Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making
A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). According to the experimental results, our expert model can detect 96.4% of failures within 50% of the sample. This suggests that our method is effective at diagnosing failures in railcars fleet.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingFault DiagnosisHybrid Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)
Health misinformation during the COVID-19 pandemic has significantly challenged public health efforts globally. This study applies the Elaboration Likelihood Model (ELM) to enhance misinformation detection on social medi…
Feature EngineeringA Framework for Selection of Machine Learning Algorithms Based on Performance Metrices and Akaike Information Criteria in Healthcare, Telecommunication, and Marketing Sector
The exponential growth of internet generated data has fueled advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) for extracting actionable insights in marketing,telecom, and health…
Quantum Machine Learning for Health State Diagnosis and Prognostics
Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laborato…
BIG-bench Machine LearningManagementQuantum Machine LearningLearning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge
A key bottleneck for developing dialog models is the lack of adequate training data. Due to privacy issues, dialog data is even scarcer in the health domain. We propose a novel method for creating dialog corpora which we…
RetrievalEnhancing Medical Learning and Reasoning Systems: A Boxology-Based Comparative Analysis of Design Patterns
This study analyzes hybrid AI systems' design patterns and their effectiveness in clinical decision-making using the boxology framework. It categorizes and copares various architectures combining machine learning and rul…
Data Integration