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|Title:||A Wireless Sensor for Fault Detection and Diagnosis of Internal Combustion Engines|
|Keywords:||Artificial Neural Networks;Fault Detection;Fault Diagnosis;Internal Combustion Engine;Smooth Variable Structure Filter;Accelerometer;Electronic Design;Vibration Analysis;Wireless Sensor;Automotive;Extended Kalman Filter;Levenberg-Marquardt|
|Abstract:||A number of non-invasive fault detection and diagnosis (FDD) techniques have been researched and have proven to have worked well in classifying faults in internal combustion engines (ICE) and other mechanical and electrical systems. These techniques are an integral step to creating more robust and accurate methods of determining where or how a fault has or will occur in such systems. These FDD techniques have the potential to not only save time avoiding a tear-down of a costly machine, but could potentially add another layer of safety in detecting and diagnosing a fault much earlier than was possible before. Looking at the previous research methods and the systems they used to acquire this data, it is a natural progression to try and make a system which is able to encapsulate all of these ideologies into one inexpensive module capable of integrating itself into the advanced set of FDD. This thesis follows along with the development of a new wireless sensor that is developed specifically for the use in FDD for ICE and other mechanical systems. A new set of software and firmware is created for the system to be able to be incorporated into previously designed algorithms. After creating and manufacturing the sensor it is put to the test by incorporating it into several Artificial Neural Networks (ANN) and comparing the results to previous experiments done with previous research equipment. Using vibration data acquired from a running engine to train a neural network, the wireless sensor was able to perform equally as well as its expensive counter parts. It proved to have the ability to achieve 100% accuracy in classifying specific engine faults. The performance of three ANN training algorithms, Levenberg-Marquardt (LM), extended Kalman Filter (EKF), and Smooth Variable Structure filter (SVSF), were tested and compared. Adding to the feasibility of a standalone system the wireless sensor was tested in a live environment as a method of instant ICE fault detection.|
|Appears in Collections:||Open Access Dissertations and Theses|
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|Hodgins_Sean_201708_MaSC.pdf||Sean Hodgins MaSC Thesis||6 MB||Adobe PDF||View/Open|
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