| Summary: | 博士 === 國立臺北科技大學 === 機電科技研究所 === 101 === In recent years, several On-Board Diagnostics of automotive engines, such as European On-Board Diagnostics (EOBD) and On-Board Diagnostics II (OBDII stands for ''Enhanced On-Board Diagnostics, Second Generation''), have been developed to the fault diagnostics of vehicle engines because of legislative regulations. To study the dynamic characteristics of engines, the vibration patterns were obtained through signal acquisition with accelerometers and analyzed via data processing. Also, the several techniques of signal process would be adopted to investigate engine faults. After the order tracking technique could construct the original dynamic patterns for engine vibrations; therefore, the fast Fourier transform and the wavelet transform were introduced to extract the vibrating feature based on the time–frequency domain analysis. Applying the similar concept, the pressure variations on an intake manifold of an internal combustion engine should also detected as fault diagnosis. These pressure signals were decomposed and reconstructed by the discrete wavelet transform (DWT) and the engine malfunction could be recognized via various techniques of artificial neural networks. Thus, the expert detection system is developed for engine fault detection. The phenomena of a periodical impact oscillation are the energy resource through the vibration harvest with piezoelectric smart materials. The power generation is available and experimental analysis is another design point of finite element ANSYS method in this paper. The testing platform was built up and used to prove the conversion efficiency from vehicle vibration energy to electrical power output by using a lever mechanism to simulate the oscillating situation from a vehicle operation actually, called as a vibration energy harvesting system.
|
|---|
