Analysis method of dynamics and structure optimization for sealing mechanism under reducing vibration condition

Shang, Xin; Liu, Xin-Cheng

doi:10.1177/1464419318759157

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Analysis of Vibration Signal Parameters. Vibration analysis method is currently one of the most effective methods used in equipment monitoring [22][23][24][25]. Sensors are installed at appropriate positions inside the equipment base or cabinet, and portable data collectors or boards are used to collect equipment vibration signals.…”

Section: Construction Of Life Prediction Model For Remanufactured Mac...mentioning

confidence: 99%

Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

Kui

Luo

et al. 2021

Advances in Mathematical Physics

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Mechanical equipment is a key component of mechanical equipment, and its working condition is directly related to the overall performance of mechanical equipment. Accurate evaluation and prediction of the performance degradation trend of mechanical equipment is of great significance to ensure the reliability and safety of the mechanical equipment system. Based on the data of typical faulty equipment, this paper analyzes the energy characteristic parameters of mechanical equipment under different types and degrees of failure in the time domain. Using amplitude spectrum analysis, Hilbert envelope demodulation and wavelet packet decomposition method, and other vibration signal processing methods, preliminary extraction of multiple statistical feature parameters are given. Secondly, in view of the irrelevant and redundant components of multiple statistical parameters, a new method for extracting fault features of mechanical equipment based on variance value and principal component analysis is proposed. This method can effectively classify the fault status of mechanical equipment. The effectiveness of the method is verified by actual equipment signals. After that, the value extracted from the vibration signal of the double-row roller equipment is used as the degradation feature. In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. The 95% confidence interval of the predicted value is also given. The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time.

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Section: Construction Of Life Prediction Model For Remanufactured Mac...mentioning

confidence: 99%

Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

Kui

Luo

et al. 2021

Advances in Mathematical Physics

View full text Add to dashboard Cite

show abstract

Analysis method of dynamics and structure optimization for sealing mechanism under reducing vibration condition

Cited by 1 publication

References 19 publications

Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

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