Intelligent mining methodology of product field failure data by fusing deep learning and association rules for after-sales service text

Abstract

The after-sales service text contains wealthy field failure data, which can be used to estimate the field reliability of product and its components. However, human-based data mining suffers from low efficiency and unstable quality. This study proposes an innovative intelligent mining methodology that combines the product failure dictionary (PFD), gate recurrent unit (GRU) and association rules (AR) to solve the problem. Firstly, the text is studied, and the segmented mining process is developed. Secondly, the PFD is edited according to the analysis results of a large product failure sample, and combined with GRU to construct the PFD-GRU efficient mining model. Thirdly, based on the failure determination criteria and the dependency syntax analysis results of the text, the AR-based mining method is formulated for re-mining the text that does not satisfy the determination threshold of the PFD-GRU model to establish the PFD-GRU-AR intelligent mining methodology. The case study results of the after-sales service text mining for four types of air-conditioner show that the segmented mining pattern can obtain better quality mining results than the one-stage mining pattern, and the P,R, and F1 of the PFD-GRU model have increased by at least 1.48%, 1.72% and 1.6% respectively compared to the corresponding indicator values of the GRU model. With the increase of the threshold value, the quality and robustness are improved and the mining results tend to be stable. As threshold value is 0.95, the PFD-GRU-AR methodology obtains minimum value for P,R, and F1 as 98.54%, 99.09% and 98.81% respectively.