The Unbalanced State Detection of Coriolis Flowmeter Based on Laplace Wavelet Sparse Representation and Customized Detection Indicator

Coriolis flow meters are widely used in the petroleum, chemical, and pharmaceutical industries. To ensure safety and efficiency during production, it is necessary to ensure the measurement accuracy of the flow meter. The dynamic imbalance of a flow meter is a key factor that affects the measurement accuracy of the flow meter. Therefore, a method for evaluating the unbalance state status of flow meters is proposed. This study aimed to quickly and accurately identify the dynamic unbalanced state of a flow meter. First, the frequency response function of the original signal is calculated to reduce the error caused by the influence of excitation. Second, the method of combining empirical wavelet and Laplacian sparse decomposition is used to extract the characteristic information of each order modal. Finally, through experiments, it is found that the second-order and third-order modal characteristic details are sensitive to the degree and location of the flow meter dynamic unbalance fault. Therefore, second-order and third-order modal characteristic information was selected to calculate two characteristic indicators that reflect the degree and location of the flow meter dynamic unbalance fault to achieve the effect of fault diagnosis. Experiments and simulations show that this method is more accurate and efficient than the other methods.