In cooling systems of thermal and nuclear power generating unit

the bearing fault signal of the motor is weak and nonlinear

which is easily masked by running signals and invalid signals

and the use of a single vibration monitoring may not be sufficient to collect complete defect information. To address this problem

vibration and sound signals are combined to monitor bearing fault signals

and the collected sound and vibration signal features are fused. To process the sound and vibration signals of motor bearings

a WR-VMD algorithm that integrates wavelet ridge(WR) and varational mode decomposition(VMD) is proposed. The WR is used to analyze the components of the original signal

and then the acquired information is used to determine the parameters of the VMD

which makes up for the shortcomings of the original VMD method that requires the parameters to be set empirically in advance. The simulated signal results show that

compared with the same type of methods

the features extracted by the WR-VMD method are the most obvious and have the least interference information. Finally

the acoustic and vibration signal fusion technique and the WR-VMD algorithm are applied to the measured motor bearing fault data

and the results show that

compared with other feature extraction algorithms of the same type

the WR-VMD extracts the most obvious features and has the highest accuracy in fault diagnosis. The acoustic and vibration signal fusion has at least a 7% increase in accuracy compared with a single vibration or acoustic signal in fault diagnosis.