The quality inspection method of piston compressor assisted with the XGBOD model

Abstract

For most manufacturers of piston compressors, all of the compressors should be tested and some key parameters are measured for the procedure of quality inspection. Traditionally, quality inspection of compressors is based on the thresholds of the key parameters (i.e., based on rules). However, due to the operation conditions of the production line varying slightly and uncertainty of measurement, the strictly fixed thresholds can often lead to misjudgment of quality inspection. To improve the correctness of quality inspection, this paper proposed a quality inspection method assisted with the Extreme Gradient Boosting Outlier Detection (XGBOD) model. XGBOD model is set up by learning anomalous patterns from historical test data, and it is used to identify finally whether it is qualified for the compressor that has been judged as unqualified by rules. The verification results demonstrate the effectiveness of the proposed quality inspection method. The XGBOD algorithm has a lower misjudgment rate than other anomaly detection algorithms. Assisted with the XGBOD model, the misjudgment rates of the proposed quality inspection method for Type-A and B compressors are reduced significantly from 15.75 % and 18.57 % to 1.18 % and 0.72 % respectively. Therefore, the quality inspection method assisted with the XGBOD model can effectively improve the correctness of the quality inspection.