Abstract
In order to diagnose the retarder faults of oil pumping machine accurately in complex environments and improve the generalization of the algorithm, a GWO-SVM fault diagnosis algorithm based on the combination of sound texture and vibration entropy characteristics was proposed. Firstly, the acquired sound signal was purified by band-pass filter, then generalized S-transform was developed to extract the box dimension, directivity, and contrast ratio, which reflect the characteristics of time-frequency spectrum, to construct three-dimensional texture eigenvectors. Secondly, the K parameter of variational mode decomposition (VMD) was reasonably selected by the energy method, and then the vibration signal was decomposed to get modal components, and the permutation entropy was obtained from modal components. Finally, joint eigenvectors were constructed and fed into SVM for learning. The gray wolf optimization (GWO) algorithm was used to optimize the parameters of the SVM model based on mixed kernel function, which reduces the impact of sensor frequency response, environmental noise, and load fluctuation disturbance on the accuracy of retarder fault diagnosis. The results showed that the GWO-SVM fault diagnosis method, which is based on the combination of sound texture and vibration entropy characteristics, makes full use of the complementary advantages of signal frequency band. And the overall diagnostic accuracy for the experimental samples reaches 100%, which has good generalization ability.
Highlights
Oil pumping machine relies on the up and down movement of the horsehead to complete the lifting of the crude oil from the wellbore. e retarder connecting the crank train is the key component of the power transmission
For nonstationary signals such as sound and vibration, there are mainly analysis methods such as dynamic time warping (DTW), wavelet transform (WT), empirical mode decomposition (EMD), and local mean decomposition (LMD) [5,6,7,8]. e DTW planning optimal path is prone to metamorphosis distortion, WT has energy leakage, and the two cannot adaptively decompose the signal. e EMD adaptive decomposition process is prone to over enveloping, end effect, and modal aliasing
Defect identification of related retarder of beam oil pumping machine has always been a technical problem in the state monitoring of distributed oil production wells. e combination of sound texture and vibration entropy characteristics and the gray wolf optimization (GWO)-SVM classification algorithm, proposed in this paper, can effectively and accurately diagnose the field faults under the complementary frequency band. e main contributions and novels of the proposed method are summarized as follows: (1) A fault diagnosis method based on the complementarity combination of sound-vibration signals is proposed for retarder equipment of oil pumping machine, which improves the accuracy of fault identification on the basis of nonmissing detection of retarder defects
Summary
Oil pumping machine relies on the up and down movement of the horsehead to complete the lifting of the crude oil from the wellbore. e retarder connecting the crank train is the key component of the power transmission. E sound signal accompanying the operation of the oil pumping machine is homologous to the vibration signal and can be obtained by the noncontact electret film capacitive sensor, which can effectively compensate for the detection failure phenomenon caused by the vibration sensor band limitation For nonstationary signals such as sound and vibration, there are mainly analysis methods such as dynamic time warping (DTW), wavelet transform (WT), empirical mode decomposition (EMD), and local mean decomposition (LMD) [5,6,7,8]. E box dimension, directivity, and contrast ratio which reflect the time-frequency texture features are extracted as eigenvectors to classify faults. Directivity, and contrast ratio of the generalized S-transformed time-frequency diagram of the sound signal in the four states (belt breakage, retarder oil leakage, gear pitting peeling, and normal state), and record them as D, Fdir, and Fc, respectively.
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