Research On Hybrid Intelligent Technique And Its Applications In Fault Diagnosis

Faults of large-scale and complex mechanical equipments are characterised by complexity, uncertainty, syndrome, et al. If a single intelligent technique is utilized to diagnose these faults, it would be too difficult to obtain a satisfied diagnosis result. Generally, the diagnosis accuracy of the single intelligent technique is lower and generalization ability is weaker. Thus, it is urgent and necessary to present a novel idea and method to solve these practical engineering problems.According to the diversity and the complementarity between individual intelligent techniques, i.e. artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA), et al., we may utilise their own merits and overcome their own shortcomings, and reinforce their advantages. By synthesising, integrating or fusing these individual intelligent techniques and different modern signal processing techniques and feature extraction methods via some means to propose hybrid intelligent diagnosis techniques, we can efficiently improve sensitivity, robustness and accuracy of a diagnosis system, reduce its uncertainty, ascertain the fault place exactly, and evaluate its severity. Therefore, it is quite worthy to investigate the hybrid intelligent technique and its applications in fault diagnosis for scientific theory studies and engineering applications. This dissertation just focuses on the extremely difficult but very attractive thesis. Aiming at incipient, slight and compound faults occurring in the mechanical equipments, the dissertation detailedly explores the fundamentals and engineering applications of the hybrid intelligent fault diagnosis techniques.The dissertation introduces basic conceptions and principles of fuzzy logic, neural network, clustering algorithm, genetic algorithm, et al., and provides an illustration for each technique to show its use and validity. Two advanced signal processing techniques suitable to nonstationary and nonlinear signals, wavelet packet analysis (WPA) and empirical mode decomposition (EMD), have been presented. WPA is an extended result of wavelet transform (WT). It orthogonally decomposes a dynamic signal into several independent frequency bands that link up mutually without redundant or omitted information. EMD method, which is based on the local characteristic time scales of a signal, adaptively decomposes the dynamic signal into a series of intrinsic mode functions (IMFs) and orthogonally presents intrinsic information of the signal. Thus, WPA and EMD have their own characteristics and could analysis the dynamic signal from different aspects, respectively.In order to improve the accuracy of fault diagnosis, we combine the superiority of WPA and EMD in processing dynamic signals, the advantage of feature evaluation method in selecting sensitive features and the strong classification ability of radial basis function neural network, and propose an intelligent fault diagnosis model based on feature evaluation and neural network. Aiming at various fault diagnosis problems, this model is able to automatically select the corresponding sensitive features and overcome blindness of traditional methods in selecting features. This model is applied to the local defects diagnosis of rolling element bearings and the slight rub fault diagnosis of a fume turbine rotor. The results demonstrate that more fault characteristic information can be precisely extracted by adopting WPA and EMD, the sensitive features can be easily selected from a large number of features with the feature evaluation method, and therefore the diagnosis accuracy has been greatly improved finally.Aiming at complex diagnosis problems of the intercurrent incipient fault and compound faults, a novel hybrid intelligent diagnosis model based on feature sets from multiple symptom domains and multiple classifier combination, is proposed, which combines statistics analysis, EMD, the improved distance evaluation technique, adaptive neuro-fuzzy inference system (ANFIS) and GA techniques. This model employs several signal preprocessing methods to mine the underlying fault information from dynamic signals. Time-domain and frequency-domain statistical features that reflect the equipment operation conditions from various aspects are synthesised to construct the multiple feature sets, which are able to completely present fault characteristics. Based on the independency and the complementarity of multiple ANFISs with the different input feature sets, we combine them and develop the hybrid intelligent diagnosis model. The practical application results of fault diagnosis of locomotive wheel pair bearings show the hybrid model is able to reliably recognise not only different fault categories and severities but also the compound faults. Thus, a desired diagnosis effect has been obtained via the hybrid model. Moreover, the application effect also validates the power of the proposed feature selection method based on the improved distance evaluation technique.Aiming at the existing shortcomings in the most popular unsupervised clustering algorithms used in the fault diagnosis field, fuzzy C-means (FCM) clustering algorithm, a novel hybrid intelligent clustering algorithm is developed. In this algorithm, the cluster number is automatically set by using the cluster validity index, feature weights are adaptively learned via a three-layer feed forward neural network with the gradient descent technique under the unsupervised mode of training, and sample weights are computed through the algorithm of distribution density function of data point. Then, the feature weights and the sample weights are assigned to the corresponding features and samples to emphasize the leading effect of sensitive features and typical samples, and weaken the interference of unrelated features and vague samples to improve the clustering performance. The test result of the benchmark data IRIS demonstrates the validity of the proposed algorithm. The algorithm is also employed to the single, incipient and compound fault diagnosis of locomotive wheel pair bearings. The results show that the hybrid intelligent clustering algorithm enables to automatically and correctly set cluster number, its clustering performance is superior to that of the FCM, and have a better practicability and generalisation.The necessity of developing remote condition monitoring and fault diagnosis systems is presented. The structures of two remote condition monitoring and fault diagnosis systems:"Monitoring and analysis system of vibration for the submarine model"and"Bearing condition monitoring and fault diagnosis system of strap transportation machines", are introduced respectively. The different functions of the two systems are developed. The hybrid intelligent vibration source identification method for the submarine model and the hybrid intelligent fault diagnosis method for the roller bearings of the strap transportation machines are proposed. The application of the hybrid intelligent technique in the two systems is detailedly studied in the dissertation.