SOUND-BASED ROLLER FAULT DETECTING METHOD BY USING DOUBLE-PROJECTION NEIGHBORHOODS PRESERVING EMBEDDING

    公开(公告)号:US20230348197A1

    公开(公告)日:2023-11-02

    申请号:US17640035

    申请日:2021-07-21

    CPC classification number: B65G43/00 G06F17/11

    Abstract: Provided is a sound-based roller fault detecting method by using double-projection neighborhoods preserving embedding, including: acquiring sound data during operation of a roller, performing a wavelet transform energy feature extraction on normal data in the data to obtain wavelet transform energy feature data, then performing double-projection neighborhoods preserving embedding feature extraction on the wavelet energy feature data to obtain an optimal projection matrix of the feature data, establishing a detection model, constructing T2 statistics of a feature space and a residual space of normal sound data, determining detection control limits according to the T2 statistics by a kernel density estimation method, and further judging whether newly acquired data has faults. According to the present method, main features of the data can be extracted under the conditions of non-dimensional reduction and dimensionnal reduction, and thus the present method can achieve the purpose of increasing fault detection accuracy.

    METHOD FOR DETECTING ABNORMAL WORKING CONDITIONS OF MULTI-VIEW DATA BASED ON FEATURE REGRESSION

    公开(公告)号:US20250103038A1

    公开(公告)日:2025-03-27

    申请号:US18724548

    申请日:2022-01-11

    Abstract: Provided is a method for detecting abnormal working conditions of multi-view data based on feature regression. By the method, data capable of being acquired in a production process is collected together, a big data pool is established, and historical data information is fully utilized; by analyzing the data in the data pool, the method for detecting abnormal working conditions based on the multi-view data is established by a feature regression method, and a general mathematical model is established for preprocessed data acquired by different sensors; left and right projection vectors solved through the model can make similar sample points have better clustering effects in a low dimensional space; and by comparing a correlation between vectors after dimensionality reduction and various category vectors, production working conditions at a current time can be recognized.

    CALCULATING AND REAL-TIME MONITORING METHOD FOR BOUNDARY OF BLAST FURNACE TUYERE RACEWAY

    公开(公告)号:US20240211662A1

    公开(公告)日:2024-06-27

    申请号:US18555827

    申请日:2021-12-03

    CPC classification number: G06F30/27 G06F2113/08 G06F2119/08

    Abstract: Provided is a calculating and real-time monitoring method for a boundary of a blast furnace tuyere raceway including: the steps of firstly establishing a depth calculation model for the raceway, and further obtaining a calculation formula for a depth of the raceway so as to obtain a change law of the depth of the raceway; establishing a boundary model for the raceway through the depth calculation model for the raceway, and determining a calculation formula for the boundary of the raceway; obtaining modelling parameters, analyzing an impact of the modelling parameters on the boundary model, and determining main parameters that affect the boundary of the raceway; finally, solving a height of the raceway; and when the height or depth of the raceway exceeds a set range, adjusting a blast wind pressure and a blast wind volume to restore the height or depth of the raceway to a normal range.

    MODELING METHOD FOR SOFT MEASUREMENT OF TEMPERATURE OF BLAST FURNACE TUYERE RACEWAY

    公开(公告)号:US20230205952A1

    公开(公告)日:2023-06-29

    申请号:US17769002

    申请日:2021-12-03

    CPC classification number: G06F30/27 G06V10/56

    Abstract: A modeling method for soft measurement of temperature of a blast furnace tuyere raceway includes: collecting picture data of flame combustion at the blast furnace tuyere raceway, physical variable data reflecting operation states of a blast furnace and combustion temperature data of the blast furnace tuyere raceway; extracting characteristics of the picture data of the flame combustion; constructing a multi-kernel least squares support vector regression model based on Pearson correlation coefficient method and least squares support vector regression algorithm as a soft measurement model; optimizing parameters of the soft measurement model by using sine cosine optimization algorithm; and taking optimal kernel function parameters of the picture data, kernel function parameters of the physical variable data and regularization parameters in the multi-kernel least squares support vector regression model as final parameters of the soft measurement model, and achieving prediction and calculation of the combustion temperature of the blast furnace tuyere raceway.

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