Abstract
The traditional fault inspection of the motorcar engine cannot detect the noise and sound signal resulted from the abnormalities of some mechanical parts. For instance, the cylinder misfires; the looseness of the fan belt is irregular; the valve clearance is out of order¡K. and so on. When the fault message cannot be delivered by the ECU of the computer, the skilled senior engineers are required at this moment to make the experiential judgments.
In the present society, due to the development of information, the computer technology makes progress by leaps and bounds. If we can make use of the monitoring method by the Acoustic signal instrument, build up a set of complete and efficient fault diagnosis system through the computer software and apply speedy and accurate way to assist the repairmen in relocating the causes for such faults, the accuracy of inspection can be greatly enhanced with a huge help in the preventive maintenance work. In that case, the fault conditions of the engine can be validated precisely
and effectively, so the overhaul efficiency of the engine can be upgraded to a large extent.
In this article, the procedures of sound signal recording will be brought forward by linking the digital camera with such a recording equipment as the high-precision microphone to make records of the fault sounds made when the engine runs. It uses the frequency analyzer to conduct the sampling and combine the computer software to further process and analyze the same. Finally the character parameters will be obtained. By applying the mathematical exercise of ¡§Back-Propagation Neural Network¡¨ to undertake the training and detection of the sounds for the purpose of identifying the kinds of the faults. It replaces the errors caused from the experiential judgments made by the expert senior engineers. In terms of the training and maintenance ability of the newly recruited technical repairmen, their capability for exact and reasonable recognition of the fault types is substantially promoted.
Keywords¡GAcoustic Signal¡ABack Propagation Neural Network
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0218103-113321 |
| Date | 18 February 2003 |
| Creators | Yeh, Huai-Jen |
| Contributors | none, none, none, none |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0218103-113321 |
| Rights | withheld, Copyright information available at source archive |
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