# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
###### Abstract  
In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measurement error has been improved to a certain extent. However, the multi-source heterogeneous information of industrial motor bearings not only improves the stability and fault tolerance of the bearing fault diagnosis model but also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, it will be difficult to make further judgments on the bearing faults of industrial motors. Therefore, solving the multi-source information conflict effectively while fully using the complementarity of bearing multi-source heterogeneous information is an urgent problem to be solved in developing industrial motor-bearing fault diagnosis technology. This paper proposes an industrial motor bearing fault diagnosis algorithm based on multi-local model decision conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the characteristic information of the bearing signals in each fault state through the local fault diagnosis model based on the bidirectional long short-term memory network (Bi-LSTM) and forms a local diagnosis. After the decision is made, evidence theory is used for fusion. In addition, the high conflict situation that may occur in the process of decision-making fusion is also considered. To this end, the trust degree distribution is introduced to reduce information conflict. Specifically, according to the difference in the sensitivity and reliability of bearing faults under different operating environments or specific conditions, the degree of difference in faults is refined into 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  
will damage the motor itself and directly affect other industrial equipment connected to it, which will cause problems in the entire industrial production process. In severe cases, it may cause safety accidents and major economic losses [3,4]. Industrial motors mainly consist of bearings, shafts, stators, rotors, and frames. Among them, industrial motor bearings, as one of the most critical components of industrial motors, play an important role in carrying the rotation of the main shaft, reducing the wear between the rotor and the stator support, and maintaining the rotation accuracy of the axis. According to statistics, the proportion of motor failures caused by damage to the bearing components is as high as 50%, much higher than the proportion of motor failures caused by other components [5]. Fault diagnosis technology for industrial motor bearings existed as early as the scale of industrial production but was limited by the level of science and technology at that time, the fault diagnosis of motor bearings mainly relied on the personal experience of maintenance personnel and the simple parameters of related instruments to make subjective decisions. It was not until the 1960s that due to the continuous development and progress of digital signal processing technology, computer technology, and pattern recognition technology, the fault diagnosis technology of industrial motor bearings was gradually improved and developed into a comprehensive system integrating a variety of modern science and technology [6, 7, 8, 9].