Signals Processing And Its Application To Process Monitoring

Process monitoring is a new subject which intercrosses and infiltrates with other knowledge. Monitoring the system operation conditions by different kinds of sensors, it can obtain the system operation informations by modern signal processing and analyzing technology, and then it can give the system a diagnosis and prediction.Based on the introduction for the research work about process monitoring and signal processing technology, the engineering signal acquisition and pretrement methods were presented. Aimming at the character of the acoustic emission signals came from the cutting tools, an acoustic emissioin method for tool wear monitoring based on wavelet analysis was gave out, and the estimate models for roughness prediction which based on artificial neural network and fuzzy theory were set up. The experiments proved that the models for roughness predictoin in turning are workable and precise. Therefore, the strategy, by using ANN, for cutting parameter optimization based on process monitoring signals was gave out, and its value is shown in the experiments.The wavelet analysis theory was introduced in detail. Combing the characteristic of the signal's singularity with wavelet's specialty thatwavelet transfer can be represented by roll-integral, the relationship between the wavelet singularity detection theory and the wavelet coefficient mudule-maxima was deduced. By wavelet multi-resolution, AE signals' energy distribution was analyzied. Then extract the character paramecter, MEAN & RMS, by the wavlet coefficient module-maxima of the scale which contained most of the signals' energy. This method was proved by the experiments.In signal processing technology's application research, the datas from different sensors were collected and used to discuss the influence from different datas selection models. It guarantees the real signal's character can be represented by the caculated parameter. Therefore, a multi-sensor signal character fusion model was built up. Regarding the roughness as the target, the suitable character parameters and net work structure were found by experiment comparision. The relevancy between cutting parameters and roughness were analyzed by gray theory respectively. Then the cutting parameter optimization strategy based on process monitoring signals was given out. And it passed the experiments' detection.