Research On The Defect Diagnosis For Rolling Bearing Based On SOPC

Rolling is the modern machines used in most parts, in the production play a very important role. Fail of these parts, not only affect the normal production, resulting in huge economic losses, but also could endanger the personal safety, lead to serious safety and environmental incidents. Therefore, Rolling machine running an effective monitoring and diagnosis is very necessary. With scientific and technological development, rolling machine to keep the high-speed, light, efficient and intelligent development, which is also the equipment condition monitoring and fault diagnosis put forward higher requirements.In the rolling bearing fault diagnosis in the past, diagnostic instruments are built in the time domain or frequency domain parameters to determine, on a variety of features can not fault parameters of automatic diagnosis, this article on this basis, in order to fast Fourier transform (FFT) as the core , extract time domain and frequency domain parameters as the fault feature, combined with BP neural network parameters on the characteristics of an automatic fault diagnosis, this system showed up for the shortcomings of traditional instruments with a very large practical significance. On this basis, using field-programmable logic device (FPGA) to build into a SOPC system, development of rolling bearing fault diagnosis-line analyzer. The system can be built using SOPC flexible features, built-in hardware-BP neural network co-processor, and greatly enhanced the speed of data processing, thereby enhancing fault diagnosis in real time. This article research work mainly has the following several points:1. Analysis of time-domain signal parameters and frequency-domain signal parameters in the failure of performance on the advantages and disadvantages, choose a suitable fault characteristics of the signal.2. The traditional instrument of the limitations of automatic fault identification to study the BP neural network fault auto-identification model. 3. View of the current application process fault diagnosis equipment problems in the design of online intelligent fault diagnosis analyzer. Technical indicators to determine the completion of the system, system programming, device selection, system hardware design ,BP hardware design and software design aspects of the work; finally rolling on the mechanical experimental equipment system debugging, experimental results show that the online analyzer reliability of fault diagnosis .