Febrile diseases such as malaria, typhoid fever, tuberculosis, and HIV/AIDS pose significant diagnostic challenges in Low- and Middle-Income Countries (LMICs). Misdiagnosis leads to delayed treatment, increased healthcare costs, and higher mortality...
The maintenance of gas turbines relies a lot on gas path diagnostics (GPD), which includes two approaches. The first approach employs a physics-based model (thermodynamic model) to convert measurement shifts (deviations) induced by deterioration into...
Monitoring gearbox health is essential in industrial systems, where undetected faults can result in costly downtime and severe equipment damage. While vibration-based diagnostics are widely utilized for fault detection, analyzing large-scale, non-sta...
In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data...
This paper examines the transition of lithium-ion batteries from electric vehicles (EVs) to energy storage systems (ESSs), with a focus on diagnosing their state of health (SOH) to ensure efficient and safe repurposing. It compares direct methods, mo...
Artificial intelligence (AI) is rapidly transforming medical diagnostics by allowing for early, accurate, and data-driven clinical decision-making. This review provides an overview of how machine learning (ML), deep learning, and emerging multimodal...
This article presents a system for the detection and prediction of faults in refrigeration equipment developed using rough set theory, a method from artificial intelligence and leveraging Internet of Things (IoT) technology for continuous data collec...
The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, ind...
This paper indicates the importance and advantages of the application of hybrid models in the control of water supply systems. A range of possibilities provided by this scientific approach is presented in the practical examples related to the fault d...
Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundament...
This study presents a data-driven analysis of electric powertrain energy flow and traction battery behavior in a modern battery electric vehicle based on real-world on-board diagnostic (OBD) measurements. Time-resolved signals acquired during an urba...
The ALEX2-test (MacroArray Diagnostics, Vienna, Austria) is a diagnostic multiplex IgE-test for the simultaneous detection of IgE to 178 allergens and 117 extracts, in addition to total IgE. Test results from more than 90 countries are stored on a GD...
The concept of concurrent mental health and substance use (MHSU) status and its detection in patients has garnered growing interest among psychiatrists and healthcare policymakers over the past four decades. Researchers have proposed various diagnost...
The automated fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC) using data mining and machine learning models have recently received substantial attention from researchers and practitioners. Various models ha...
This study develops an interpretable, data-driven framework for forecasting daily MDA8 ozone levels in the Beijing–Tianjin–Hebei (BTH) region, integrating statistical diagnostics, XGBoost-based SHAP feature interpretation, and the Tempora...
Battery diagnostics and prognostics support safe electrified transport and grid storage, yet comparison across model families is hindered by inconsistent notation, validation, and uncertainty reporting. This review organizes state-of-health, state-of...
The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe’s renewable energy strategic planning, WT system...
Pediatric appendicitis remains a challenging condition to diagnose accurately due to its varied clinical presentations and the non-specific nature of symptoms, particularly in younger patients. Traditional diagnostic approaches often result in delaye...
This paper describes a methodology, developed by the authors, for condition monitoring and diagnostics of several critical components in the large-scale applications with machines. For industry, the main target of condition monitoring is to prevent t...
A novel anomaly detection algorithm is presented to analyze a group of signals that must be correlated under normal conditions. The method is called Clustered Correlation Health Scan (CCH-Scan). It detects abnormal signals, the durations correspondin...
Artificial intelligence and machine learning have increasingly transformed optometry, enabling automated classification and predictive modeling of eye conditions. In this study, we introduce Optometry Random Forest, an artificial intelligence-based s...
Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major...
This paper proposes a novel data-driven method for machine fault diagnosis, named multisensor-BPF-Signal2Image-CNN2D. This method uses multisensor data, bandpass filtering (BPF), and a 2D convolutional neural network (CNN2D) for signal-to-image recog...
Background/Objectives: Isocitrate dehydrogenase (IDH) status is important for the classification and prognosis of adult gliomas. Whole-tumor MRI summary measures may obscure intratumoral spatial heterogeneity, whereas data-driven habitat analysis ide...
The service life of aluminium electrolytic capacitors is becoming a critical design factor in power supplies. Despite rising power density demands, electrolytic capacitors and switching devices are the two most common parts of the power supply that a...
Wireless connectivity plays a pivotal role in enabling real-time telemetry, sensor feedback, and autonomous navigation within Industry 4.0 environments. This paper presents a ROS 2-based mobile robotic platform designed to perform real-time network d...
The rapid digital transformation of the retail industry has generated large-scale, high-frequency data; yet many retailers still rely on siloed systems that decouple demand forecasting from operational inventory management. This separation often lead...
Adhesively bonded composite reinforcements have been increasingly used in civil engineering since the 1980s. They depend on the effective transfer of forces throughout the adhesive joint that may be affected by defects or damages. It is therefore nec...
Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the...
Modern industries encourages the use of hydrogen as an energy carrier to decarbonize the electricity grid, Polymeric Electrolyte membrane fuel cell which uses hydrogen as a fuel to produce electricity, is an efficient and reliable ‘power to gas...
This paper presents a data-driven flexible hybrid architecture which explores the use of a Specific Energy Consumption (SEC) index for predicting the Remaining Useful Lifetime (RUL) of spare mechanical parts of an industrial electric machine. The arc...
This paper presents a data-driven probabilistic framework for analysing power system faults using Monte Carlo simulations. The study evaluates the operational reliability of multiple high-voltage switchgear topologies—including single-busbar sy...
Power conversion systems for wireless power transfer (WPT) applications have demanding requirements for continuity of service, besides being operated with stressing environmental conditions. Diagnostic and prognostic programs are thus quite useful an...
In-cylinder pressure provides critical insights for analyzing and optimizing combustion in internal combustion engines, yet its acquisition across the full operating space requires extensive testing, while physics-based models are computationally dem...
Background: Current low milk supply (LMS) definitions use subjective maternal perceptions or arbitrary thresholds for 24 h milk production (MP), potentially misclassifying cases. This study aimed to re-evaluate the definition of LMS using data-driven...
Safe drilling in hydrate-bearing sediments is essential for environmentally responsible natural gas hydrate development. Complex pressure variations, fluid migration, and mechanical disturbances during drilling may increase the risks of gas influx, l...
Genomics-based diagnostic data (GBDD) are becoming increasingly important for laboratory diagnostics. Due to the large quantity of data and their heterogeneity, GBDD poses a big data challenge. Current analysis tools for GBDD are primarily designed f...
The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts w...
The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance....
Owing to the rapid expansion of data science, data-driven methods have emerged as a dominant trend in chiller fault detection and diagnosis (FDD). Most of these methods prioritize feature selection to achieve optimal diagnostic performance. However,...
The lack of gas turbine field data, especially faulty engine data, and the complexity of fault embedding into gas turbines on test benches cause difficulties in representing healthy and faulty engines in diagnostic algorithms. Instead, different gas...
Fault diagnosis of diesel engines is a critical task in the operation and maintenance of complex equipment. Diesel engine fault diagnosis technology based on deep learning has seen widespread development due to its powerful feature learning and fault...
Personalized learning paths aim to save learning time and improve learning achievements by providing the most appropriate learning sequence for heterogeneous students. Most existing methods that construct personalized learning paths focus on students...
The increasing impact of online popularity on consumption calls for integrated sustainability diagnostic frameworks that combine both consumption and production data. This study aims to develop a macro sustainability diagnostic model integrating mult...
This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed feature sets. A dataset acquired under variable load and multiple operating condit...
Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making...
The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on th...
Modern distributed computing systems face increasingly complex architectural evolution and potentially costly failures, calling for efficient and robust automated diagnosis to ensure the stability of large-scale data processing. Existing data-driven...
Diagnostic analgesia and lunging are parts of the equine lameness examination, aiding veterinarians in localizing the anatomical region(s) causing pain-related movement deficits. Expectation bias of visual assessment and complex movement asymmetry ch...
The gearbox is a critical component in modern industrial systems, directly determining the operational reliability of machinery. Therefore, effective fault diagnosis is essential to ensure its proper functioning. Modern diagnostic approaches often em...
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