Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-17 Cooperative journals: 《计算机应用研究》
Abstract： In view of the shortcomings of the previous pattern recognition methods and the saliency problem of features, this paper proposed a pattern recognition method — Agent discriminate model based feature weighted (ADMFW) . The core of this method is to use the weighting factor to obtain the weighted feature and utilize the agent discriminate model to establish the weighting function of weighted feature. Firstly, calculating the weighting factors of the features to evaluate the saliency of the feature and assigning the weights to features. Secondly using the weighting features and the agent discriminate model to establish the prediction model. Finally, applying the prediction model to identify the unknown samples. The analysis of the experimental data of rolling bearing shows that this method can identify the working states and fault types of rolling bearings effectively.
Subjects: Mechanical Engineering >> Other Disciplines of Mechanical Engineering Subjects: Computer Science >> Computer Application Technology submitted time 2017-12-26
Abstract： The existing methods of bearing diagnosis have some disadvantages: The conventional method has complex mathematical calculation and poor diagnosis effect. It generally only diagnoses the fault location and irrespective of the load and the fault size. The existing convolutional neural network method use the traditional convolution neural network. A network can only output a property and can not simultaneously diagnose multiple properties. In order to simultaneously diagnose the fault location, fault size and load, for the first time put forward a multi-attributes convolution neural network (MACNN) and applied to the bearing fault diagnosis. The multi-attribute convolution neural network is trained using one-dimensional vibration signal training . The advantages lies in overcoming the shortcomings of the traditional method: the diagnosis result of any combination of the fault attributes can be obtained, the network parameters are less, the method is simple, the generalization ability is strong and the accuracy rate is high. A series of tests have been carried out using the bearing data of Case Western Reserve University. The results show that the proposed method can accurately diagnose several properties of bearing faults with high accuracy and good generalization ability.
Peer Review Status:
Awaiting Review
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