A novel model for product defect detection based on the automatic aspect term extraction

Industry 4.0 is a global modernization movement in the manufacturing industry, heralding a new paradigm of digitalization, decentralized control, and autonomy in the realm of the manufacturing industry. From the perspective of quality management, Product Defect Detection (PDD) is an important industrial application throughout the entire product life-cycle management, which aims to detect the defects of products. Product defect, which essentially involves safety-related defect and performance defect, has been drawing widespread attention from business and academia due to their relevance to failure costs and the user experience. With the rapid development of social media, performance defects are increasingly discussed in online reviews and forums, and there also exist some text-mining-based methods to extract the defect information. However, the existing methods suffer from the limitation of expert dependence and failure detection of unseen defects. To fill these gaps, we first creatively introduce the semi-structured eWOM (electronic word-of-mouthh) data to avoid manual work. Secondly, we designed the automatic aspect term extraction (ATE) module to extract the product components to tackle the problem of the predefined corpus. Besides, detailed hierarchical defect information can also be provided to support the manufacturer in locating the defect component. Thirdly, an aspect-level text classification model is introduced with the advantage of the local-context attention mechanism. A case study in the automotive industry is provided to validate the effectiveness of the proposed model, and a wide range of experiments are offered to illustrate the high performance of the proposed model.