Transactions of Nanjing University of Aeronautics & Astronautics
An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression
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Abstract:
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 proposed. Linear 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 control. To 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 weight. Experiments 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) algorithm. The 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.
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TP301
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