A method for predicting the remaining life of a rubber hose

By collecting pressure and temperature signals in rubber hoses, constructing health indicators, and using deep learning models for adaptive modeling, the accuracy and stability issues of rubber hose life prediction are solved. This enables adaptation to complex working conditions and dynamic maintenance strategies, reducing safety risks and resource waste.

CN121920228BActive Publication Date: 2026-07-21HENGYU GRP HYDRAULIC FLUID TECH HEBEI CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HENGYU GRP HYDRAULIC FLUID TECH HEBEI CO LTD
Filing Date
2026-01-14
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing rubber hose life prediction technologies suffer from problems such as inaccurate estimation of single parameters, insufficient consideration of changes in operating conditions, and accumulation of model errors. These issues make it difficult to achieve accurate prediction of remaining life, leading to unreasonable maintenance strategies, safety risks, and resource waste.

Method used

By collecting pressure and temperature signals from rubber hoses, extracting multi-source degradation features, constructing health indicators, and using a deep learning model for adaptive modeling, combined with an online update mechanism to dynamically correct life prediction, an exponential continuous degradation model is established to predict the remaining life of rubber hoses.

Benefits of technology

It improves the accuracy and stability of life prediction, enhances the adaptability to complex operating conditions, realizes dynamic correction of remaining life, reduces maintenance costs and operational risks, and provides a scientific basis for maintenance decisions.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a rubber hose residual life prediction method, and belongs to the technical field of industrial data prediction based on machine learning; during service of the rubber hose, running state data of the rubber hose is collected, and effective state data sets for degradation analysis are obtained through preprocessing; in view of degradation characteristics of the rubber hose under the action of pressure fatigue and material aging, a plurality of degradation related features are extracted from the multi-source data, and a health index comprehensively reflecting the overall degradation state of the rubber hose is constructed; working condition information of pressure level and temperature condition is introduced, a rubber hose life degradation model is established, model parameters are identified through historical running data, so that the model can describe the evolution law of the health index under different working condition conditions; finally, the latest state data is continuously acquired and the health index is updated, and the rubber hose residual service time corresponding to the failure threshold is predicted by inputting the life degradation model.
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