Research And Development Of Rotating Machinery Fault Diagnosis Instrument Based On Wavelet Analysis

Rotating machinery is largely used in modern industry and mining, and plays very important role in enterprise's production. Once these equipment break down, it not only affects normal production to cause huge economic loss, moreover possibly can endangers life safety, and leads to serious security and environment accident. Therefore, the operation of effective monitoring and diagnosis for rotating machinery condition is necessary. With the development of science and technology, constantly rotating machinery to high-speed,light,highly efficient and intelligent development, it also sets a higher request to the equipment condition monitor and fault diagnosis.In rotating machinery fault diagnosis, previous diagnosis instruments all are built on fast Fournier transform (FFT) as the core of traditional signal processing method, and basically can satisfy to the steady signal symptom extraction and analysis. But to symptom extraction of non-steady signals which are result from machine load change, speed instability and machine failures is relatively short, especially often has great limitation to certain parts early fault recognition. Therefore, the front technology of signal processing—wavelet analysis is adopted to conduct in-depth research and exploration in this article, and has made up the insufficiency of tradition signal processing method. This has vital significance to reduce misinformation and enhance diagnosis rate. In this foundation, digital signal processor (DSP) is used to develop the portable rotating machinery fault diagnosis analyzer. The system uses DSP to cause the complex wavelet algorithm and FFT spectral analysis algorithm loaded into possibly, and promotes the data processing speed greatly, thus enhances fault diagnosis real-time capability. This article research work mainly has the following several points:1. In view of the tradition signal processing method which is not suitable for non-steady signals symptom extraction, wavelet packet analysis is adopted in rotating machinery fault symptom extraction. The results of diagnosis example indicate the validity of this method.2. In view of the limitation of the traditional method to certain parts early fault recognition, the more precise fault diagnosis method—wavelet packet-frequency spectrum analysis is studied. The results of example show that, when rotating machinery happens to early weak failures, carrying on the FFT spectral analysis to vibrates signals isn't able directly to reflect fault characteristic information, but the wave packet-spectral analysis can succeed in extracting the weak fault information which are hided in rotating machinery.3. Considering the present questions of fault diagnosis instrument application, we design the intelligent fault diagnosis analyzer that is advantageous for the operation, and has powerful functions and good real-time capability. The system technical specification and scheme, components choice, software and hardware design are completed. Finally, carrying on the debugging in rotating machinery test installation to the system, the experimental results indicate the reliability of the fault diagnoses analyzer.