A data detection method and system for valve faults

By performing time synchronization, delay alignment, and coherence analysis on multi-channel sensor data, a coherence relation matrix is ​​constructed. Spatial transformation and adaptive filtering are then performed, solving the accuracy and reliability problems of valve fault detection in existing technologies and enabling accurate identification and predictive maintenance of early complex faults.

CN121901932BActive Publication Date: 2026-06-30HAOGONG VALVE CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HAOGONG VALVE CO LTD
Filing Date
2026-03-20
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing valve fault detection methods cannot effectively uncover the collaborative change patterns between multi-sensor signals, resulting in insensitivity to early fault responses, difficulty in distinguishing complex faults, and a high false alarm rate under high background noise, which limits the accuracy and reliability of predictive maintenance.

Method used

By acquiring multi-channel sensor data for time synchronization and delay alignment, a coherence relation matrix is ​​constructed, spatial transformation processing is performed, adaptive filtering and time-frequency domain feature extraction are carried out, and combined with feature evolution trend analysis, valve fault identification information is generated.

Benefits of technology

It significantly improves the accuracy of early composite fault identification, reduces the risk of misjudgment, enhances the universality and reliability of the method, and enables adaptive detection of valve faults under different operating conditions.

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Abstract

This invention discloses a data detection method and system for valve faults, relating to the field of fault detection technology. The method includes: acquiring multi-channel sensor data under valve operating conditions; performing time synchronization and delay alignment processing on the multi-channel sensor data based on valve operating conditions; performing spatial coherence analysis on the spatiotemporally aligned multi-channel signal set and constructing a coherence relation matrix; using the coherence relation matrix as a transformation constraint, performing coherence-preserving spatial transformation processing on the spatiotemporally aligned multi-channel signal set; and performing adaptive filtering on the transformed signal representation based on valve fault characteristics to obtain an enhanced signal representation. This invention, by extracting and constructing a coherence relation matrix that can characterize the cooperative relationship between signals and valve faults from the spatiotemporally aligned multi-channel signals, and using this as a constraint for targeted transformation of the signal space, can effectively enhance the aggregation and separability of weak cooperative features characterizing multi-physics coupled faults in the transform domain.
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