# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### Decision Conflict Resolution Method Based on Sensitive Differences of Multi-Model Fault
#### 3.2.2 Multi-Local Model Conflict Resolution under the Condition of Balanced Fault Sensitivity  
In the actual fault diagnosis process of industrial motor bearings, if a high degree of conflict occurs during the decision fusion of multi-local models, then the combination rules of the classic D-S evidence theory will no longer apply. This section introduces the idea of trust distribution for the high-level decision-making conflicts that occur under fault-like sensitivity balance. While using information entropy to quantify the uncertainty of high-level conflict evidence, it also calculates the high support value of conflicting evidence. According to the information entropy value and support value of each highly conflicting evidence, the corresponding trust degree is assigned to it. The basic probability assignment of each conflicting piece of evidence is reconstructed to obtain a more reasonable and  
\(O(A_{n})\) reflects the degree of support of the two evidence for fault type \(A_{n}\). Finally, the information entropy value and support value of the evidence are integrated to construct the trust distribution formula of element \(A_{n}\), as shown below:  
\[W_{A_{n}}=\frac{Z(A_{n})+h_{i}m_{in}}{2}. \tag{27}\]  
Among them, \(W_{A_{n}}\) comprehensively reflects the degree of recognition of the element \(A_{n}\) by the two evidence bodies. Therefore, this paper uses it as the basic probability assignment for reconstruction. 2.3 Conflict Resolution of Multi-Local Models under the Condition of Non-Equilibrium Fault Sensitivity  
Due to differences in actual working environments, operating conditions, motor loads, sensor types, and installation locations for industrial motor bearings, the sensitivity of each fault type will likely show a corresponding imbalance, that is, the fault sensitivity non-equilibrium situation. Aiming at the high decision-making conflicts that occur under the condition of unbalanced fault sensitivity, this section first compares the fitting differences between the bearing conflict data samples and the standard reference samples through the gray correlation analysis technology. Then determine the optimal average correlation degree of the bearing data according to the trust degree of each high conflict evidence. Finally, reconstruct the basic probability assignment of each conflict evidence. In this way, the conflict between evidence can be reduced, and the credibility and rationality of multi-local model decision fusion can be improved. In essence, gray relational analysis transforms the infinite convergence problem into an approximate convergence problem; transforms the infinite space problem into a finite sequence problem to solve; replaces the continuous concept with discrete data. Specifically, the gray relational analysis describes the variable information in the system through mathematical theory, quantitatively analyzes the development and change of the system, and compares the correlation similarity of the internal components of the system. In addition, gray relational analysis has no hard and fast rules for the value and law of variable information and is applicable to most analysis scenarios. In this section, the gray relational analysis is used to take the spatial type of information as the mathematical benchmark, and the gray relational axiom is used as the principle to express the fitting degree between the bearing conflict data sample and the standard reference sample in numerical form. The closer the fitting degree between samples is, the greater the degree of correlation between them is, the higher the sensitivity is, and the corresponding distribution coefficient of trust degree is also greater. The conflict resolution process of multi-local models under the condition of unbalanced fault sensitivity is as follows:  
(1) Assume that the recognition framework \(\theta=\{A_{1},A_{2},A_{3},\ldots,A_{n}\}\) is known, where \(n\) is the number of fault types.