보증데이터를 이용한 텍스트 마이닝 기반 고장 현상 예측 연구

Purpose: This paper uses unstructured warranty data, such as customer complaints and repair information, extracted through text mining and proposes a methodology for identifying and predicting automotive failure. Methods: The Word2Vec and CountVectorizer algorithms were used to determine the similarity between and frequency of the vectorized meanings of the words. A word embedding model was constructed on the bases of these distinct features of the words, and multimodal-deep neural network (DNN) modeling was performed to derive the prediction results of the failure phenomenon. A comparative analysis of the performance metrics of different combinations of the models was performed. Results: The model using both the CountVectorizer algorithm for extracting features within similar text clusters and the multimodal-DNN for training the data exhibited the best performance. Conclusion: This study shows the effects of different data structures and feature extraction algorithms on failure prediction performance. The developed model improves the prediction accuracy by applying the relevant feature extraction algorithm and text classification learning model.