Abstract
Centrifugal pumps, like other rotating equipment, produce vibration signals during operation. Vibration signals often contain pump state information. Therefore, we can obtain pump state information by using appropriate signal processing methods. Synchrosqueezing wavelet transform (SWT) is a new time-frequency analysis technology. It is an algorithm for rebuilding time-frequency signals, which is similar to the empirical mode decomposition method. It can improve the time-frequency resolution of the signal compared with wavelet transform. In this paper, the SWT is used to analyze the vibration signal of centrifugal pump and extract characteristics. The data shows that the SWT can effectively extract the information of signal in time domain and frequency domain. Then we use the Support Vector Machine (SVM) to classify the features and realize the fault diagnosis of centrifugal pump. The result proves that the fault diagnosis method based on the SWT and SVM.
Highlights
Rotary machinery plays an important role in modern industrial production
There are three main types of fault diagnosis methods for centrifugal pumps: fault diagnosis based on signal, fault diagnosis based on analytic model and fault diagnosis based on expert knowledge
A fault diagnosis method of centrifugal pump based on Synchrosqueezing wavelet transform (SWT) and Support Vector Machine (SVM) is presented in this paper
Summary
Rotary machinery plays an important role in modern industrial production. Once the rotary machinery at key parts lose effectiveness, it will lead to serious consequences. Based on the vibration signals of the equipment, many fault diagnosis methods have already been proposed and successfully applied in the fault diagnosis of different rotary devices. These methods use statistical analysis, frequency domain analysis or time-frequency analysis to identify and process the vibration data. The classification algorithm is used to classify the characteristic so as to realize the fault diagnosis of machinery. Wavelet Decomposition is used to extract the vibration signal of centrifugal pump, and Support Vector Machine is used as classifier for on-line fault diagnosis in [3]. The frequency domain characteristics are extracted and are classified by SVM to realize the fault diagnosis of centrifugal pump
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