Method for detecting abnormal working conditions of multi-view data based on feature regression

Patent metadata

Field	Value
Publication number	US-2025103038-A1
Application number	US-202218724548-A
Country	US
Kind code	A1
Filing date	Jan 11, 2022
Priority date	Jan 11, 2022
Publication date	Mar 27, 2025
Grant date	—

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.

First claim

1 . A method for detecting abnormal working conditions of multi-view data based on feature regression, comprising:
acquiring sample data under different working conditions in an industrial production process, and preprocessing the acquired sample data; establishing the method for detecting abnormal working conditions based on the multi-view data by a feature regression method; and performing an on-line abnormal working condition detection by the method for detecting abnormal working conditions based on the multi-view data; the method comprising the following steps: Step 1: acquiring the sample data under different working conditions in an actual industrial production process, denoted as {X j i |t=1, 2 . . . , w; j i =1, 2, . . . , n}, wherein X j i is j th sample data in an i th view, w is the number of views, and n is the number of samples under different views; Step 2: preprocessing the acquired sample data under different working conditions; Step 3: after preprocessing the sample data, establishing the method for detecting abnormal working conditions based on the multi-view data by the feature regression method; and Step 4: performing the on-line abnormal working condition detection by the method for detecting abnormal working conditions based on the multi-view data, wherein Step 2 comprises: if the sample data X j i in the i th view is image data, firstly performing graying processing and normalization processing on the image data, then obtaining an average value { X th |h=1, 2, . . . , r} of all data under different working conditions according to the acquired sample data, wherein r is the number of working condition categories, and further obtaining the preprocessed sample data:

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and if the sample data X j i ∈i q×l in the i th view is vector data, the preprocessed sample data is:

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Assignees

• Univ Northeastern

Inventors

• Ma Chuan
• Zhang Yingwei
• Feng Lin

Classifications

• G05B19/4184

  characterised by fault tolerance, reliability of production system · CPC title

• G05B23/0281 Primary

  Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis · CPC title

• G05B23/02

  Electric testing or monitoring · CPC title

• G05B19/418

  Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] · CPC title