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An Intelligent Early Warning Method of press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Transactions of Nanjing University of Aeronautics and Astronautics 2020年第4期37卷 597-606页

作者： XUE Shanliang LI Chen College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is *** regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality *** preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and p weight(LAOpW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with p *** show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)*** application of LAOpW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

关键词： quality early warning outlier data detection linear regression local outlier factor based on area density and p weight(LAOpW) information entropy p weight

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