Integrating Domain Knowledge Research Articles

Computationally Efficient Inference via Time-Aware Modular Control Systems

Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed meta control)—an architecture for learning optimal control by employing a physics-informed neural network when the state space is too large for reward-based learning. Second, we offer a way to leverage impulse response as a tool for system modeling and control. We propose IMPULSTM, a novel approach for incorporating time awareness into recurrent neural networks designed to accommodate irregular sampling rates in the signal. Third, we propose DIMAS, a modular approach to increasing computational efficiency in distributed control systems via domain-knowledge integration. We analyze the performance of the first two contributions on a set of corresponding benchmarks and then showcase their combined performance as a domain-informed distributed control system. The proposed approaches show satisfactory performance both individually in their respective applications and as a connected system.

Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery

Early fault detection in rotating machinery needs careful expert analysis of vibration data for monitoring a component state. Online methods that automatically set a threshold and raise an alarm when the vibration signature is anomalous are meant to efficiently manage key assets in a preventive maintenance plan. In recent years a focus has raised on data driven methods in parallel with the increasing attention towards machine learning and, particularly, deep learning models. In this regard, for rotating equipment components, an important aspect relates to labelled data scarcity for supervised training. On the other hand, the advent of the Internet of Things allows to gather data from multiple assets with relevant information on the asset state itself. Self-supervised learning methods in deep learning application are currently tackling this problem. Investigating Self-learning approaches to integrate domain knowledge and learn relevant features from unlabeled data is therefore important for condition monitoring applications. In this paper a methodology is proposed based on cycle consistency representation learning for training an embedder network on univariate unlabeled data. In order to learn a distance metric in the embedding space the original data are transformed to generate sequences of augmented inputs to enforce learnable pattern similarity in the augmented pairs. A differentiable cycle-consistency loss is chosen to maximize the numbers of augmented pairs in the learned embedding space that have minimum features distance. The pretext task in the described self-supervised setting aims to train a feature extractor for discriminating dissimilar samples in the embedding space by a distance metric and to provide a useful representation for down-stream tasks. The paper analyzes the performance of the approach for anomaly detection in rotating machinery. The methodology is tested on vibration data provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, considering different accelerated life test campaigns. The data were collected to monitor the fault development in bearings and the model shows how the learned embedding space discriminates effectively anomalous samples from normal ones in the degradation stages of the bearings.

Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions. To address the simulation-to-reality gap, data augmentation with controlled sensor noise is applied during training, ensuring robustness and generalization for real-world deployment without fine-tuning. Training results underscore HDRS’s superior convergence speed, training stability, and policy learning efficiency compared to baselines. Simulation and real-world evaluations establish HDRS-DRL as a competitive alternative, outperforming traditional approaches, and offering practical applicability in industrial settings.

Domain knowledge-powered attention for air traffic management hazardous events classification

The automated classification of hazardous events in Air Traffic Management (ATM) involves concise texts with specialized terminology. The problem is currently addressed by machine learning text classification methods; however, some methods overlook domain knowledge, while others require it excessively. This research aims to identify a process for integrating low-cost, text-based domain knowledge into classification to address the issue of domain shift. To achieve this, the Wide and Deep Bidirectional Encoder Representations from Transformers (WD-BERT) model is proposed, featuring a unique knowledge-powered attention mechanism. WD-BERT's Wide Attention module assesses the compatibility between input text and domain knowledge to determine class probabilities, while its Deep Attention module extracts contextual features guided by domain knowledge. Additionally, a text mining method is employed to extract domain knowledge from ATM regulation texts. The model is trained and evaluated using a dataset derived from the China ATM Hazard Source Database. It achieves a multi-label classification accuracy of 80.24% and an F1-micro score of 0.88, outperforming comparative models with state-of-the-art performance. Breakdown analyses of the model indicate that the carefully designed attention mechanism endows WD-BERT with the ability to integrate domain knowledge, allowing it to excel in contexts involving complex sentences, multiple terms, and domain shifts. The innovation of this study lies in the proposed domain knowledge-powered attention mechanism, which allows simply organized domain knowledge to effectively guide ATM hazardous events classification, alleviating the impact of domain shift. This method can also be applied to other vertical domains without knowledge graphs.

Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.

Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.

Enhancing Supply Chain Resilience through Emerging Technologies: A Holistic Approach to Digital Transformation

This article investigates the critical role of domain knowledge in enhancing the effectiveness and accuracy of data analysis across various industries. Through a comprehensive examination of case studies in finance, healthcare, and retail sectors, we demonstrate how industry-specific expertise significantly improves data interpretation, metric selection, and the derivation of actionable insights. The article highlights the synergistic relationship between technical, analytical skills and a deep understanding of industry contexts, revealing that domain experts are better positioned to identify meaningful patterns, develop accurate predictive models, and generate more relevant insights for stakeholders. We provide practical strategies for data analysts to acquire and integrate domain knowledge into their analytical practices, including collaboration with subject matter experts, targeted industry research, and participation in sector-specific training. The article also explores challenges such as potential biases, balancing generalist and specialist skills, and keeping pace with rapidly evolving industries. Furthermore, we examine future directions, including the role of AI and machine learning in augmenting domain expertise and the emergence of new interdisciplinary approaches and industry-specific tools. Our findings have significant implications for both aspiring data analysts seeking to specialize in specific industries and organizations aiming to optimize their analytical capabilities in an increasingly complex data landscape.

Differentiable vertex fitting for jet flavor tagging

This work explores the use of differentiable programming to integrate domain knowledge, in the form of domain specific software, into neural networks to develop scientific machine learning systems. We propose a differentiable vertex fitting algorithm that estimates the crossing point of multiple curves. In the high energy physics setting, these curves are defined by particle equations of motion and the crossing point represents the origin of particle production. This differentiable vertex fitting algorithm can be seamlessly integrated into neural networks, and we show its utility and efficacy in the high energy physics application of the classification of jets, i.e., collimated streams of particles in particle detectors whose originating parent particle we aim to classify. We demonstrate how differentiable vertex fitting can be integrated into larger transformer-based models for jet flavor tagging and show improvements in heavy flavor jet classification when compared to baseline models. Published by the American Physical Society 2024

Novel spatio-temporal attention causal convolutional neural network for multi-site PM2.5 prediction

Multi-site PM2.5 prediction has emerged as a crucial approach, given that the accuracy of prediction models based solely on data from a single monitoring station may be constrained. However, existing multi-site PM2.5 prediction methods predominantly rely on recurrent networks for extracting temporal dependencies and overlook the domain knowledge related to air quality pollutant dispersion. This study aims to explore whether a superior prediction architecture exists that not only approximates the prediction performance of recurrent networks through feedforward networks but also integrates domain knowledge of PM2.5. Consequently, we propose a novel spatio-temporal attention causal convolutional neural network (Causal-STAN) architecture for predicting PM2.5 concentrations at multiple sites in the Yangtze River Delta region of China. Causal-STAN comprises two components: a multi-site spatio-temporal feature integration module, which identifies temporal local correlation trends and spatial correlations in the spatio-temporal data, and extracts inter-site PM2.5 concentrations from the directional residual block to delineate directional features of PM2.5 concentration dispersion between sites; and a temporal causal attention convolutional network that captures the internal correlation information and long-term dependencies in the time series. Causal-STAN was evaluated using one-year data from 247 sites in mainland China. Compared to six state-of-the-art baseline models, Causal-STAN achieves optimal performance in 6-hour future predictions, surpassing the recurrent network model and reducing the prediction error by 8%–10%.

A review of feature selection strategies utilizing graph data structures and Knowledge Graphs.

Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature selection (FS) within KGs, emphasizing their roles in enhancing machine learning (ML) model efficacy, hypothesis generation, and interpretability. Through this comprehensive review, we aim to catalyze further innovation in FS for KGs, paving the way for more insightful, efficient, and interpretable analytical models across various domains. Our exploration reveals the critical importance of scalability, accuracy, and interpretability in FS techniques, advocating for the integration of domain knowledge to refine the selection process. We highlight the burgeoning potential of multi-objective optimization and interdisciplinary collaboration in advancing KG FS, underscoring the transformative impact of such methodologies on precision medicine, among other fields. The paper concludes by charting future directions, including the development of scalable, dynamic FS algorithms and the integration of explainable AI principles to foster transparency and trust in KG-driven models.

Chemical reaction enhanced graph learning for molecule representation.

Molecular representation learning (MRL) models molecules with low-dimensional vectors to support biological and chemical applications. Current methods primarily rely on intrinsic molecular information to learn molecular representations, but they often overlook effectively integrating domain knowledge into MRL. In this article, we develop a reaction-enhanced graph learning (RXGL) framework for MRL, utilizing chemical reactions as domain knowledge. RXGL introduces dual graph learning modules to model molecule representation. One module employs graph convolutions on molecular graphs to capture molecule structures. The other module constructs a reaction-aware graph from chemical reactions and designs a novel graph attention network on this graph to integrate reaction-level relations into molecular modeling. To refine molecule representations, we design a reaction-based relation learning task, which considers the relations between the reactant and product sides in reactions. In addition, we introduce a cross-view contrastive task to strengthen the cooperative associations between molecular and reaction-aware graph learning. Experiment results show that our RXGL achieves strong performance in various downstream tasks, including product prediction, reaction classification, and molecular property prediction. The code is publicly available at https://github.com/coder-ACAC/RLM.

A Domain-Specific Lexicon for Improving Emergency Management in Gas Pipeline Networks through Knowledge Fusing

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks.

An intelligent decision support framework for nursing home resource planning with enhanced heterogeneous service demand modeling

Demand-based nursing home resource planning is of great importance to ensure adequate resources (e.g., beds and staffs) available to provide care services with desired quality, yet challenging. The challenge mainly lies in modeling heterogeneous demand of nursing home residents, reflected by various individual characteristics, diverse dwelling duration with multiple competing discharge dispositions, and diverse daily service need. Existing studies often assumed a homogeneous population of patients and neglected the complexity of demand heterogeneity and uncertainty, leading to biased demand estimation and misguided decisions. The objective of this work is to improve nursing home resource planning decisions in response to the complex demand heterogeneity and uncertainty. To address the challenges, we propose a novel knowledge-guided and data-driven decision support framework. This is the first work of integrating domain knowledge with predictive and decision analytics to enhance modeling fidelity and decision performance for nursing home resource planning. Specifically, to effectively capture different aspects of heterogeneous demand, we develop a novel knowledge-guided demand modeling module with predictive models, including a length-of-stay model with competing risk for duration analysis, a tree-based system for learning daily service need variations, and a demand simulator for capturing uncertainty of fluctuating demand. Moreover, to determine optimal capacity and staffing decisions under demand heterogeneity and uncertainty, we develop a demand-based decision-making module with effective optimization models and solution algorithms, ensuring satisfactory quality of care at reduced costs. Furthermore, to demonstrate the improved prediction and decision performances of the proposed framework, we provide a proof-of-the-concept case study using real data from our industrial collaborator and investigate how demand heterogeneity and uncertainty will impact resource planning decisions. The proposed framework also demonstrates its appealing adaptability under changing resident census compositions.

Quantitatively determining melting properties for energetic compounds via knowledge-infused molecular graphs and interpretable deep learning

The melting properties of energetic compounds are critical to their performances, but challenges persist in understanding the molecular features and design strategies that drive these properties. Integrating domain knowledge into data-driven approaches for predicting melting properties enhances the generation of comprehensive insights and enables the construction of interpretable prediction models. For this purpose, a knowledge-infused molecular graphs (KIMGs) were devised to describe the characters of energetic compounds, by which prediction models were developed in conjunction with the message passing neural networks. A melting-point dataset composed of around 30,000 melt-castable energetic compounds was constructed, and a collection of 29 key descriptors relevant to melting behaviors is integrated in KIMGs. The model achieved the best mean absolute error of 10.93 K for the melting point prediction. The model interpretability from both molecular graphs and feature importances offer insights for understanding the complex interplay that determine the melting properties of energetic compounds. This work enables researchers not only to predict melting behaviors with enhanced accuracy but also applicable to establishing other quantitively structure-property relationships.

A Novel Hybrid Deep Learning Framework for Evaluating Field Evapotranspiration Considering the Impact of Soil Salinity

AbstractAccurate evaluation of evapotranspiration (ET) is crucial for efficient agricultural water management. Data‐driven models exhibit strong predictive ET capabilities, yet significant limitations like naive extrapolation hamper wider generalization. In this perspective, we explore a novel hybrid deep learning (DL) framework to integrate domain knowledge and demonstrate its potential for evaluating ET under the influence of soil salinity. Specifically, we integrated physical constraints from process models (Penman‐Monteith or Shuttleworth‐Wallace) and salinity‐induced stomatal stress mechanisms into the DL algorithm, and evaluated its performance by comparing four diverse scenarios. Results demonstrate that hybrid DL framework offers a promising alternative for ET estimation, achieving comparable accuracy to pure DL during training and validation. Nonetheless, due to the limited available measurements, data‐driven model may not adequately capture plant responses to salt stress, leading to significant prediction biases observed during independent testing. Encouragingly, the hybrid DL model (DL‐SS) integrating Shuttleworth‐Wallace and salinity‐induced stomatal stress mechanisms demonstrated enhanced interpretability, generalizability, and extrapolation capabilities. During testing, DL‐SS consistently showed optimal performance, yielding root mean square error (RMSE) values of 37.4 W m−2 for sunflower and 39.2 W m−2 for maize. Compared to traditional Jarvis‐type approaches (JPM and JSW) and pure DL model during testing, DL‐SS achieved substantial reductions in RMSE values: 51%, 33%, and 43% for sunflower, and 45%, 31%, and 35% for maize, respectively. These findings highlight the importance of integrating prior scientific knowledge into data‐driven models to enhance extrapolation capability of ET modeling, especially in salinized regions where conventional models may struggle.

Interpretable Representation and Customizable Retrieval of Traffic Congestion Patterns Using Causal Graph-Based Feature Associations

The substantial increase in traffic data offers new opportunities to inspect traffic congestion dynamics from different perspectives. This paper presents a novel framework for the interpretable representation and customizable retrieval of traffic congestion patterns using causal relation graphs, which harnesses many of these opportunities. By integrating domain knowledge with innovative data management techniques, we address the challenges of effectively handling and retrieving the growing volume of traffic data for diverse analytical purposes. The framework leverages causal graphs to encode traffic congestion patterns, capturing fundamental phenomena and their spatiotemporal relationships, thus facilitating an interpretable high-level view of traffic dynamics. Moreover, a customizable similarity measurement function is introduced based on inexact graph matching, allowing users to tailor the retrieval process to specific requirements. This framework’s capability to retrieve customizable and interpretable congestion patterns is demonstrated through extensive experiments with real-world highway traffic data in the Netherlands, highlighting its value in supporting diverse data-driven studies and applications.

Demystifying XAI: Requirements for Understandable XAI Explanations.

This paper establishes requirements for assessing the usability of Explainable Artificial Intelligence (XAI) methods, focusing on non-AI experts like healthcare professionals. Through a synthesis of literature and empirical findings, it emphasizes achieving optimal cognitive load, task performance, and task time in XAI explanations. Key components include tailoring explanations to user expertise, integrating domain knowledge, and using non-propositional representations for comprehension. The paper highlights the critical role of relevance, accuracy, and truthfulness in fostering user trust. Practical guidelines are provided for designing transparent and user-friendly XAI explanations, especially in high-stakes contexts like healthcare. Overall, the paper's primary contribution lies in delineating clear requirements for effective XAI explanations, facilitating human-AI collaboration across diverse domains.

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records.

Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. This approach not only addresses the heterogeneous nature of domain knowledge but also recognizes the unique data and knowledge repositories of individual health care institutions, necessitating careful and respectful management of proprietary information. This study aimed to enhance the clinical relevance and interpretability of PLMs by integrating external knowledge in a manner that respects the diversity and proprietary nature of health care data. We hypothesize that domain knowledge, when captured and distributed as stand-alone modules, can be effectively reintegrated into PLMs to significantly improve their adaptability and utility in clinical settings. We demonstrate that through adapters, small and lightweight neural networks that enable the integration of extra information without full model fine-tuning, we can inject diverse sources of external domain knowledge into language models and improve the overall performance with an increased level of interpretability. As a practical application of this methodology, we introduce a novel task, structured as a case study, that endeavors to capture physician knowledge in assigning cardiovascular diagnoses from clinical narratives, where we extract diagnosis-comment pairs from electronic health records (EHRs) and cast the problem as text classification. The study demonstrates that integrating domain knowledge into PLMs significantly improves their performance. While improvements with ClinicalBERT are more modest, likely due to its pretraining on clinical texts, BERT (bidirectional encoder representations from transformer) equipped with knowledge adapters surprisingly matches or exceeds ClinicalBERT in several metrics. This underscores the effectiveness of knowledge adapters and highlights their potential in settings with strict data privacy constraints. This approach also increases the level of interpretability of these models in a clinical context, which enhances our ability to precisely identify and apply the most relevant domain knowledge for specific tasks, thereby optimizing the model's performance and tailoring it to meet specific clinical needs. This research provides a basis for creating health knowledge graphs infused with physician knowledge, marking a significant step forward for PLMs in health care. Notably, the model balances integrating knowledge both comprehensively and selectively, addressing the heterogeneous nature of medical knowledge and the privacy needs of health care institutions.

Harnessing machine learning for predictive maintenance in energy infrastructure: A review of challenges and solutions

Predictive maintenance (PdM) has emerged as a vital strategy for optimizing the reliability and efficiency of energy infrastructure. In this paper, we present a comprehensive review of the challenges and solutions associated with harnessing machine learning (ML) techniques for predictive maintenance in the energy sector. The adoption of ML algorithms in predictive maintenance holds immense promise for mitigating equipment failures, reducing downtime, and optimizing maintenance schedules. However, several challenges impede the effective implementation of ML-based PdM strategies. These challenges include the need for large and high-quality data sets, the complexity of integrating heterogeneous data sources, and the interpretability of ML models in real-world settings. To address these challenges, we discuss various solutions and best practices. These include data preprocessing techniques to handle noisy and incomplete data, feature engineering methods for extracting meaningful insights, and model interpretability approaches for enhancing trust and understanding of ML predictions. Additionally, we explore the integration of domain knowledge and human expertise into ML algorithms to improve predictive accuracy and relevance. Furthermore, we examine the role of edge computing and distributed ML techniques in enabling real-time predictive maintenance, particularly in remote or resource-constrained environments. We also discuss the importance of regulatory compliance, privacy protection, and ethical considerations in the deployment of ML-based PdM solutions.

Non-invasive glucose extraction by a single polarization rotator system in patients with diabetes.

This study utilizes a Mueller matrix-based system to extract accurate glucose levels from human fingertips, addressing challenges in skin complexity. Integration of domain knowledge and data science aims to enhance prediction accuracy using a Random Forest model. The primary goal is to improve glucose level predictions by selecting effective features based on the Pearson product-moment correlation coefficient (PPMCC). The interpolation compensates for delayed glucose concentration. This study integrates domain knowledge and data science, combining a Mueller matrix-based system and a random forest model. It is noted that 16 effective features were identified from 27 test points collected from a healthy volunteer in the laboratory. These features were divided into training and prediction sets in a ratio of 8:2. As a result, the regression coefficient, R2, was 0.8907 and the mean absolute relative difference (MARD) was 6.8%, respectively. This significantly improves prediction accuracy, demonstrating the model's robustness and reliability in accurately forecasting outcomes based on the identified features. In addition, in the Institutional Review Board (IRB) tests at NCKU's hospital, all data passed the same preprocessing and model. The measurement results from an individual diabetic patient demonstrate high accuracy for blood glucose concentrations below 150 mg/dL, with acceptable deviation at higher levels and no severe error zones. Over a three-month period, data from the participating diabetic patient showed a MARD of 4.44% with the R2 of 0.836, and the other patient recorded a MARD of 7.79% with the R2 of 0.855. The study shows the proposed approach accurately extracts glucose levels. Integrating domain knowledge, data science, and effective strategies significantly improves prediction accuracy.

Advancing multimodal diagnostics: Integrating industrial textual data and domain knowledge with large language models

The rapid advancement and application of large language models (LLMs) in various domains prompt an investigation into their potential in the field of prognostics and health management (PHM), particularly for enhancing data-driven model capabilities. This study explores the integration of domain knowledge accumulated in unstructured text data such as technical documents and maintenance logs into diagnostics models using LLMs. The study demonstrates the new possibilities to exploit data that are traditionally underutilized due to their complexity and the presence of domain-specific jargon. By leveraging LLMs for contextual understanding and information extraction from such texts, this study proposes a novel approach that combines textual data with existing condition monitoring systems to improve the accuracy of diagnostics models. A case study on hydrogenerators illustrates the feasibility and value of integrating LLMs into PHM systems. The findings suggest that the incorporation of LLMs can lead to more informed, accurate diagnostics, ultimately enhancing operational efficiency and safety within industrial environments.