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

• URL: http://arxiv.org/abs/2111.14977v1
• Date: Mon, 29 Nov 2021 21:41:34 GMT
• Title: A Natural Language Processing and Deep Learning based Model for Automated Vehicle Diagnostics using Free-Text Customer Service Reports
• Abstract summary: 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. Deep learning algorithms are employed to validate service requests and filter vague or misleading claims.
• Score: 3.970010025578998
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Artificial Intelligence Based Predictive Maintenance for Electric Buses [0.0]
  This study employs a graph-based feature selection method to analyze relationships among CAN Bus parameters of electric buses.<n>The raw data collected over two years underwent extensive preprocessing to ensure data quality and consistency.<n>The developed system effectively predicts vehicle alarms, enhances feature interpretability, and supports proactive maintenance strategies aligned with Industry 4.0 principles.
  arXiv  Detail & Related papers  (2025-10-27T21:39:25Z)
• AutoGD: Automatic Learning Rate Selection for Gradient Descent [54.195493042469]
  We introduce AutoGD: a gradient descent method that automatically determines whether to increase or decrease the learning rate at a given iteration.<n>We show that we can recover the optimal rate of GD (up to a constant) for a broad class of functions without knowledge of smoothness constants.
  arXiv  Detail & Related papers  (2025-10-10T23:47:22Z)
• Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods [51.28632782308621]
  We focus on overtake detection using Controller Area Network (CAN) bus data collected from five in-service trucks provided by the Volvo Group.<n>We evaluate three common classifiers for vehicle manoeuvre detection, Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM)<n>Our pertruck analysis also reveals that classification accuracy, especially for overtakes, depends on the amount of training data per vehicle.
  arXiv  Detail & Related papers  (2025-07-01T09:20:41Z)
• Test-time adaptation for geospatial point cloud semantic segmentation with distinct domain shifts [6.80671668491958]
  Test-time adaptation (TTA) allows direct adaptation of a pre-trained model to unlabeled data during inference stage without access to source data or additional training. We propose three domain shift paradigms: photogrammetric to airborne LiDAR, airborne to mobile LiDAR, and synthetic to mobile laser scanning. Experimental results show our method improves classification accuracy by up to 20% mIoU, outperforming other methods.
  arXiv  Detail & Related papers  (2024-07-08T15:40:28Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• AutoFT: Learning an Objective for Robust Fine-Tuning [60.641186718253735]
  Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. Current approaches to robust fine-tuning use hand-crafted regularization techniques. We propose AutoFT, a data-driven approach for robust fine-tuning.
  arXiv  Detail & Related papers  (2024-01-18T18:58:49Z)
• GEqO: ML-Accelerated Semantic Equivalence Detection [3.5521901508676774]
  Common computation is crucial for efficient cluster resource utilization and reducing job execution time. detecting equivalence on large-scale analytics engines requires efficient and scalable solutions that are fully automated. We propose GEqO, a portable and lightweight machine-learning-based framework for efficiently identifying semantically equivalent computations at scale.
  arXiv  Detail & Related papers  (2024-01-02T16:37:42Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions [18.36339203254509]
  F introduces a lightweight, function-based cloud execution model that finds its relevance in a range of applications like IoT-edge data processing and anomaly detection.
  arXiv  Detail & Related papers  (2023-08-11T04:41:19Z)
• AutoCure: Automated Tabular Data Curation Technique for ML Pipelines [0.0]
  We present AutoCure, a novel and configuration-free data curation pipeline. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction. In practice, AutoCure can be integrated with open source tools to promote the democratization of machine learning.
  arXiv  Detail & Related papers  (2023-04-26T15:51:47Z)
• LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision [63.08516384181491]
  We present LogLAB, a novel modeling approach for automated labeling of log messages without requiring manual work by experts. Our method relies on estimated failure time windows provided by monitoring systems to produce precise labeled datasets in retrospect. Our evaluation shows that LogLAB consistently outperforms nine benchmark approaches across three different datasets and maintains an F1-score of more than 0.98 even at large failure time windows.
  arXiv  Detail & Related papers  (2021-11-02T15:16:08Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.