Unsupervised Anomaly Analysis-Based Manufacturing Quality Test and Grading Method for Combine Harvesters.

Manufacturing quality tests mainly depend on manual methods, and most quality evaluations rely on the quality evaluation intervals, which lacks quantitative means for grading. As a consequence, it is challenging for the test process to find hidden quality problems early. In this paper, we conduct research on the test and grading method for combine harvester manufacturing quality and propose a hybrid method of "end-of-line test & secondary grading", including a LabVIEW (R)-based test system and LOF (Local Outlier Factor)-based secondary grading method. The developed test system collects data from multiple working systems in a combine harvester and constructs the dataset. We further investigate eight unsupervised anomaly detection methods for grading harvester quality. Taking the evaluation indices of intracluster compactness (CP), intercluster separation (SP), and anomaly hit amount, we comprehensively assess these methods for different system performances in the harvester. The assessment results validate the LOF as being better among the eight anomaly detection methods, which obtains smaller CP integral values of 241.49, 433.51, and 309.93 and larger SP integral values of 383.82, 565.07, and 543.80 for the electrical system, hydraulic system, and braking performance datasets, respectively. Meanwhile, the LOF also obtains a good anomaly hit amount. Finally, the end-of-line test system integrates the advanced LOF-based grading method to quantitatively assess the harvester performance and identify potential quality-violated parts over time. The developed system proposes a solution for the automation and optimization demand in agricultural machinery manufacturing evaluation and provides technical guidance for product operation and maintenance management.