# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
###### Abstract
Common fault diagnosis methods for industrial motor bearings can be roughly divided into fault diagnosis methods based on analytical models, fault diagnosis methods based on signal analysis [10, 11, 12, 13, 14, 15], and fault diagnosis methods based on knowledge reasoning [16, 17, 18, 19, 20]. Although the bearing fault diagnosis method based on the analytical model can detect the fault in a short period of time, as the research object becomes more and more complex, the dependence of this method on the mathematical model also increases. In addition, the actual application process is often affected by several other factors such as modeling error, noise interference, and unknown input. Therefore, this type of method can no longer adapt to the complication trend of bearing fault diagnosis and is gradually being replaced by fault diagnosis methods based on signal analysis and knowledge reasoning. Although the bearing fault diagnosis method based on signal analysis can avoid the construction of complex mathematical analysis models, its diagnostic accuracy is excessively dependent on signal analysis and processing technology and manual feature selection, which increases the difficulty of feature selection and the uncertainty in the process of industrial motor bearing fault diagnosis to a certain extent. In addition, with the development of the industrial Internet, smart manufacturing information systems are faced with more uncertainties, dynamics, and complexity [21,22]. Hence, more efficient fault diagnosis methods are necessary. With the rapid development of related technologies, intelligent fault diagnosis methods have gradually replaced traditional ones. Algorithms based on knowledge reasoning have begun to emerge in the field of fault diagnosis, and the fault diagnosis technology of industrial motor bearings has also entered a new stage of development. The most representative one is the fault diagnosis method based on deep learning. This type of method does not need to extract fault features through complex signal analysis techniques. In addition, it can integrate adaptive feature extraction and intelligent fault identification in one model or system, which makes it more conducive to the practical application of industrial fault diagnosis [23, 24, 25, 26]. In recent years, multi-source information fusion technology also plays an important role in the field of industrial motor bearing fault diagnosis. Various bearing fault diagnosis methods combined with information fusion technology have achieved relatively good results [27, 28, 29, 30]. Multi-source information fusion technology breaks the limitations of traditional bearing fault diagnosis methods so that people no longer judge the operating status of industrial motor bearings based on a single type of sensor signal, which greatly reduces the uncertainty in fault diagnosis. Therefore, the fault diagnosis method based on deep learning and multi-source information fusion will be the future development trend of industrial motor bearing fault diagnosis technology. Currently, most industrial motor bearing fault diagnosis methods are based on the diagnostic analysis of a single type of sensor signal, which has certain limitations. Due to the operating noise interference of the environment, the measurement error of the sensors, and other uncertain factors in the fault diagnosis process of industrial motor bearings, the bearing information collected only based on a single type of sensor may not fully reflect the operation of the bearing. In recent years, although the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress and research results, the uncertainty of noise interference and signal measurement error has been improved to a certain extent, but there are still some problems need to be further studied.