A method and system for predicting the wear life of a piston ring assembly

By acquiring and processing piston ring group working status data in real time and building and optimizing wear life prediction models, the problem of inaccurate wear monitoring in traditional methods is solved, and more efficient equipment maintenance and more reliable data communication is achieved.

CN119293619BActive Publication Date: 2025-03-18XIAMEN UNIV OF TECH
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Patent Information

Application Number
CN202411834172.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-13
Publication Date
2025-03-18
Estimated Expiration
2044-12-13

AI Technical Summary

Technical Problem

The traditional piston ring group wear monitoring method relies on regular inspections and manual judgments, making it difficult to accurately predict the time of failure, and poses a major safety hazard.

Method used

Real-time acquisition of piston ring group working status data, and filling in missing data through improved wavelet transform denoising and KNN interpolation methods, standardization and normalization processing are built to build an over-limit learning machine model and optimize parameters. Combined with the white whale optimization algorithm, the wear life of piston ring group is predicted.

Benefits of technology

It improves the accuracy of wear life prediction, optimizes equipment maintenance decisions, enhances the reliability and security of data communication, improves the intelligence and automation level of the system, effectively improves equipment management efficiency, and reduces failure rate and maintenance costs.

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Abstract

The present invention relates to the technical field of piston ring group wear monitoring, specifically a method and system for predicting the wear life of a piston ring group; firstly, the working status data of the piston ring group is collected in real time, and the improved wavelet transform is used for denoising and the missing data is processed by the KNN interpolation method, then the data after standardization and normalization is processed, and the filter label generation and audit mechanism are used to ensure the ordered homogeneity of the data, and an ultra-limited learning machine model is constructed, and the hidden layer output matrix is ​​calculated by randomly generating hidden layer weights and bias vectors, and the model parameters are optimized by the white whale optimization algorithm to improve the prediction accuracy, and then the remaining life is predicted, and the final prediction result is used to calculate the fault risk coefficient, and compared with the preset threshold value to provide maintenance suggestions, and the real-time data and prediction results are displayed to assist users in making maintenance decisions. The present invention combines advanced signal processing technology and machine learning algorithms to improve the maintenance efficiency of the piston ring group and the reliability of equipment operation.
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