# An Intelligent Diagnosis Method for Machine Fault Based on Federated Learning
## 1 Introduction  
In the era of big data, the amount of data obtained by sensors is very large, and the data types are diverse. The fault diagnosis methods based on deep learning make it possible for mechanical big data processing. However, the reliable fault diagnosis model based on deep learning depends on abundant fault data. In engineering, different enterprises own the different types of fault data and the data from different enterprises is private and hardly shared, so the established fault model cannot recognize the newly added fault types. Therefore, it is of great significance how to establish a shared fault diagnosis model without exchanging data so that the model can recognize newly added fault types. At present, the mechanical fault diagnosis method based on deep learning has been widely studied. For example, in Ref. [1], a deep belief network was used to diagnose the health of aero-engines. In Ref. [2], the fault diagnosis in bearings and planetary gearboxes is successfully completed by a deep neural network, which overcomes the deficiency in traditional signal processing methods, i.e., traditional signal processing excessively relying on expert knowledge. In Ref. [3], a new hierarchical network based on deep belief network was proposed to diagnose the fault damage degree of rolling bearings. In Ref. [4], a deep convolutional neural network was used to monitor gearboxes to extract features adaptively. Compared with features extracted from time domain and frequency domain, those extracted by deep convolutional neural network are better. In Ref. [5], a deepautoencoder has been successfully applied into the fault diagnosis of bearings and gears. In Ref. [6], in order to better adapt to the one-dimensionality of the bearing and gear vibration signals, one-dimensional deep convolutional neural network was used for fault diagnosis. In Ref. [7], a normalized deep convolutional neural network was proposed to solve the imbalance of fault data. In Ref. [8], the bearing signal was compressed and collected by compressed sensing and classified by deep learning. In Ref. [9], a deep convolutional neural network model was used for end-to-end fault diagnosis of noisy signals. In Ref. [10], a novel deep capsule network with random delta rules was proposed to solve the problems of load variation and noise in bearing vibration signals. In Ref. [11], a fault diagnosis method of sparse autoencoders was proposed to recognize different coupling faults of bearings. In Ref. [12], the combination of deep learning and transfer learning was used for fault diagnose of rotating machinery. In Ref. [13], a deep residual neural network was proposed for fault diagnosis of rolling bearing; the proposed method can prevent gradient disappearance and gradient explosion. In Ref. [14], deep transfer learning was applied to diagnose the fault of rolling bearing under variable conditions, and the recognition rate is improved by 2-8%. In Ref. [15], a deep convolutional neural network was used to recognize the fault types based on the fusion data collected by the horizontal and vertical sensors mounted in the gearbox. In Ref. [16], the sparse denoising autoencoder and transfer learning was combined to diagnose bearing faults. In Ref. [17], a transfer model of fault diagnosis was established by deep residual neural network. The proposed model can transfer laboratory data to actual engineering data and has a high recognition rate. In Ref. [18], a multi-source transfer learning network was proposed to solve sample imbalance of mechanical fault by getting together and transferring the diagnosis knowledge modules of multiple machines. In summary, the existing mechanical fault diagnosis methods can be roughly divided into two categories, which are shown in Figure 1.  
1. In Figure 1a, the multi-fault diagnosis models are used to recognize the different fault types. 2. In Figure 1b, the data of different fault types are fused to construct a sample set, which can be used to establish a diagnosis model which can recognize different fault types.