Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph

Abstract

An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

“…There are currently two main types of research. One is based on knowledge graphs [1][2][3][4], involving multiple steps such as knowledge graph construction, knowledge learning, and knowledge reasoning. Key technologies include ontology modeling of domain knowledge, graph mining algorithms, and logical reasoning algorithms based on fast graph search.…”

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

“…Jiangquan Zhang et al [30] proposed an intelligent diagnosis algorithm based on CNN, which can automatically accomplish the process of the feature extraction and fault diagnosis. Zhibo Li et al [31] proposed a fault diagnosis method based on the fusion of deep learning with a knowledge graph. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators.…”