Plunger pump fault signal time-frequency diagram noise reduction enhancement method and system
A fault signal and enhancement system technology, applied in image enhancement, neural learning methods, image data processing, etc., can solve problems such as dependence on diagnostic accuracy, inability to guarantee fault characteristics, and dependence on experience
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Embodiment 1
[0054] The problem to be solved by the present invention is to provide a fault diagnosis method for a high-speed aviation plunger pump, which can realize signal noise reduction enhancement, thereby realizing signal feature extraction and fault diagnosis, and improving the diagnosis accuracy of noise-containing signals.
[0055] In order to achieve the above goals, the present invention provides a Grad-CAM-based method for enhancing the noise reduction of the time-frequency diagram of the fault signal of the plunger pump, which is characterized by comprising the following steps:
[0056] S1: Collect the vibration signal of the casing when the plunger pump fails
[0057] At the sampling frequency fs, the vibration signal of the plunger pump under different inlet pressures is collected
[0058] S2: Data sample segmentation
[0059] The method adopted in the present invention needs to perform time-frequency transformation on the original signal to obtain a time-frequency diagram ...
Embodiment 2
[0086] Embodiment 2 is a preferred example of Embodiment 1.
[0087] like figure 1 , is the flow chart of the method for enhancing the noise reduction of the time-frequency diagram of the fault signal of the plunger pump based on the Grad-CAM of the present invention, and diagnosing the cavitation fault of a high-speed aviation plunger pump, including the following steps:
[0088] S1: Install a vibration sensor on the casing of the plunger pump, connect the acquisition equipment, and collect the vibration signal when the pump has different degrees of cavitation under different inlet pressures, and the sampling frequency is 10240Hz.
[0089] S2: Divide the original vibration signal into N segments, and each segment is used as a sample. Cavitation grades were assigned to each sample segment in terms of flow loss. This implementation case is divided into four grades, namely severe cavitation, moderate cavitation, slight cavitation and no cavitation. In the implementation case, ...
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