## 1 Introduction  
With the development of the information age, the reliability requirements of mechanical equipment are getting higher and higher [1]. Rotating parts of mechanical equipment, such as shafts, gears and bearings, are prone to shaft cracks, gear breaks and bearing wear and other dangerous hazards [2]. In order to ensure the safe and reliable operation of the equipment, it is particularly important to obtain fault sources and fault data in time, and perform an accurate and effective fault diagnosis. Signal correlation function was widely used in rotor system fault diagnosis--many scholars have done research in this area. A diesel engine fault warning method based on the correlation analysis of the envelope of cylinder head vibration signal was proposed [3; 4; 5], and a correlation analysis was proven to be able to better reflect the fault state of the unit. A maximum correlation kurtosis deconvolution algorithm (MCKD) was used to process generator stator vibration signals, and the extraction effect of fault signal characteristic frequency was improved [6]. [PERSON] et al. [7] proposed a partial correlation integral algorithm, and used the size index calculated by the algorithm to successfully predict the faults of rolling bearings in rotating machinery. [PERSON] et al. [8] used the obtained current signal for feature selection based on cross-correlation technology. Then, they used these functions to perform fault detection on the four-layer multiplier perceptron artificial neural network (ANN) training, and obtain a good diagnosis. [PERSON] et al. [9] proposed a method for gearbox fault feature extraction based on empirical mode decomposition (EMD) and multi-fractal detrended cross-correlation analysis (MFDCCA), which can successfully achieve better fault diagnosis accuracy. The fault information obtained by a single sensor is single, while multiple different sensors can obtain fault information from multiple angles. Sensor data from multiple locations or different angles are also used to extract unsupervised features in the frequency domain and detect engine failures in industrial environments [10; 11]. In addition, multi-sensor systems can simultaneously detect sensors or deal with faults through Bayesian networks, and quantify the reliability of sensor data through network weights [12; 13]. [PERSON] et al. [14] proposed a conversion method that converts vibration signals from multiple sensors into images. Using experimental data compared with other methods, they found that this method has higher recognition accuracy and faster convergence speed. For gearbox failures of multi-source signals, [PERSON] et al. [15] proposed a new tensor classifier, called the core flexible displaceable convex hull-based tensor (KFDCH-TM), which used wavelet packet transform (WPT) from multiple sources. The feature tensor extracted from the source signal is used to diagnose gearbox faults through KFDCH-TM. With the development of fault diagnosis and signal processing technology, signal fusion technology is constantly updated and optimized. In the traditional multimodal sensor fusion method, data fusion can be achieved by manually extracting the features of multimodal sensors and simply connecting the growth vectors [16; 17; 18]. Research on multi-mode sensor fusion using deep learning networks for fault diagnosis has only begun to appear in recent years. [PERSON] et al. [19] proposed a deep-coupled auto encoder network for vibration and acoustic sensor data fusion to realize the fault diagnosis of gears and bearings. [PERSON] et al. [20] constructed a deep-coupling restricted Boltzmann machine for data fusion of vibration and acoustic emission sensors in order to diagnose tool faults. [PERSON] et al. [21] proposed a multi-sensor fusion algorithm using the CNN-LSTM network to monitor bearing faults. [PERSON] et al. [22] used a DCNN-based two-way vibration signal data fusion method for the health status recognition of planetary gearboxes.