This paper develops a model-based data driven algorithm for fault classification in proton exchange membrane fuel cells (PEMFCs). The proposed approach overcomes the drawbacks of voltage and current density assumptions in conventional model-based fau...
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 review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehen...
In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the...
Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. Most research works are limited to sensor fault detec...
The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically–meaningful nonlinear continu...
Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make...
Model-based and data-driven methods are commonly used in leak location strategies in water distribution networks. This paper formulates a hybrid methodology in two stages that complements the advantages and disadvantages of data-driven and model-base...
The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation network...
Localising leaks in pressurised water distribution networks (WDNs) is crucial for reducing water loss but remains challenging because of model uncertainties and limited sensor data. Nevertheless, many state-of-the-art methods rely on idealised assump...
Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlin...
The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques repre...
In recent times, cities have increasingly promoted bicycling as a mode of transport as part of their strategy to develop a more sustainable transportation system. Australia is one of the countries that seeks to promote bicycling in a significant mann...
Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environme...
Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On th...
Fatigue has always been one of the major causes of structural failure, where repeated loading and unloading cycles reduce the fracture energy of the material, causing it to fail at stresses lower than its monotonic strength. However, predicting fatig...
This paper presents a pressure management technique for the reduction of leaks considering as a case study a branched water distribution system. The proposed technique is based on the detection and location of the leak using a genetic algorithm (GA)...
Factors such as changes in the external atmospheric environment, volatility in the external radiation, convective heat transfer, and radiation between the internal surfaces of the airship skin will cause a series of changes in the motion model of an...
Buildings’ long-term techno-economic performance monitoring is critical for benchmarking in order to reduce costs and environmental impact while providing adequate services. Reliable building stock performance data provide a fundamental knowled...
Cupriavidus necator DSM 545 can utilise glycerol to synthesise poly(3-hydroxybutyric acid) under unbalanced growth conditions, i.e., nitrogen limitation. To improve poly(3-hydroxybutyric acid) (PHB) batch production by C. necator through model-guided...
Geometrically parametrized partial differential equations are currently widely used in many different fields, such as shape optimization processes or patient-specific surgery studies. The focus of this work is some advances on this topic, capable of...
Given the challenges associated with accurately modeling complex nonlinear systems with time delays in industrial processes, this paper introduces an advanced model-free control algorithm that combines data-driven and model-driven approaches. Initial...
In the context of climate change, the correct management of groundwater, which is strategic for meeting water needs, becomes essential. Groundwater modeling is particularly crucial for the sustainable and efficient management of groundwater. This man...
This study aimed to develop a non-destructive measurement method utilizing acoustic sensors for the efficient determination of the internal temperature of shiitake mushroom sticks during the cultivation period. In this research, the sound speed, air...
With the rapid development of socioeconomics and the continuous advancement of urbanization, water environment issues in plain river networks have become increasingly prominent. Accurate and reliable water quality (WQ) predictions are a prerequisite...
The operation of control devices in a water distribution network establishes a transient flow that leads to pressure oscillation during a certain time. Depending on the pressure wave’s amplitude, infrastructure can be exposed to dangerous press...
Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the pro...
Flooding remains one of the most disruptive and costly natural hazards worldwide. Conventional approaches for estimating flood damage cost rely on empirical loss curves or historical insurance data, which often lack spatial resolution and predictive...
Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an al...
The long-term preservation of heritage structures relies on effective Structural Health Monitoring (SHM) systems, where sensor placement is key to ensuring early damage detection and guiding conservation efforts. Optimal Sensor Placement (OSP) method...
Exploring the idea of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phase-less measurements. In...
Rebar procurement inefficiencies, such as inaccurate quantity estimation and misaligned delivery schedules, often lead to excessive waste, supply shortages, and project delays. While existing optimization methods reduce cutting waste, their effective...
Compared to rigidly-backed absorbers, the selection of appropriate optimization techniques for the optimal design of broadband acoustic mufflers remains under-investigated. This study determines the most effective optimization strategy for maximizing...
Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (...
Recently, image haze removal has gained increasing attention in the field of image restoration. Data-driven and model-based methods are popular among researchers. The dark channel prior prevails in model-based methods, where the model parameters, atm...
Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and longevity of electric vehicles, battery energy storage systems, and other energy applications. This paper presents a comp...
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in main...
The increasing application of plain bearings in various industries, especially under challenging conditions like thin lubricating films and high temperatures, necessitates effective monitoring to prevent failures and ensure reliable performance. Whil...
A significant part of modern natural sciences aims to establish a model-based approach to describe the behavior of physical systems and forecast their dynamics in different scenarios. The successful application of model-based analysis of transport ph...
In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controlle...
This paper introduces an approach that leverages large language models (LLMs) to convert detailed descriptions of an Operational Design Domain (ODD) into realistic, executable simulation scenarios for testing autonomous vehicles. The method combines...
This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, p...
This study investigates model-based emergency strategies for damage prevention following cable breaks in six-degree-of-freedom cable-driven parallel robots. By relaxing real-time constraints, the empirical performance limit of existing and novel reco...
State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sens...
It is paramount to improve operational conversion efficiency in air-conditioning refrigeration. It is noticed that control efficiency for model-based methods highly relies on the accuracy of the mechanism model, and data-driven methods would face cha...
The solid oxide fuel cell (SOFC) system is complicated because the characteristics of gas, heat, and electricity are intricately coupled. During the operation of the system, problems such as frequent failures and a decrease in the stack’s perfo...
Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics systems because of its robustness against parametric uncertainties and matched disturbances. Although SMC design has traditionally addressed process model-based...
Data-driven formulations are currently developed to deal with the complexity of the multi-physics governing the response of microelectromechanical systems (MEMS) to external stimuli and can be extremely helpful. Such devices are in fact characterized...
Online temperature estimation of key components (windings and magnets) in permanent magnet synchronous motors (PMSMs) has emerged as a critical technology for ensuring the safe operation of PMSMs, preventing insulation degradation, and avoiding the d...
This study aims to enhance additive manufacturing (AM) quality control. AM builds 3D objects layer by layer, potentially causing defects. High-resolution micrograph data capture internal material defects, e.g., pores, which are vital for evaluating m...
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