| Summary: | 博士 === 中原大學 === 機械工程研究所 === 96 === ABSTRACT
The fuzzy neural networks is a modern method of the intelligence fault diagnosis for mechatronic equipments. The relationships between faults and symptoms in frequency spectrum were defined by expert’s knowledge and experiment results. The amplitude of rotary frequency was used for normalization and the level of signal symptom ratio was described by using fuzzy membership functions. The relationships between faults and vibration symptoms were inferred by using If-then rules or neural networks. Due to the type of faults are numerous, the structure of fuzzy neural networks is large and leads to spending more training time. The new faults and symptoms were added, the modified diagnosis rules were retraining.
This study proposed the relation vector which is the relationship between single fault and symptoms in frequency spectrum. The whole relation matrix can be decomposed to several independent relation vectors and trained and computed independently, and did not influenced by whole knowledge increase and correction. Due to the whole knowledge use amplitude of rotary frequency for normalization, the diagnosis results display output matrix. Each column is output of relation vector and the element of column vector is output of faults. The row of all column vectors composed the certainty row vector of diagnosis results for relation faults.
Because the error fell into local minimum easily in training of neural network, in this study, using genetic algorithm for globe search and replacing the backward computation of neural network. On the basis of MSE(mean square error) is globe minimum, solving the optimal weightings of neural network and improving the shortcomings of MSE of neural network fell into local minimum and obtaining the superior diagnosis results.
The vibration displacement or acceleration obtained from signals measurement system in mechanical monitoring. The symptoms in vibration signals extracted as the inputs of fault diagnosis expert system by using time domain analysis, orbit analysis, frequency spectrum analysis, waterfall spectrum analysis, whole spectrum analysis and so on. The inference of symptoms and faults based on the relationships matrix which composed of research and expert knowledge, the expert system was trained by using fuzzy neural networks. Due to the more unknown faults, symptoms in vibration signals and knowledge, in this study, the vibration signals analyzed by using auto regression and been the diagnosis knowledge of new faults or modified the relation vectors.
The symptoms in vibration signals are not definite or mew machine, the frequency spectrum is correct by using FFT. The vibration signals in time domain of normal machine, the time series analysis by enough order and obtain the AR coefficients. The normal spectrum of AR obtained with roots and power ratio, and trained to be relation vector of normal operation by using fuzzy neural networks. Although it does not known the relation matrix of expert diagnosis system includes the faults and symptoms when machine used for a long time and faults happened, the AR coefficient in normal machine save in relation vectors. The AR coefficient of vibration signals between fault and normal conditions can obtained diagnosis results by using fuzzy neural networks and determined machine is normal or not immediately. The type of fault was recognized by experts and correct as frequency spectrum of relation vector and add to the new row in diagnosis expert system.
The correct AR coefficient obtained with enough order, in this study, based on the frequency spectrum of FFT to decide the order of AR coefficient. Because the order is large and complication of frequency spectrum increased, the computation time need long. And the precision of AR spectrum limit within the precision of FFT frequency spectrum and can not exceed. Thus, in this study, it does not necessary for using AR coefficient to fault diagnosis in known frequency spectrum relation matrix, and used to assistance and supplement in fault relation matrix.
This study use P integration observation method to diagnose rotor systems using vibration responses. An integral is defined by integrating the distance of trajectories and origin in phase plane. The responses of a nonlinear system for rotating machinery are used to identify the relationship between the P integration value and the integrated interval.
The relation between input and output samples save in network weightings by training samples in supervise neural network. The diagnosis results by forward computation with test sample. When the training samples of neural networks are not enough, it is easy to get the wrong diagnosis results. This study utilizes the statistical parameters of vibration signals in time domain and combines the Bayesian networks in gear fault diagnosis and compared with fuzzy neural networks and probability neural networks. Also, the superior diagnosis ability investigated with Bayesian networks when training samples are not enough or have not fault samples.
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