A Natural Language Processing and Deep Learning based Model for Automated Vehicle Diagnostics using Free-Text Customer Service Reports

TL;DR

This paper presents a machine learning pipeline combining NLP and deep learning to automate vehicle diagnostics from customer reports, significantly improving validation accuracy and classification performance.

Contribution

It introduces a novel NLP and deep learning-based model that enhances vehicle fault detection and service request classification from free-text customer reports.

Findings

BiLSTM-CNN model improves validation accuracy by over 18%.

Enhanced NLP techniques increase classification metrics by over 25%.

ROC-AUC of 0.82 indicates strong model performance.

Abstract

Initial fault detection and diagnostics are imperative measures to improve the efficiency, safety, and stability of vehicle operation. In recent years, numerous studies have investigated data-driven approaches to improve the vehicle diagnostics process using available vehicle data. Moreover, data-driven methods are employed to enhance customer-service agent interactions. In this study, we demonstrate a machine learning pipeline to improve automated vehicle diagnostics. First, Natural Language Processing (NLP) is used to automate the extraction of crucial information from free-text failure reports (generated during customers' calls to the service department). Then, deep learning algorithms are employed to validate service requests and filter vague or misleading claims. Ultimately, different classification algorithms are implemented to classify service requests so that valid service requests can be directed to the relevant service department. The proposed model- Bidirectional Long Short Term Memory (BiLSTM) along with Convolution Neural Network (CNN)- shows more than 18\% accuracy improvement in validating service requests compared to technicians' capabilities. In addition, using domain-based NLP techniques at preprocessing and feature extraction stages along with CNN-BiLSTM based request validation enhanced the accuracy ($>25\%$), sensitivity ($>39\%$), specificity ($>11\%$), and precision ($>11\%$) of Gradient Tree Boosting (GTB) service classification model. The Receiver Operating Characteristic Area Under the Curve (ROC-AUC) reached 0.82.