Abstract
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.
Highlights
Equipment in the national economic industries such as machinery, transportation, energy, and metallurgy are often under high load, variable working conditions, and continuous operation
For these major mechanical equipment, the quality of the equipment can be improved after the optimization of the design and manufacturing process, but it is still difficult to guarantee that there will be no failure during the service process [2]
This article focuses on the important scientific issues in the operation and maintenance of machinery and equipment at this stage, combined with the development plan of the national machinery and manufacturing science, researches the support vector machine model and state space model in the data-driven life prediction method, and uses system science to discover and understand the general law of life prediction of complex equipment based on theories and methods
Summary
Equipment in the national economic industries such as machinery, transportation, energy, and metallurgy (especially high-end, large, key electromechanical equipment) are often under high load, variable working conditions, and continuous operation. The safety and reliability of equipment has an important impact on the national economy and people’s livelihood, social stability, and national resources and environment For these major mechanical equipment, the quality of the equipment can be improved after the optimization of the design and manufacturing process, but it is still difficult to guarantee that there will be no failure during the service process [2]. This article focuses on the important scientific issues in the operation and maintenance of machinery and equipment at this stage, combined with the development plan of the national machinery and manufacturing science, researches the support vector machine model and state space model in the data-driven life prediction method, and uses system science to discover and understand the general law of life prediction of complex equipment based on theories and methods. This paper takes the vibration signal of mechanical equipment as the research object and studies the two key issues of vibration fault feature extraction and remaining life prediction
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