# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
###### Abstract
For instance, while improving the stability and fault tolerance of the bearing fault diagnosis model, the multi-source heterogeneous information of industrial motor bearings also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, making further judgments on industrial motor bearing failures will be difficult. Therefore, solving multi-source information conflicts while fully using the complementary of bearing multi-source heterogeneous information is one of the urgent problems to be solved in the development of industrial motor bearing fault diagnosis technology. To fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts, an industrial motor bearing fault diagnosis algorithm based on multi-local model decision fusion conflict resolution (MLMF-CR) is proposed in this paper. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the bearing signals in the local fault diagnosis model based on Bidirectional Long Short-Term Memory Network (Bi-LSTM). The feature information of each fault state is fused with evidence theory after forming a local diagnosis decision. In addition, high-conflict situations that may occur during decision-making fusion are focused on consideration. To this end, the idea of trust distribution is introduced to reduce the degree of information conflict. Specifically, according to the differences in sensitivity and reliability of bearing faults in different operating environments or under specific conditions, the degree of difference in fault sensitivity is refined into two types: balanced sensitivity and unbalanced sensitivity. When the fault sensitivity is balanced, the trust of different information sources is quantified by support and uncertainty. When the sensitivity is unbalanced, gray relational analysis is used to assign trust degrees to different information sources. The algorithm can effectively resolve the high degree of conflict in the decision-making fusion process while considering the complementarity of multi-source heterogeneous information. The following of this paper is organized as follows. Section 2 introduces the related work. Section 3 presents the proposed method. Section 4 describes the performance evaluation. Finally, we provide a conclusion to summarize this paper in Section 5. The abbreviations used in this paper are listed in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline MLMF-CR & Multi-Local Model decision Fusion Conflict Resolution \\ Bi-LSTM & Bidirectional Long Short-Term Memory Network \\ EDM & Electrical discharge machining \\ F1 & F1-score \\ _ACC_ & Accuracy \\ P & Precision Recall \\ BHD & Balanced Homodyne Detection \\ TBIDF & Two-stage Bayesian inference decision fusion \\ FDB-IDS & Fault diagnosis algorithm based on improved D-S \\ S-LSTM & Fault diagnosis algorithm based on long-short term memory \\  & network on tree structures \\ DSNCR & D-S theory improved Yager D-S combination rule \\ ECACE & Improved Deng Yong conflict evidence combination method \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The abbreviations used in this paper.