# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### The Fault Diagnosis Method Based on Deep Learning  
With the rise of machine learning, deep learning based on neural networks has also achieved rapid development and remarkable results in related fields such as fault diagnosis. Since the deep learning-based industrial motor bearing fault diagnosis method can adaptively extract the deep-level features of the bearing signal and has obvious advantages in nonlinear computing capabilities, optimization capabilities, maintaining the integrity of signal features, and mining the correlation between features, etc.; therefore, it has attracted extensive attention. How to fully integrate multi-source data information and use the intersectionality and complementary of multi-source data information to maximize the effectiveness of data is the most basic problem in data analysis and application. It is against this background that multi-source information fusion technology emerges and develops. In practical applications, the data information acquired by a single or single type of sensor often has certain ambiguity. The emergence of multi-source information fusion technology can effectively improve this situation. Multi-source information fusion technology combines the data collected by multiple sensors at different levels to obtain more abundant and complete data. The complex system structure and operating environment of industrial motor bearings make it inevitable that many uncertain factors will appear in the fault diagnosis process. Although with the development of deep learning and neural networks, fault diagnosis algorithms based on knowledge reasoning have reduced the uncertainty in the bearing diagnosis process to a certain extent, the problem of \"knowledge acquisition bottleneck\" still restricts the use of related fault diagnosis algorithms. Therefore, it is necessary to combine multi-source information fusion technology with bearing fault diagnosis technology and fuse the characteristic information of different types of sensors to reflect the real-time operating status of industrial motor bearings accurately.  
Fault Diagnosis of Industrial Motor Bearings Based on Multi-Local Model Decision Conflict Resolution  
In the actual industrial motor bearing fault diagnosis process, there are uncertain factors such as noise interference in the operating environment and sensor measurement error, so the industrial motor bearing information collected only based on a single type of sensor may not fully reflect the true state of the bearing which seriously affects the reliability and accuracy of diagnosis. Only by comprehensively utilizing the multi-source heterogeneous information of industrial motor bearings can the actual faults of the bearings be diagnosed more accurately. However, in some cases, due to the multi-source of bearing heterogeneous data, conflicts will occur in the process of information fusion when making full use of the complementarity of bearing multi-source heterogeneous information.  
Based on the above reasons, this paper proposes MLMF-CR algorithm. The algorithm makes full use of the multi-source heterogeneous information of industrial motor bearings and constructs a multi-local fault diagnosis model based on Bi-LSTM to improve the accuracy of fault identification and training efficiency, which is helpful for practical industrial applications. In addition, since the D-S evidence theory cannot effectively integrate highly conflicting evidence, this paper proposes a decision-making conflict resolution strategy based on the sensitive difference of faults according to the sensitivity and reliability differences of different information sources to effectively resolve the conflicts in the process of decision-making fusion, which can improve the rationality and credibility of multi-local model decision fusion. In the following of this section, we first introduce the construction of multi-local fault diagnosis model based on Bi-LSTM. Then, we present the proposed decision conflict resolution method based on sensitive differences of multi-model fault.