Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.

中文翻译：

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多源振动故障信号时空多相关的融合方法及应用

结合其他信号处理方法，相关算法被广泛用于转子故障的诊断和识别。为了解决单个传感器的振动信号太单一的问题，提出了一种新的多源振动信号融合方法。该方法通过使用空间位置的多个互相关来探索不同位置的振动传感器之间的相关性。首先，小波降噪和线性归一化用于处理原始数据。然后，建立传感器之间的信号能量相关函数，并获得自适应权重。最后，获得数据融合结果。以不同速度下的转子轴承和齿轮故障为例，使用时空多重相关融合方法（STMF）对三个不同位置的振动传感器的数据进行融合。通过智能故障诊断方法堆叠式自动编码器（SAE），与单传感器数据，平均加权融合数据和神经网络融合数据相比，STMF方法在700 rpm，900 rpm和1100 rpm时可达到94％以上的诊断精度。结论是，STMF方法的结果是更有效和更好的。