Fault Diagnosis of Overflow Valve Based on Trispectrum

• 作者: Wenbing Wu
• 来源期刊: 世界工程和技术(英文)
• 学科: 地球科学
• 关键词: Speed Control Valve Trispectrum Two-Dimensional Slice Two-Dimensional Wavelet Least Square Support Vector Machine Fault Diagnosis
• 年，卷（期）: 2020,（4）
• 页码范围: 765-773
• 页数: 9页
• 分类号: P31

摘要

The high-order spectrum can effectively remove Gaussian noise. The three-spectrum and its slices represent random signals from a higher probability structure. It can not only qualitatively describe the linearity and nonlinearity of vibration signals closely related to mechanical failures, Gaussian and non-Gaussian Performance, and can greatly i</span><span style="font-family:Verdana;"></span><span style="font-family:"">mprove the accuracy of mechanical fault diagnosis. The two-dimensional slices of trispectrum in normal and fault states show different peak characteristics. 2-D wavelet multi-level decomposition can effectively compress 2-D array information. Least squares support vector machine can obtain the global optimum under limited samples, thus avoiding the local optimum problem, and has the advantage of reducing computational complexity. In this paper, 2-D wavelet multi-level decomposition is used to extract features of trispectrum 2-D slices, and input LSSVM to diagnose the fault of the pressure reducing valve, which has achieved good results.