Most existing parametric approaches for detecting linear or nonlinear Granger causality(GC)face challenges in estimating appropriate time delays,a critical factor for accurate GC detection.This issue becomes particularly pronounced in nonlinear complex systems,which are often opaque and consist of numerous components or variables.In this paper,we propose a novel temporal convolutional network(TCN)-based end-to-end GC detection approach called the interpretable temporal convo-lutional framework(ITCF).Unlike conventional deep learning models,which act like a"black box"and are difficult to analyse the interactions between variables,the proposed ITCF is able to detect both linear and nonlinear GC and automatically estimate time delay during the multivariant time series prediction.Specifi-cally,GC is obtained by employing the least absolute shrinkage and selection operator(Lasso)regression during the prediction of multivariate time series using TCN.Then,time delays can be esti-mated by interpreting the TCN kernels.We propose a convolu-tional hierarchical group Lasso(cHGL),a hierarchical regulari-sation approach to effectively utilise temporal information within each TCN channel for enhanced GC detection.Additionally,as far as we are concerned,this paper is the first to integrate the Iterative Soft-Thresholding Algorithm into the backpropagation of TCN to optimise the proposed cHGL,which enables causal channel selection and induces sparsity within each TCN channel to remove redundant temporal information,ultimately creating an end-to-end GC detection framework.The testing results of four experiments,involving two simulations and two real data,demonstrate that the proposed ITCF,in comparison with state-of-the-art,offers a more reliable estimation of GC relationships in complex systems featuring intricate dynamics,limited data lengths,or numerous variables.

This paper presents the design and implementation of a web tool offering an innovative method for detecting, diagnosing and classifying bearing faults in rotating machinery under limited data conditions, providing explainability and interpretability of the results obtained. The tool uses a machine learning model to detect and diagnose bearing faults. A monotonic smoothed stacked autoencoder builds a health indicator without requiring feature extraction, making the tool useful without the need for specialized staff. The tool generates explainability and interpretability reports with a correlation analysis between the health indicator and well-known engineering features and easily interpretable details on the diagnosed faults. The tool includes the option to use preloaded state-of-the-art datasets, while also allowing users to upload their own datasets to analyze vibration data from real industrial equipment.

Advancements in neuroimaging have facilitated unprecedented insights into brain connectivity, making the study of brain effective connectivity networks (ECNs) essential for understanding neurological functions and diseases. Recently, neural networks (NNs) have emerged as powerful tools for ECN estimation due to their prominent universal approximation ability and less reliance on prior knowledge. However, most NN-based approaches fail to eliminate redundant temporal information and lack rigorous causal validation mechanisms. This paper introduces a novel end-to-end framework for estimating ECNs utilising Least Absolute Shrinkage and Selection Operator (Lasso) regression of Temporal Convolutional Networks (TCNs), named the Causal Validation augmented Temporal Convolutional Framework (CVTCF). In the CVTCF, a convolutional Hierarchical Group Lasso (cHGL) is proposed to detect Granger Causality (GC) inputs and eliminate redundant temporal information during GC detection. Additionally, the framework incorporates permutation importance validation based on the Wilcoxon signed-rank test to enhance the reliability of GC detection. The proposed CVTCF generally outperformed state-of-the-art methods in a controlled simulation using the chaotic Lorenz-96 model and the publicly available blood-oxygen-level-dependent (BOLD) benchmark dataset. Furthermore, the proposed CVTCF has enabled a detailed analysis of the causal interactions within the cerebral cortex, bringing to light the intricate relationships that underlie neurological functioning and impairment of neurodegenerative conditions like Alzheimer's Disease (AD) and Parkinson's Disease (PD). This study demonstrates the potential of using ECN estimation based on the CVTCF as indicators for neurodegenerative diseases and paves the way for future diagnostic and therapeutic strategies.

Traditional parametric system identification methods usually rely on apriori knowledge of the targeted system, which may not always be available, especially for complex systems. Although neural networks (NNs) have been increasingly adopted in system identification, most studies have failed to derive interpretable parametric models for further analysis. In this paper, we propose a novel end-to-end autoregressive with exogenous input (ARX) model identification framework using NNs. An order-wise neural network structure is introduced and trained using a multitask learning approach to simultaneously identify both the model terms and coefficients of the ARX model. Through testing with various neural network backbones and training data sizes in different scenarios, we empirically demonstrate that the proposed framework can effectively identify an arbitrary stable ARX model with finite simulation training data. This study opens up a new research opportunity for parametric system identification by harnessing the power of deep learning.

In industrial settings, the use of frictional noise to improve wear monitoring is highly promising. It enables the identification of changes in friction and wear conditions, the assessment of different phases of wear, and the examination of the impact of wear on machine performance. By analysing acoustic signatures, it is conceivable to continuously monitor the wear characteristics and surface conditions. This helps in predicting wear and detecting aberrant wear regimes in real-time. The data demonstrate that in dry conditions, the aluminum disc has higher coefficients of friction relative to cast iron and mild steel, likely due to the absence of graphite flakes in aluminum. Under lubricated conditions, a layer of lube significantly decreases the coefficient of friction, with no apparent deviations across the materials, demonstrating that complete lubrication avoids direct metal contact. In lubrication-starved applications, oily depictions nevertheless help minimize friction, though less efficiently than complete lubrication. In dry conditions, frictional sound levels for mild steel are higher due to direct surface hits, while lubrication reduces noise by eliminating metal-on-metal contact. As a result, monitoring noise levels is a helpful indicator of lubrication difficulties, aiding in maintenance and repairs.

— Detecting wheel slip is important in railway operations, to prevent damage to wheels and tracks, reduce maintenance costs, improve safety and enhance passenger comfort. Slip activity is characterised by reduced adhesion between the wheel and the rail and limits effective braking or acceleration, causing also operational risks. It is influenced by environmental conditions, vehicle load, track and axle quality, contaminants, inclines, rail oxidation, and braking forces. This paper introduces an innovative method for wheel slip detection in operational trains, utilizing wavelet analysis combined with Long-Short Term Memory (LSTM) modelling. This method analyzes operational data to effectively identify wheel slip, showing promising results when compared to traditional classification-based machine learning methods such as decision trees, forests, logistic regression, naïve Bayes, and support vector machines. This novel approach addresses the complexities of wheel slip detection and is capable of identifying the conditions leading to slip several seconds prior to the commencement of the slip event, offering a practical solution for real-world railway systems.

Track quality geometry measurements are crucial for the railways’ timely maintenance. Regular measurements prevent train delays, passenger discomfort and incidents. However, current fault diagnosis or parameter deviation relies on simple threshold comparison of multiple laser scanners, linear variable differential transformer (LVDT) and camera measurements. Data threshold exceedances enact maintenance actions automatically. However, issues such as measurement error, and sensor failure can result in false positives. Broad localisation resolution prevents trending/ inferencing by comparison with healthy data baseline at the same position over periodic inspections. False alarms can result in costly ineffective interventions, are hazardous and impact the network availability. This paper proposes a novel methodology based on convolutional neural network (CNN) technique for detecting and classifying track geometry fault severity automatically. The proposed methodology comprises an automatic flow of data for quality assessment whereby outliers, missing values and misalignment are detected, restored and where appropriate curated. Improved, “clean” datasets were then analysed using a pretrained CNN model. The method was compared with a suite of machine learning algorithms for diagnosis including k-nearest neighbour, support vector machines (SVM), and random forest (RF). The analysis results of a real track geometry dataset showed that track quality parameters including twist, cant, gauge, and alignment could be effectively diagnosed with an accuracy rate of 97.80% (CNN model). This result represents a remarkable improvement of 38% in comparison with the traditional threshold-based diagnosis. The benefits of this research are not only associated with maintenance intervention cost savings. It also helps prevent unnecessary train speed restrictions arising from misdiagnosis.

The Railway Inspection and Maintenance Vehicle (RIMV) is a state-of-the-art autonomous ground robot for the railway environment. The robot has specialised hardware tailored for specific functionalities, ensuring optimal performance in its designated tasks. It incorporates a GPS, IMU, and encoder for robust localisation, and it integrates a LiDAR system and a depth camera for collision avoidance. Prioritising safety, the RIMV features an IP camera for real-time environmental monitoring and is equipped with emergency stop mechanisms. An Ubuntu Linux machine manages the robot's data processing, complemented by a 5G Wi-Fi router for remote monitoring and operation when necessary. ROS2 is the primary platform for robot operations, standing out for its open-source solution and enhanced cybersecurity features. We validated the RIMV in a heritage railway setting with an ultrasonic inspection kit to detect rail-track defects. Beyond this specific application, the RIMV is versatile and designed to serve as a platform for other autonomous inspection and maintenance tasks within the railway sector. Its adaptability highlights its potential to enhance efficiency and safety in railway maintenance, offering invaluable assistance to human operators across varied scenarios. (c) 2024 The Authors. Published by Elsevier B.V.

Aluminium alloys have been integral to numerous engineering applications due to their favourable strength, weight, and corrosion resistance combination. However, the performance of these alloys in coastal environments is a critical concern, as the interplay between fracture toughness and fatigue crack growth rate under such conditions remains relatively unexplored. This comprehensive review addresses this research gap by analysing the intricate relationship between fatigue crack propagation, fracture toughness, and challenging coastal environmental conditions. In view of the increasing utilisation of aluminium alloys in coastal infrastructure and maritime industries, understanding their behaviour under the joint influences of cyclic loading and corrosive coastal atmospheres is imperative. The primary objective of this review is to synthesise the existing knowledge on the subject, identify research gaps, and propose directions for future investigations. The methodology involves an in-depth examination of peer-reviewed literature and experimental studies. The mechanisms driving fatigue crack initiation and propagation in aluminium alloys exposed to saltwater, humidity, and temperature variations are elucidated. Additionally, this review critically evaluates the impact of coastal conditions on fracture toughness, shedding light on the vulnerability of aluminium alloys to sudden fractures in such environments. The variability of fatigue crack growth rates and fracture toughness values across different aluminium alloy compositions and environmental exposures was discussed. Corrosion–fatigue interactions emerge as a key contributor to accelerated crack propagation, underscoring the need for comprehensive mitigation strategies. This review paper highlights the pressing need to understand the behaviour of aluminium alloys under coastal conditions comprehensively. By revealing the existing research gaps and presenting an integrated overview of the intricate mechanisms at play, this study aims to guide further research and engineering efforts towards enhancing the durability and safety of aluminium alloy components in coastal environments.

The crack initiation and propagation in an aluminium alloy in a corrosive environment are complex because of the loading parameters and material properties, which may result in a sudden failure in real-time applications. This paper investigates the fracture toughness of aluminium alloy under varying environmental and corrosion conditions. The main objective of the work is to link the interdependencies of humidity and temperature for an AL6082-T651 alloy in a corrosive environment. This study investigates AL6082-T651alloy's fracture behaviour and mechanism through microstructure and fractographic studies. The results show that a non-corroded sample, at room conditions, provided more load-carrying capacity than a corroded sample. Additionally, an increase in temperature improves fracture toughness, while an increase in humidity results in a decrease in fracture toughness.

Laser thermography manifests superior sensitivity and compatibility to detect cracks and small subsurface defects. However, the existing related systems have limitations on either inspection efficiency or unknown directional cracks due to the utilization of stationary heat sources. This article reports a fiber-guided motorized rotation laser-line scanning thermography (FMRLST) system aiming to rapidly inspect cracks of impact damage with unknown direction in composite laminates. An optical head with fiber delivery integrated with a rotation motor is designed and developed to generate novel scanning heating in a circumferential rotation manner. An FEM model is first proposed to simulate the principle of FMRLST testing and produce thermograms for the development of postprocessing methods. A damage enhancement method based on curvelet transform is developed to enhance the visualization of thermal features of cracks, and purify the resulting image by suppressing the laser-line heating pattern and canceling noise. The validation on three composite specimens with different levels of impact damage suggests that the developed FMRLST system can extract unknown impact surface cracks efficiently. The remarkable sensitivity and flexibility of FMRLST to arbitrary cracks, along with the miniaturized probe-like inspection unit, present its potential in onsite thermographic inspection, and its design is promising to push the laser spot thermography toward.

Concrete with fiber reinforcement is stronger and more ductile than concrete without reinforcement. Significant efforts have been made to demonstrate the properties and enhancements of concrete after reinforcement with various types and shapes of fibers. However, the issue of optimization in the reinforcement process is still unanswered. There is no academic study in the literature now available that can pinpoint the ideal fiber type, quantity, and shape and, more crucially, the overall technical viability of the reinforcement. The parametric analysis in this study determines the ideal shape, size, and proportion of fibers. The input and output parameters were separated from the optimization design variables. Input parameters included assessment of samples of fresh and mechanical concrete properties and the influence of type, length, and percentage of fiber on concrete performance. The aim was to establish the most efficient relationship between fiber dose and dimension to optimize the combined responses of workability and splitting tensile, flexural, and compressive strength. The mechanical and fresh properties of concrete reinforced with four different fibers, PFRC-1, PFRC-2, SFRC-1, and SFRC-2, were tested. The analysis showed that SFRC-2-20 mm-1%, with compressive, split tensile, flexural, and workability values of 44.7 MPa, 3.64 MPa, 5.3 MPa, and 6.5 cm respectively, was the most effective combination among the materials investigated. The optimization technique employed in this study offers new, important insights into how input and output parameters relate to one another.

Cast iron pipes are widely used in various industries due to their high strength and durability. However, over time, these pipes can develop cracks and experience a decrease in fracture toughness, leading to potential leaks. Understanding the interdependency between leak rate, degradation of fracture toughness, and crack growth is crucial for assessing the integrity of cast iron pipes and implementing effective maintenance strategies. This paper investigates the relationship between these factors through a comprehensive analysis of existing literature and experimental data. The findings provide valuable insights into the mechanisms behind crack propagation and offer guidance for mitigating the risks associated with cast iron pipe failures.

An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after significant reliability issues arise (such as flight delays and high component removals). Several publications and research studies have been conducted related to this issue, one of them involves performing simulations and providing aircraft operation datasets. The recently published NASA CMAPPS datasets have been utilised in this paper since they simulate flight data recording from various measurements. A prognostics model can be developed by analysing these datasets and predicting the engine’s reliability before failure. However, the state-of-the-art prognostics techniques published in the literature using these NASA CMAPPS datasets are mainly purely data-driven. These techniques mainly deal with a “black box” process which does not include uncertainty quantification (UQ). These two factors are barriers to prognostics applications, particularly in the aviation industry. To tackle these issues, this paper aims at developing explainable and transparent algorithms and a software tool to compute the engine health, estimate engine end of life (EoL), and eventually predict its remaining useful life (RUL). The proposed algorithms use hybrid metrics for feature selection, employ logistic regression for health index estimation, and unscented Kalman filter (UKF) to update the prognostics model for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison and dataset 03 is employed as a new state-of-the-art. As a result, the proposed algorithms yield 34.5–55.6% better performance in terms of the root mean squared error (RMSE) compared with the previous work. More importantly, the proposed method is transparent and it quantifies the uncertainty during the prediction process.

This study investigates the parametric influence of physical dimensions and material properties of a composite cantilever beam on its modal response under fixed-free boundary conditions. The considered beam was made of PZT and aluminium materials. Euler-Bernoulli beam theory was used to investigate the modal response of a simple beam first and later expanded to the composite beam. Different combinations of physical dimensions and material properties, including the moment of inertia, length, area, young modulus and density, were used to analyse their influence on the obtained modal response. The obtained results showed that the density, the area of the beam cross-section, and the length of the beam have an inverse effect on the natural frequency. While the moment of inertia and modulus of elasticity has a positive influence on the values of the natural frequency. The analysis provides valuable insights into the dynamic behaviour of the beam and the effects of different parameters on its resonance characteristics, paving the way for optimising energy harvesting systems and enhancing their performance.

The combined effect of temperature and humidity on the fracture toughness of aluminium alloys has not been extensively studied, and little attention has been paid due to its complexity, understanding of its behaviour, and difficulty in predicting the effect of the combined factors. Therefore, the present study aims to address this knowledge gap and improve the understanding of the interdependencies between the coupled effects of temperature and humidity on the fracture toughness of Al–Mg–Si–Mn alloy, which can have practical implications for the selection and design of materials in coastal environments. Fracture toughness experiments were carried out by simulating the coastal environments, such as localised corrosion, temperature, and humidity, using compact tension specimens. The fracture toughness increased with varying temperatures from 20 to 80 °C and decreased with variable humidity levels between 40% and 90%, revealing Al–Mg–Si–Mn alloy is susceptible to corrosive environments. Using a curve-fitting approach that mapped the micrographs to temperature and humidity conditions, an empirical model was developed, which revealed that the interaction between temperature and humidity was complex and followed a nonlinear interaction supported by microstructure images of SEM and collected empirical data.

Autonomous robot integration in railways infrastructure maintenance accelerates the digitization and intelligence of infrastructure survey & maintenance, providing high-efficiency and low-cost execution. This paper proposes a health assessment based on 3D reconstruction technology for railway track maintenance using a mobile robotic sensing platform. By combining multiple sensing and taking advantage of a robotic manipulator, a digital model of the target track components is built by a robot-actuated vision system which provides better 3D structural and surface condition reconstruction. Global geo-location and surrounding laser scanning are integrated to reinforce the digital completeness of the model for intelligent management. The new method consists of the following steps: First, according to scheduled maintenance tasks, a Robotics Inspection and Repair System (RIRS) navigates to the task location and uses the onboard depth camera for positioning. Then robot-mounted vision system is guided with an automated trajectory to build the 3D reconstruction of the track or repair object using the vision modeling technique. Finally, the 3D reconstructed model is fused with surrounding mapping of depth vision and Lidar scanning. Both laboratory tests and a realistic track test validated the feasibility of the proposed method by creating an accurate 3D reconstructed model. The modeled rail steel section size is quantitively compared with the ground truth in dimension, demonstrating good accuracy with a size error of less than 0.3 cm. The main contribution includes: (1) unmanned automatic 3D reconstruction by a robotic mobile manipulator, (2) the technique trims the reconstruction details & data to the specific maintenance goal or components, which supports the infrastructure maintenance towards the high-detailed & target-oriented digital management. This combination strategy of robotic automation and sensor fusion lies down a promising foundation for automated digital twin establishment for railway maintenance with autonomous RIRS, and upgrades technology readiness and digital intelligence for maintenance management

Crack propagation is a critical phenomenon in materials science and engineering, significantly impacting structural integrity, reliability, and safety across various applications. The accurate prediction of crack propagation behavior is paramount for ensuring the performance and durability of engineering components, as extensively explored in prior research. Nevertheless, there is a pressing demand for automated models capable of efficiently and precisely forecasting crack propagation. In this study, we address this need by developing a machine learning-based automated model using the powerful H2O library. This model aims to accurately predict crack propagation behavior in various materials by analyzing intricate crack patterns and delivering reliable predictions. To achieve this, we employed a comprehensive dataset derived from measured instances of crack propagation in Acrylonitrile Butadiene Styrene (ABS) specimens. Rigorous evaluation metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, were applied to assess the model’s predictive accuracy. Cross-validation techniques were utilized to ensure its robustness and generalizability across diverse datasets. Our results underscore the automated model’s remarkable accuracy and reliability in predicting crack propagation. This study not only highlights the immense potential of the H2O library as a valuable tool for structural health monitoring but also advocates for the broader adoption of Automated Machine Learning (AutoML) solutions in engineering applications. In addition to presenting these findings, we define H2O as a powerful machine learning library and AutoML as Automated Machine Learning to ensure clarity and understanding for readers unfamiliar with these terms. This research not only demonstrates the significance of AutoML in future-proofing our approach to structural integrity and safety but also emphasizes the need for comprehensive reporting and understanding in scientific discourse.

The effective operation of water management systems is contingent upon leak localization and detection – a common problem that is more acute in large networks. This paper reviews the salient literature in this context and demonstrates the effectiveness of leakage location methods (LLMs) and leakage detection methods (LDMs). Although there is a significant amount of literature that discusses leakage localization and detection technologies, an academic lacuna still exists concerning the linkage between degradation mechanisms and LDMs and do not cover or connect past efforts from the start of a degradation mechanism that leads to changes in the mechanical strength (such as a reduction in fracture toughness) of pipes and results in crack propagation and leakage. This review focuses on these issues in the context of degradation mechanisms and common detection methods.

Inspection and repair interventions play vital roles in the asset management of railways. Autonomous mobile manipulators possess considerable potential to replace humans in many hazardous railway track maintenance tasks with high efficiency. This paper investigates the prospects of the use of mobile manipulators in track maintenance tasks. The current state of railway track inspection and repair technologies is initially reviewed, revealing that very few mobile manipulators are in the railways. Of note, the technologies are analytically scrutinized to ascertain advantages, unique capabilities, and potential use in the deployment of mobile manipulators for inspection and repair tasks across various industries. Most mobile manipulators in maintenance use ground robots, while other applications use aerial, underwater, or space robots. Power transmission lines, the nuclear industry, and space are the most extensive application areas. Clearly, the railways infrastructure managers can benefit from the adaptation of best practices from these diversified designs and their broad deployment, leading to enhanced human safety and optimized asset digitalization. A case study is presented to show the potential use of mobile manipulators in railway track maintenance tasks. Moreover, the benefits of the mobile manipulator are discussed based on previous research. Finally, challenges and requirements are reviewed to provide insights into future research.