) and Convolutional Neural Networks (CNN) which solved the data variability problem and outperformed the previous ML/DL models. / Thesis / Doctor of Philosophy (PhD) / Predictive maintenance is extremely important to fleet owners. On-duty automobile engine failures add cost of extra towing, gas and labor expenses which can add up to millions of dollars every year. Early knowledge of upcoming failures helps reduce these expenses. Thus, companies invest considerably in fault detection and diagnosis (FDD) systems to reduce unnecessary costs. Artificial Intelligence (AI) is getting increasingly used in the data driven signal based FDD industry because it requires less labor and equipment. It also results in higher productivity since it can operate continuously. This research offers Artificial Intelligence based solutions to detect and diagnose the degradation of three Internal Combustion Engine (ICE) parts which may cause on-duty failures: lead-acid accessory battery, spark plugs, and Exhaust Gas Recirculation (EGR) valve. Since the goal behind most FDD systems is cost
reduction, it is important to reduce the cost of the FDD test. Therefore, all the FDD solutions proposed in this research are based on three types of built-in sensors: battery voltage sensor, knock sensors and speed sensor. Furthermore, the engine database, the Machine Learning (ML) and Deep Learning (DL) models, and the virtual operating machines were all stored and operated in the cloud.
In this research, eight Machine Learning (ML) and Deep Learning (DL) models are proposed to detect degradations in three vehicle parts mentioned above. Additionally, novel advanced pre-processing approaches were designed to enhance the performance of the models. All the developed models showed excellent detection accuracies while classifying engine data obtained under artificially and physically induced fault conditions. Since some variant data samples could not be detected due to experimental flaws, defective sensors and changes in temperature and humidity, novel pre-processing methods were proposed for Long Short-Term Memory Networks (LSTM-RNN) and Convolutional Neural Networks (CNN) which solved the data variability problem and outperformed the previous ML/DL models.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26769 |
| Date | January 2021 |
| Creators | Seddik, Essam |
| Contributors | Habibi, Saeid, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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