Arti cial neural networks (ANNs) are a powerful processing units inspired by the human brain. They can be used in many applications due to their pattern classi cation abilities, ability to model complex nonlinear input-output mappings, and their ability to adapt and learn. The relatively new Smooth Variable Structure Filter (SVSF) has recently been applied to the training of feedforward multilayered neural networks. It has shown to have good accuracy and a fast speed of convergence. In this thesis, an engine fault detection system using an ANN will be implemented. ANNs are used in engine fault detection due to the high-noise environment that engine operate in. Additionally the fault detection system must work while the engine is mounted in a vehicle, which provide additional sources of noise. The SVSF training method is evaluated and compared to other traditional training methods. Also di erent accelerometer types are compared to evaluate whether lower cost accelerometers can be used to keep the system cost down. The system is tested by inducing a missing spark fault, a fault that has a complex fault signature and is di cult to detect, especially in an engine with a high number of cylinders. / Thesis / Master of Applied Science (MASc)

Deep Learning Networks (DLN) is a relatively new artificial intelligence algorithm that gained popularity quickly due to its unprecedented performance. One of the key elements for this success is DL’s ability to extract a high-level of information from large amounts of raw data. This ability comes at the cost of high computational and memory requirements for the training process. Estimation algorithms such as the Extended Kalman Filter (EKF) and the Smooth Variable Structure Filter (SVSF) are used in literature to train small Neural Networks. However, they have failed to scale well with deep networks due to their excessive requirements for computation and memory size. In this thesis the concept of using EKF and SVSF for DLN training is revisited. A New family of filters that are efficient in memory and computational requirements are proposed and their performance is evaluated against the state-of-the-art algorithms. The new filters show competitive performance to existing algorithms and do not require fine tuning. These new findings change the scientific community’s perception that estimation theory methods such as EKF and SVSF are not practical for their application to large networks. A second contribution from this research is the application of DLN to Fault Detection and Diagnosis. The findings indicate that DL can analyze complex sound and vibration signals in testing of automotive starters to successfully detect and diagnose faults with 97.6% success rates. This proves that DLN can automate end-of-line testing of starters and replace operators who manually listen to sound signals to detect any deviation. Use of DLN in end-of-line testing could lead to significant economic benefits in manufacturing operations. In addition to starters, another application considered is the use of DLN in monitoring of the State-Of-Charge (SOC) of batteries in electric cars. The use of DLN for improving the SOC prediction accuracy is discussed. / Thesis / Doctor of Science (PhD) / There are two main ideas discussed in this thesis, both are related to Deep Learning (DL). The first investigates the use of estimation theory in DL network training. Training DL networks is challenging as it requires large amounts of data and it is computationally demanding. The thesis discusses the use of estimation theory for training of DL networks and its utility in information extraction. The thesis also presents the application of DL networks in an end-of-line Fault Detection and Diagnosis system for complex automotive components. Failure of appropriately testing automotive components can lead to shipping faulty components that can harm a manufacturer’s reputation as well as potentially jeopardizing safety. In this thesis, DL is used to detect and analyze complex fault patterns of automotive starters, complemented by sound and vibration measurements.

Faulty manufactured alternators lead to commercial and safety concerns when installed in vehicles. Alternators have a major role in the Electrical Power Generation System (EPGS) of vehicles, and a defective alternator will lead to damaging of the battery and other important electric accessories. Therefore, fault detection and diagnosis of alternators can be implemented to quickly and accurately determine the health of an alternator during end of line testing, and not let faulty components leave the manufacturer. The focus of this research is to develop a Model Based Fault Detection and Diagnosis (FDD) strategy for detecting alternator faults during end of line testing. The proposed solution uses Extended Kalman Smooth Variable Structure Filter (EK-SVSF) to detect common alternator faults. A solution using the Dual Extended Kalman Filter (DEKF) is also discussed. The alternator faults were programmatically simulated on alternator measurements. The experimental results prove that both the EK-SVSF and DEKF strategies were very effective in alternator modeling and detecting open diode faults, shorted diode faults, and stator imbalance faults. / Thesis / Master of Applied Science (MASc)