Alfredo Núñez

• PhD Electrical Engineering
• Full-time Associate Professor (UHD) at Delft University of Technology

Computer-aided simulations are routinely used to predict a prototype's performance. High-fidelity physics-based simulators might be computationally expensive for design and optimization, spurring the development of cheap deep-learning surrogates. The resulting surrogates often struggle to generalize and predict novel scenarios beyond their training...

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research sho...

This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to predict the deflection of beams and the magnitude of the loads. The mathematical representation of the...

A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs). This paper aims to enhance the generalization capabilities of PIML, facilitating practical, real-world applications where accur...

This study presents a measuring framework for railway transition zones using a case study on the Swedish line between Boden and Murjek. The final goal is to better understand the vertical dynamics of transition zones using hammer tests, falling weight measurements, and axle box acceleration (ABA) measurements. Frequency response functions (FRFs) fr...

This article develops and tests a self-contained railway track monitoring system that fits in existing vehicles without the need for speed and load control. Combining a train-borne laser Doppler vibrometer and axle box accelerometers enables synchronized measurements of train-track response under operational conditions. Utilizing a GPS antenna and...

Parametric partial differential equations (PDEs) are ubiquitous in various scientific and engineering fields, manifesting the behavior of systems under varying parameters. Predicting solutions over a parametric space is desirable but prohibitively costly and challenging. In addition, recent neural PDE solvers are usually limited to interpolation sc...

This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations. Specifically, Euler-Bernoulli and Timoshenko beam models on the Winkler foundation are simulated using a transfer learning approach within a causality-respecting physics-informed neural network (PINN) framework. Conventional PINNs encounter challe...

This article proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler–Bernoulli and Timoshenko theories, where the double beams are connected with a Winkler foundation. In particular, forward and inverse problems for the Euler–Bernoulli and...

Load identification from vibration measurements is usually cost-effective for obtaining dynamic loads on structures. Modal testing enables structural models to be validated or updated prior to being used for load identification. This paper proposes a methodology to make better use of modal test data for developing load identification methods. Modal...

The railway industry has the potential to strongly contribute to achieving various sustainable development goals by expanding its role in the transportation system of different countries. To realize that, complex technological and societal challenges are to be addressed, along with the development of suitable state-of-the-art methodologies fully ta...

A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs). This paper aims to enhance the generalization capabilities of PIML, facilitating practical, real-world applications where accur...

A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs). This paper aims to enhance the generalization capabilities of PIML, facilitating practical, real-world applications where accur...

Due to train load and aging, the dynamic properties of railway tracks degrade over time and deviate over space, which should be monitored to facilitate track maintenance decisions. A train-borne laser Doppler vibrometer (LDV) can directly measure track vibrations in response to the moving train load, which can be potentially applied to large-scale...

Microgrids (MGs) are sustainable solutions for rural zone electrification that use local renewable resources. However, only careful planning at the start of an MG project can ensure its future optimal operation. In this paper, a novel methodology for MG planning by using the uncertainty characterization of renewable resources and demand is presente...

Operational modal analysis (OMA) enables the identification of modal characteristics under operational loads and conditions. Traditional frequency-domain methods cannot directly capture modal changes over time, while existing time-frequency representations are not sufficiently interpretable due to spurious modes and implicit parameter design. This...

This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to predict the deflection of beams and the magnitude of the loads. The mathematical representation of the...

This paper proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Bernoulli and Timoshenko theory, where the double beams are connected with a Winkler foundation. In particular, forward and inverse problems for the Euler-Bernoulli and Timo...

Vision-Based Measurement (VBM) is a promising technology for monotonous and repetitive vision activities, due to its ability to process, analyze and understand visual content, including 2D images and videos, 3D point cloud images, etc. In the era of Industry 4.0, incorporating Artificial Intelligence, especially Deep Learning (DL), into VBM has ach...

Modular-multilevel-converters (MMCs) are vital components in direct current transmission networks. Predictive maintenance scheduling of MMCs requires estimations of the failure probabilities of MMCs during a period of time in the future. Particularly, the predicted future failure probabilities are influenced by two main factors, the mission profile...

To ensure the reliability of power systems, the independent system operator (ISO) manages the planning process of the maintenance of generation units for generation companies (GENCOs). This paper focuses on a widely studied two-layer long-term predictive maintenance decision making framework in a deregulated environment. In the first layer the ISO-...

This paper proposes a new hypothesis for the formation process of short pitch rail corrugation. An FE wheel-track dynamic model is utilized to verify the hypothesis by reproducing corrugation initiation and consistent growth. It is found longitudinal compression modes are responsible for corrugation initiation with necessary initial excitation that...

Speckle noise is a major problem for structural vibration measurements with Laser Doppler vibrometer on moving platform (LDVom) due to its highly random, frequent, and broadband nature, especially at high speeds. This paper develops a new post-processing framework to reduce speckle noise based on a case study of LDVom measurements on railway tracks...

Non-electrified regional railway lines with typically employed diesel-electric multiple units require alternative propulsion systems to meet increasingly strict emissions regulations. With the aim to identify an optimal alternative to conventional diesel traction, this paper presents a model-based assessment of hydrogen-powered propulsion systems w...

Maintenance of generation units is a measure to ensure the reliability of power systems. In this paper, a novel blockchain-based truthful condition-based maintenance of generation units (T-CBMGU) platform is proposed to innovate and upgrade state-of-the-art CBMGU. In addition, two valid inequalities are proposed to accelerate the convergence speed...

Control Engineering Practice - Special Issue on Advanced Supervision, Maintenance, and Optimization for Intelligent Transportation Systems

The railway sector is facing significant challenges in addressing the increasing concerns related to climate change, environmental pollution and scarcity of resources. This especially applies to often non-electrified regional railway networks, with passenger services provided by diesel-driven vehicles. Innovative propulsion system concepts offer si...

In this study, a wheel-rail transient rolling contact model capable of accounting for the nonlinear displacement-force properties of hanging sleepers is proposed. The sleeper hanging status affected by rail irregularities is an input for an analysis of the wheel-rail contact behavior and related rail degradation in terms of plastic deformation and...

Natural disasters pose a tremendous risk to the reliability of distribution networks. In this paper, a novel real-time UAV routing strategy for coordination between monitoring and inspection for post-disaster restoration in distribution networks is proposed. With our proposed real-time UAV routing strategy, damages can be inspected by UAVs for post...

Brace Sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in t...

Hybridization of diesel multiple unit railway vehicles is an effective approach to reduce fuel consumption and related emissions in regional non-electrified networks. This paper is part of a bigger project realized in collaboration with Arriva, the largest regional railway undertaking in the Netherlands, to identify optimal solutions in improving t...

This paper investigates three-dimensional (3D) rail vibrations under fastening constraint up to 5000 Hz and provides insights into rail vibration control by fastening parameters. A methodology is proposed, including experimental investigation and numerical simulations of rail vibrations. Three steps are considered: 1) experimental investigation of...

This paper proposes an automatic high-precision detection method for structure parameters of catenary cantilever devices (SPCCD) using 3D point cloud data. The steps of the proposed detection method are: 1) segmenting and recognizing the components of the catenary cantilever devices, 2) extracting the detection plane and backbone component axis of...

Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. In this paper, a novel short-term preventive maintenance method is propose...

In this paper, we present a solution method based on finite element (FE) modeling to predict multimodal dispersive waves in a free rail. As well as the modal behaviors and wavenumber-frequency dispersion relations, the phase and group velocities of six types of propagative waves are also derived and discussed in detail in the frequency range of 0–5...

With the rapid development of deep learning technologies, researchers have begun to utilize convolutional neural network (CNN)-based object detection methods to detect multiple catenary support components (CSCs). The literature has focused on the detection of specified large-scale CSCs. Additionally, CNN architectures have faced difficulties in ide...

The catenary insulator maintains electrical insulation between catenary and ground. Its defects may happen due to the long-term impact from vehicle and environment. At present, the research of defect detection for catenary insulator faces several challenges. 1) Localization accuracy is low, which causes the localized object to be incomplete or/and...

This study evaluates the degradation of wheels and rails at railway crossings. The evaluation method is composed of 1) finite element simulation of dynamic wheel/crossing interaction and 2) multi-criteria analysis of wheel/rail degradation in terms of yield behavior, rolling contact fatigue (RCF) and wear. With the aid of this method, we conducted...

This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of...

The growing variety of data from condition monitoring of high-speed railways offer unprecedented opportunities to improve railway infrastructure maintenance. For condition monitoring of railway catenaries, this paper proposes a data-driven approach that uses a Bayesian network (BN) to integrate the inspection data from catenaries into a key perform...

This paper presents a review of research and models regarding sustainability of railway passenger services. In order to take into account all relevant aspects in terms of environmental impacts of a railway passenger service, a holistic system perspective is required, that includes a whole life cycle assessment. A life cycle approach is important si...

We develop a Model Predictive Control (MPC) approach for condition-based maintenance planning under uncertainty for railway infrastructure systems composed of multiple components. Piecewise-affine models with uncertain parameters are used to capture both the nonlinearity and uncertainties in the deterioration process. To keep a balance between robu...

In this paper, distributed optimization approaches are developed for the planning of maintenance operations of large-scale railway infrastructure formulated as a Mixed-Integer Linear Programming (MILP) problem. The proposed planning problem is solved using three different distributed optimization schemes: Parallel Augmented Lagrangian Relaxation (P...

The condition-based maintenance of high-speed railway catenary is an important task to ensure the continuous availability of train power supply. To improve the condition monitoring of catenary, this paper presents a novel scheme to detect catenary local irregularities using pantograph head acceleration measurements. First, a series of experimental...

In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated...

Prediction interval modelling has been proposed in the literature to characterize uncertain phenomena and provide useful information from a decision-making point of view. In most of the reported studies, assumptions about the data distribution are made and/or the models are trained at one step ahead, which can decrease the quality of the interval i...

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using Hilber...

This paper presents a Pareto-based maintenance decision system for rail welds in a regional railway network. Weld health condition data are collected using a train in operation. A Hilbert spectrum-based approach is used for data processing to detect and assess the weld quality based on multiple registered dynamic responses in the axle box accelerat...

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about def...

This paper describes an approach for characterizing the dynamic behavior of the vehicle/track interaction at railway crossings. In the approach, we integrate in situ axle box acceleration (ABA) measurements with roving-accelerometer hammer tests to evaluate the influence of train speed, train moving direction (facing and trailing directions), senso...

In this paper, we present a method for evaluating the performance of railway crossing rails after long-term service. The method includes 1) 3D profile and hardness measurements; 2) finite element simulation of wheel/rail interaction; and 3) numerical prediction of rail degradation. We conducted a case study on a crossing that had been in service fo...

This paper presents a methodology to support decision making based on the tram wheel-rail interface condition. The methodology relies on the following measurements: tram failure log-files regarding wheel-sliding events, monitored acoustics data and open source weather information. The proposed methodology consists of three stages: 1) data collectio...

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about def...

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation...

This paper studies the evolvement of the wear irregularity of contact wire using wire thickness data measured yearly from a section of railway catenary. The power spectral density and time-frequency representation based on the wavelet transform are employed for data analysis, with an emphasis on local wear irregularities that are crucial for contac...

This paper develops a multi-level decision making approach for the optimal planning of maintenance operations of railway infrastructures, which are composed of multiple components divided into basic units for maintenance. Scenario-based chance-constrained Model Predictive Control (MPC) is used at the high level to determine an optimal long-term com...

In this paper, we investigate the capability of an axle box acceleration (ABA) system to evaluate the degradation at railway crossings. For this purpose, information from multiple sensors, namely, ABA signals, 3D rail profiles, Global Positioning System (GPS) and tachometer recordings, was collected from both nominal and degraded crossings. By prop...

—Microgrids are suitable electrical solutions for providing energy in rural zones. However, it is challenging to propose in advance a good design of the microgrid because the electrical load is difficult to estimate due to its highly dependence of the residential consumption. In this paper, a novel estimation methodology for the residential load pr...

This study proposes a Bayesian network (BN) dedicated for the intelligent condition monitoring of railway catenary systems. It combines five types of measurements related to catenary condition, namely the contact wire stagger, contact wire height, pantograph head displacement, pantograph head vertical acceleration and pantograph-catenary contact fo...

A three-dimensional (3D) finite element (FE) dynamic frictional rolling contact model is presented for the study of short pitch corrugation that considers direct and instantaneous coupling between the contact mechanics and the structural dynamics in a vehicle-track system. In this study, we examine the system responses in terms of vibration modes,...

In this paper, a method for the identification of distributed-parameter systems is proposed, based on finite-difference discretization on a grid in space and time. The method is suitable for the case when the partial differential equation describing the system is not known. The sensor locations are given and fixed, but not all grid points contain s...

Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that...

For the condition monitoring of railway catenaries, the potential utilization of pantograph head (pan-head) vertical acceleration instead of pantograph-catenary contact force is discussed in this paper. In order to establish a baseline of the pan-head acceleration before it can be used for health condition monitoring, one of the essential frequency...

This paper presents a condition-based treatment methodology for a type of rail surface defect called squat. The proposed methodology is based on a set of robust and predictive fuzzy key performance indicators. A fuzzy Takagi-Sugeno interval model is used to predict squat evolution for different scenarios over a time horizon. Models including the ef...

This paper presents a sensitivity analysis of railpad parameters on vertical railway track dynamics, incorporating the nonlinear behavior of the fastening (i.e., downward forces compress the railpad whereas upward forces are resisted by the clamps). For this purpose, solid railpads, rail-railpad contact and preloaded springs are defined in a three-...

Rail defect detection by video cameras has recently gained much attention in both academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semi-supervised learning techniques. In this paper we investigate if positive defective...

This paper develops a multilevel decision making approach based on model predictive control (MPC) for condition-based maintenance of rail. We address a typical railway surface defect called “squat”, in which three maintenance actions can be considered: no maintenance, grinding, and replacement. A scenario-based scheme is applied to address the unce...

This paper explores the use of pantograph–catenary contact force (PCCF) for monitoring of the current collection quality and detection of anomalies in the interaction between pantograph and catenary. The concept of catenary structure wavelength (CSW) is proposed as the dominant component of PCCF. It describes the signal components caused by the cyc...

This paper presents a health condition monitoring system for insulated rail joints (IRJs) based on axle box acceleration (ABA) measurements. The ABA signals from all the wheels of the measuring train are processed to extract those characteristics that better represent the quality of the IRJ. Then, different indicators are used for damage assessment...

This paper presents the design and evaluation of a dynamic simulator for an ISCC (integrated solar combined cycle) plant. The design of the simulator is based on the phenomenological equations for both a combined cycle plant and a solar plant. The simulator incorporates a regulatory control strategy based on PI (proportional-integral) controllers a...

In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated detec...

Microgrids are sustainable solutions for electrification of rural zones that can make use of their local renewable resources. In this paper, we propose a new method for microgrid planning which includes the effect of the uncertainties of the renewable resources explicitly. Fuzzy interval models are used because they can capture nonlinearities and s...

This paper develops a defect-based risk analysis methodology for estimating rail failure risk. The methodology relies on an evolution model addressing the severity level of rail surface defect, called squat. The risk of rail failure is assessed by analyzing squat failure probability using a probabilistic analysis of the squat cracks. For this purpo...

In this paper, we propose a tractable scenario-based receding horizon parameterized control (RHPC) approach for freeway networks. In this approach, a scenario-based min–max scheme is used to handle uncertainties. This scheme optimizes the worst case among a limited number of scenarios that are considered. The use of parameterized control laws allow...

Rail joints are a weak component in railway tracks because of the large impact and wheel-rail contact forces. Every train passage contributes to the deterioration of rail joints, causing visible (e.g., battered rails) and invisible (e.g., loose bolts) damages. The invisible damage cannot be detected by the commonly performed visual inspection, whic...

Road traffic networks are large-scale systems that demand distributed control strategies. Distributed model predictive control (DMPC) arises as a feasible alternative for traffic control. Distributed strategies decompose the whole traffic network into different subnetworks with local optimal controllers that make decisions on actions to be taken by...

In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In th...

The noncentralized model predictive control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we prop...

This paper develops a new decision making method for optimal planning of railway maintenance operations using hybrid Model Predictive Control (MPC). A linear dynamic model is used to describe the evolution of the health condition of a segment of the railway track. The hybrid characteristics arise from the three possible control actions: performing...