As the automotive industry becomes increasingly competitive, parts manufacturers are under extreme pressure to improve the quality of their parts, while at the same time reducing costs. The method currently used to detect failures in automotive light assemblies after vibration endurance testing involves manual inspection only after the test is completed. An adaptable, reliable and low cost real time monitoring and diagnostic system that would interrupt the testing operation at the first onset of a failure is desired. This thesis describes accelerometer based, microphone (single and dual-microphone) based and acoustic emission sensor based monitoring systems for automotive light assembly failure detection during endurance testing. Preliminary results from accelerometer based and dual-microphone based diagnostic systems show that significant differences between healthy and faulty fog light assemblies can be detected. Based on these initial testing results, subsequent testing and data analysis were conducted for accelerometer based and dual microphone based systems. Four data analysis methods have been used: (1) Averaging signals in the time domain, (2) FFT of time domain waveforms over a specified time, (3) Averaging frequency spectra, and (4) Statistical methods for time domain signals. Individual frequency spectra (from FFT) and the average of multiple frequency spectra have shown potential to distinguish between signals from faulty and healthy light assemblies. Statistical measures, such as, Arithmetic mean (mu) and Kurtosis (K) can also be used to differentiate healthy and faulty light assemblies. In general, this work has shown the good potential to develop methods for adaptable, reliable and low cost real time monitoring and diagnostic systems that would interrupt the testing operation at the first onset of a failure.
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