A method and system for predicting the remaining service life of equipment under multiple operating conditions

By employing collaborative design with shared and private experts and a window-level state-driven routing mechanism, the contradiction between cross-condition adaptability and single-condition timing fidelity was resolved. This enabled accurate prediction of the remaining service life of equipment under multiple conditions, improved prediction accuracy and consistency, and provided reliable maintenance decision support.

CN122241292BActive Publication Date: 2026-08-11HUAZHONG UNIV OF SCI & TECH
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Patent Information

Application Number
CN202610716534.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-22
Publication Date
2026-08-11
Estimated Expiration
2046-05-22

AI Technical Summary

Technical Problem

Existing technologies struggle to strike a balance between cross-condition adaptability and single-condition time-series fidelity, which affects prediction accuracy.

Method used

A multi-condition equipment remaining service life prediction method is adopted. Through collaborative design by shared experts and private experts, the shared experts capture the general degradation trend across operating conditions, while the private experts fit the degradation mode of specific operating conditions. A window-level state-driven routing mechanism is adopted to dynamically select the expert network combination. Combined with state modulation feature embedding module and physical constraints, the generalization ability and temporal continuity of the model under multiple operating conditions are ensured.

Benefits of technology

It significantly improves prediction accuracy and timing consistency across multiple operating conditions, provides reliable maintenance decision-making support, and has engineering interpretability.

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Abstract

This invention belongs to the technical field of predictive maintenance of equipment, and discloses a method and system for predicting the remaining service life of equipment under multiple operating conditions. The method includes: constructing a time-series sample set and generating an operating condition vector based on the operating condition parameters of each sample; constructing a prediction model: a state-driven encoding module includes a state-driven hybrid expert layer, a private expert layer generates gating weights with window-level states, performs weighted output of the private expert layer, and combines the output of the shared expert layer to obtain high-dimensional time-series features; the window-level state is obtained based on the degradation information of the operating condition vector and time-series data; the prediction head module maps the high-dimensional time-series features to the predicted value of the remaining service life; and the remaining service life is predicted using the trained prediction model. This invention, through the collaborative design of shared and private experts and the adoption of a window-level state-driven routing mechanism, can effectively separate common trends across operating conditions from operating condition-specific degradation features, providing multi-operating-condition prediction accuracy.
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