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Automatic inspections of overhead contact lines (OCLs) are developed to implement anomaly detection during normal operation. It is an essential prerequisite for efficient maintenance of railway electrification system. This paper presents a comprehensive survey on the inspections of OCLs with an emphasis on computer vision technology, which has developed rapidly due to its ability to understand images. Our survey begins with a brief introduction on anomalies in OCL inspections and generic procedures of computer vision inspection for anomaly detection. Subsequently, for detecting deviations of parameters and defective components during OCL inspections, the existing techniques involving stereo vision and object vision, especially convolution neural networks are described in detail from two aspects: measurement of OCL parameters and identification of OCL conditions. Some interference factors in OCL inspection are analyzed. Actual cases of the inspections are also briefly shown. Challenges and suggestions for further research on OCL inspection are drawn toward the end of the paper.

The traction power supply system is one of the most important parts of a railway system, which is responsible for providing electricity to power the running trains and other operating equipment. The performance of the power supply has a profound impact on the railway system. Therefore, it is necessary to conduct research on the reliability of the power supply system considering traction load and ensure it is highly reliable. This article introduces some reliability-related indexes based on the fault tree analysis (FTA) method and proposes a method to evaluate the system reliability of a dc-electrified railway. The effect of traction load on power supply system reliability is studied based on Institute of Electronics Engineers (IEEE) standards. A dynamic reliability and life evaluation model is built up to evaluate the reliability of traction substation and to develop a plan to reduce the operational risk of the power system. Through analysis of a practical example of the Singapore East–West line, the system reliability and remaining useful life of each traction substation are obtained. The outcome from this study provides guidance for the maintenance and future operation plan of the power system.

In October 2018, Porterbrook and the University of Birmingham announced the HydroFLEX project, to demonstrate a hydrogen-hybrid modified train at Rail Live 2019. The concept of modifying a Class 319 Electric Multiple Unit was developed, with equipment including a fuel cell stack, traction battery, 24 V control system and hydrogen storage elements to be mounted inside one of the carriages. This was followed by procurement of a fuel cell stack, traction batteries, and control equipment, which was then installed inside the train, being fixed to the seat rails. One substantial change from the concept was the provision of considerably more hydrogen storage than the minimum necessary, providing the train with more potential to be further modified to allow for higher speed mainline testing. After the Rail Live exhibition where HydroFLEX was demonstrated, numerous modifications were performed to increase the reliability and power of the HydroFLEX train, primarily concerned with modifying the base train logic, with the aim of a successful mainline test. Supporting this effort was a multitude of documentation concerning safety, operations, and approvals to gain approvals from the relevant approvals bodies. The project demonstrated the feasibility of using hydrogen fuel cells as an autonomous fuel for railway propulsion systems, which has the potential for full decarbonisation.

In railways, using a track- and ride-quality monitoring system on in-service train has become desirable for coordination and security. Identification of the track- or train-related rough rides via ...

Urban mobility is increasingly becoming accepted as a basic human need, as socio-economic opportunities depend on the ability to reach places within an acceptable time. Conversely, the emergence of megalopoleis as dominant features of the global landscape has increased commuting effort to unprecedented levels, due to the ever expanding urban areas and the associated travel distances. This now poses a risk to the efficient accessibility of cities, but there is an assumption that the problem can be overcome by increasing the speed of transport systems. However, advocates of this approach overlook important utility trade-offs that arise from the conflict between greater vehicle speeds and the additional time required to access the services. In this paper, we investigate this approach and show that higher speeds in metro systems do not always result in faster travel in cities. We then propose a new approach to addressing the problem, which culminates in a solution that can overcome the current paradoxes and increase door-to-door speeds more effectively. The resulting operational concept optimizes speed and coverage in urban rail systems in megalopoleis, accommodating the longer trips within time budgets. We position this research as a starting point to a new perspective on developing complex urban systems in the future.

Drug release within the oral cavity can be of paramount importance for formulations that are designed for specific purposes such as taste-masking, faster onset of therapeutic action, localization of treatment or avoidance of first-pass metabolism. Preclinical methods for assessment of dissolution in the oral cavity are necessary for design and development of these formulation but currently there is no consensus on what variables should be defined to achieve biorelevance in these tests. In this study, biorelevant simulated salivary fluids (SSFs) that can be uniformly applied for oral cavity dissolution testing were developed. Unstimulated saliva (US) SSF and stimulated saliva (SS) SSF were separately developed since the two states significantly differ. Physicochemical properties including pH, buffer capacity, surface tension and viscosity were assessed during development and optimised to mimic human saliva (HS). In order to account for the salivary proteins in HS, use of bovine submaxillary mucin (BSM) and porcine gastric mucin (PGM) in SSFs was evaluated. Following optimisation of the SSFs, biorelevance of the developed SSFs to HS was assessed by their comparative physicochemical properties as well as dissolution profiles of three diverse model compounds (sildenafil citrate, efavirenz, and caffeine) which showed comparable profiles between the SSFs and HS. This work addresses the lack of uniformed biorelevant dissolution media for oral cavity dissolution studies and provides a basis for standardised dissolution tests that provide consistency and harmonisation in future oral cavity dissolution studies. We envisage that this will have a positive impact on the development of new medicines that require functionality in the oral cavity.

Now more than ever, monitoring railway track geometry from in-service vehicles is an attractive proposition that ensures improved infrastructure performance without interrupting railway operations. Communicating the collected sensors-data to a central server has always been an issue due to the current GSM-R and LTE data-rate and spectrum limitations. The prospect of opportunistic access to an inefficiently utilised frequency spectrum, known as TV White Spaces (TVWS), is proposed to solve the spectrum scarcity problem that exploits desirable railway propagation characteristics. In order to provide full protection for the spectrum primary users, IEEE 802.22 standard sets strict policies on the mobile platforms. This research proposes a novel handover scheme that utilises a greedy algorithm to select the operational frequency channels. The scheme takes into account; the train’s trajectory, including the possibility of train delays, and coexistence issues between the spectrum’s secondary users. A case study of two trains reporting their collected maintenance data to 3 Access Points (APs) while travelling between Selly Oak and New Street Station, Birmingham, UK is presented. For high channels availability (≥40%), an average of 30 megabytes of extra track data can be transmitted using the new approach for an 8 min journey. In addition, a single channel can be used in the new approach for an average consecutive distance of 1.05 km compared with an average of 0.58 km for IEEE 802.22 standard. Both systems provide identical interference performance with more transmission power that can reach up to 42.2 dBm allowed under the new scheme.

Hypertension is one of the most common chronic cardiovascular disorders. Sustained-release formulations are developed to maintain drug therapeutic levels throughout the treatment of hypertension, t...

Rail profile measurement is one of the most critical tasks for track quality inspection to ensure the safe operation of track systems. In modern railway systems, laser triangulation sensors have been widely adopted in onboard measuring units to carry out 2-D rail profile measurement due to their robustness and truly noncontact properties. However, existing solutions limit the degrees of freedom of the laser sensor and thus cannot provide full coverage of the rail profile due to the “shadowing effect” of triangulation sensors. Incomplete profiles limit the performance of wear assessment hence more detailed inspections still rely on contact-based tools operated by humans. These processes are time-consuming and incompatible with the ever-shortening maintenance windows available in modern railway systems. Benefiting from the miniaturization of sensing technology and improving processors, multisensing systems combine the strengths of different sensors. This article presents a new solution for a laser-based multisensing system for noncontact rail profile measurement and wear inspection. The addition of an inertial measurement unit (IMU) and a camera module allows portable rail profile measurement without “blind spots.” Optimized iterative closest point (ICP) registration is then applied to generate a complete representation of the rail profile. Experimental results demonstrate that the proposed system can provide accurate and efficient rail profile measurement, and could potentially replace conventional contact-based inspection tools.

As a typical railway Cyber-physical System (CPS), radio-based train control systems have been playing an increasingly important role in rail transit. The engaged network, Global System for Mobile Communications for Railway (GSM-R), which is an out-dated wireless communication technology, will be decommissioned due to diminishing support from industry, and a new generation successor, e.g. Long-Term Evolution (LTE), is urgently required to replace the current network. The radio-based train control systems must be safety critical, which relies on a high-security Data Communication System (DCS). In this paper, a novel wireless network migration methodology in DCS is proposed. By using this methodology, the high-security required DCS performance in radio-based train control systems is maintained and the network migration cost, e.g. the used number of base station (BS), is reduced when updating the GSM-R to LTE.

Background Despite their important role in railway operations, switches and crossings (S&C) have changed little since their conception over a century ago. It stands now that the existing designs for S&C are reaching their maximum point of incremental performance improvement, and only a radical redesign can overcome the constraints that current designs are imposing on railway network capacity. This paper describes the process of producing novel designs for next generation switches and crossings, as part of the S-CODE project. Methods Given the many aspects that govern a successful S&C design, it is critical to adopt multi criteria decision making (MCDM) processes to identify a specific solution for the next generation of switches and crossings. However, a common shortcoming of these methods is that their results can be heavily influenced by external factors, such as uncertainty in criterium weighting or bias of the evaluators, for example. This paper therefore proposes a process based on the Pugh Matrix method to reduce such biases by using sensitivity analysis to investigate them and improve the reliability of decision making. Results In this paper, we analysed the influences of three different external factors, measuring the sensitivity of ranking due to (a) weightings, (b) organisational and (c) discipline bias. The order of preference of the results was disturbed only to a minimum while small influences of bias were detected. Conclusions Through this case study, we believe that the paper demonstrates an effective case study for a quantitative process that can improve the reliability of decision making.

An improved method with feature extraction and neural network classification is proposed and applied to address the fault diagnosis problem in railway switch systems. Features are extracted by analyzing the nonstationary signal time–frequency characteristics of the electromechanical machine under different fault severities. To enhance the fault diagnosis capability, an energy-based thresholding wavelets (EBTW) approach is proposed by reconstructing a low-dimensional feature vector with selected parameters. Different from the conventional discrete wavelet transform and soft-thresholding wavelet transform, the proposed method localizes and redistributes the signal energy to achieve an efficient dimension reduction. The first step of the proposed method is to perform a wavelet transform upon the original signals, whose wavelet coefficients are then thresholded and an extraction of features is made, on the basis of the energy conservation property of wavelet transforms. Once the features are extracted and selected, several state-of-the-art neural-network-based classifiers are applied for fault diagnosis by identifying the signal features from a machine normal operation and faulty operations with multiple severities. The effectiveness of the proposed method is verified using real-world operational railway switch data and compared with conventional feature extraction methods under different classifiers.

Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser-based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human-dominated defect classification and evaluation work. This study presents a 3D model-based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi-class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model-based automatic track surface defect inspection.

Roller bearings are one of the most safety-critical components in many machines. Predicting the vibration-based remaining useful life of roller bearings allows operators to make informed maintenance decisions and to guarantee reliability and safety. The health indices (HIs) for degradation assessment are constructed by extracting feature information from the collected data, which significantly influences the prognosis result. Conventional HI construction methods rely heavily on expert knowledge and also have limited capacity for learning health information from the raw data from roller bearings. Furthermore, outlier regions often occur in HIs developed by those methods, and these can easily result in false alarms. To address these problems, a novel HI construction method based on a deep multilayer perceptron (MLP) convolution neural network (DMLPCNN) model, which also considers outlier regions, is proposed in this paper. In the proposed model, a 1-D MLP convolution block, consisting of a convolution layer and a micronetwork, is applied to learn features directly from the vibrational data. The learned features are then mapped into an HI using a global average pooling layer and a logistic regression layer. Finally, an outlier region correction method, based on sliding thresholds, is proposed to detect and remove outliers in the HI. The outlier region correction method is able to enhance the interpretability of the constructed HI. The effectiveness of the proposed method is verified using whole-life data sets of 17 bearings. Experimental results demonstrate that the proposed method outperforms conventional methods.

Nowadays the railway industry is beginning to give serious consideration to using intelligent traffic management systems (TMSs) in order to improve railway performance regarding train and passenger delays and robust use of capacity. The TMS is responsible for handling railway traffic once a disturbance happens. A fundamental input parameter of a TMS is the train positions, to be used for traffic re-planning purposes. Inaccuracy in the train positioning data could significantly influence the effectiveness of a TMS. In this study, the authors developed a framework to evaluate how inaccuracies in the train position reporting may affect the TMS performance. This is achieved by assessing the impact of adding inaccuracies to the train position reported to a simulated TMS as it handles operational disturbances in real-time. The performance of the TMS is analysed by considering variability in overall delay outcomes after re-planning based on using accurate/inaccurate positional data. They demonstrate the usefulness of their framework in determining the positional accuracy required for the effective application of a basic rescheduling system via an example on a bottleneck area. Results show how the positioning inaccuracies can affect TMS and thus the overall delay.

The development of conductive inks is required to enable additive manufacturing of electronic components and devices. A gold nanoparticle (AuNP) ink is of particular interest due to its high electrical conductivity, chemical stability, and biocompatibility. However, a printed AuNP film suffers from thermally induced microcracks and pores that lead to the poor integrity of a printed electronic component and electrical failure under external mechanical deformation, hence limiting its application for flexible electronics. Here, we employ a multifunctional thiol as a cohesion enhancer in the AuNP ink to prevent the formation of microcracks and pores by mediating the cohesion of AuNPs via strong interaction between the thiol groups and the gold surface. The inkjet-printed AuNP electrode exhibits an electrical conductivity of 3.0 × 106 S/m and stable electrical properties under repeated cycles (>1000) of mechanical deformation even for a single printed layer and in a salt-rich phosphate-buffered saline solution, offering exciting potential for applications in flexible and 3D electronics as well as in bioelectronics and healthcare devices.

There have been many efforts to improve oral drug bioavailability and therapeutic efficacy and patient compliance. A variety of controlled-release oral delivery systems have been developed to meet these needs. Gastroretentive drug delivery technologies have the potential to achieve retention of the dosage form in the upper gastrointestinal tract (GIT) that can be sufficient to ensure complete solubilisation of the drugs in the stomach fluids, followed by subsequent absorption in the stomach or proximal small intestine. This can be beneficial for drugs that have an “absorption window” or are absorbed to a different extent in various segments of the GIT. Therefore, gastroretentive technologies in tandem with controlled-release strategies could enhance both the therapeutic efficacy of many drugs and improve patient compliance through a reduction in dosing frequency. The paper reviews different gastroretentive drug delivery technologies and controlled-release strategies that can be combined and summarises examples of formulations currently in clinical development and commercially available gastroretentive controlled-release products. The different parameters that need to be considered and monitored during formulation development for these pharmaceutical applications are highlighted.

Controlling the microstructure of materials by means of phase separation is a versatile tool for optimizing material properties. Phase separation has been exploited to fabricate intricate microstructures in many fields including cell biology, tissue engineering, optics, and electronics. The aim of this study was to use phase separation to tailor the spatial location of drugs and thereby generate release profiles of drug payload over periods ranging from 1 week to months by exploiting different mechanisms: polymer degradation, polymer diluent dissolution, and control of microstructure. To achieve this, we used drop-on-demand inkjet three-dimensional (3D) printing. We predicted the microstructure resulting from phase separation using high-throughput screening combined with a model based on the Flory-Huggins interaction parameter and were able to show that drug release from 3D-printed objects can be predicted from observations based on single drops of mixtures. We demonstrated for the first time that inkjet 3D printing yields controllable phase separation using picoliter droplets of blended photoreactive oligomers/monomers. This new understanding gives us hierarchical compositional control, from droplet to device, allowing release to be "dialled up" without manipulation of device geometry. We exemplify this approach by fabricating a biodegradable, long-term, multiactive drug delivery subdermal implant ("polyimplant") for combination therapy and personalized treatment of coronary heart disease. This is an important advance for implants that need to be delivered by cannula, where the shape is highly constrained and thus the usual geometrical freedoms associated with 3D printing cannot be easily exploited, which brings a hitherto unseen level of understanding to emergent material properties of 3D printing.

Oral delivery of amphotericin B (AmpB) is desirable because it provides a more patient-friendly mode of administration compared to the current delivery approach akin with the marketed AmpB formulations. The goal of the study was to investigate the pharmacokinetics and tissue distribution of orally administered chitosan-coated AmpB-loaded nanostructured lipid carriers (ChiAmpB NLC) administered to Sprague Dawley rats at a dose of 15 mg/kg. Orally administered ChiAmpB NLC resulted in a two-fold increase in the area under the curve (AUC

Train delays occur often in daily railway operations due to a variety of initiating incidents. On a heavily loaded mainline railway, a single train delay may lead to a series of secondary delays across the network. In this study, the authors describe a peer-to-peer system to solve train rescheduling problems in railway network bottlenecks. A designed Genetic Algorithm is chosen as the local search algorithm on each side. Based on the local search algorithm, different negotiation protocols are raised to find globally feasible solutions. The proposed approach is tested in a railway bottleneck section in the UK, and the computational result is compared with a centralised method to show its performance in terms of computation time and optimality.

Communication-based train control (CBTC) systems have been playing a progressively significant role in metro signaling in recent years. As safety-critical systems, CBTC systems have very strict requirements on the wireless communication performance between train and wayside access points (AP), which is highly dependent on the deployment of the APs. In this paper, by customizing and adopting a decomposition-based multiobjective evolutionary algorithm, the proposed AP deployment optimization method has been implemented, verified, and its implementation accuracy has been assessed. A real-world case study is carried out in an integrated simulation platform, in which the optimized AP deployments are verified and show better performance than the original planning.

In railway condition monitoring, feature classification is a very critical step, and the extracted features are used to classify the types and levels of the faults. To achieve better accuracy and efficiency in the classification, the extracted features must be properly selected. In this paper, maximal information coefficient is employed in two different stages to establish a new feature selection method. By using this proposed two-stage feature selection method, strong features with low redundancy are reserved as the optimal feature subset, which results in the classification process having a more moderate computational cost and good overall performance. To evaluate this proposed two-stage selection method and prove its advantages over others, a case study focusing on the rolling bearing is carried out. The result shows that the proposed selection method can achieve a satisfactory overall classification performance with low-computational cost.

As demand for rail transportation continues to increase rapidly, significant challenges have emerged in many railway systems in terms of Capacity, Safety and Customer Satisfaction. To deal with these challenges, intelligent technologies, such as artificial intelligence, big data, and machine learning, have been gradually introduced to rail transportation. It is therefore timely and appropriate to have a focused investigation and discussion about Intelligent Rail Transportation. This special issue provides a forum for scientists and engineers working in academia, industry, and government to present their latest research findings and engineering experiences in developing and applying intelligent technologies to improve railway’s autonomy, cooperation, and integration.

For metro systems in over-crowded conditions, when an unexpected disturbance occurs, the operation of trains might be disturbed due to the high frequency and density of the metro traffic. A large number of passengers might be stranded on platforms due to service gaps and the limited free capacity of trains. In this paper, by introducing binary variables as selection indicators for ATO profiles which were preset in on-board ATO systems by metro signal suppliers, we develop a mixed integer programming (MIP) model for a metro train timetable rescheduling problem in order to jointly optimize the total train delay, the number of stranded passengers, and the energy consumption of trains. We formulate the total energy consumption as the difference between the tractive energy consumption and the regenerated energy by considering the mass of in-vehicle passengers. Then, we adopt commercial optimization software CPLEX to solve the proposed model, which can obtain tradeoff solutions in a short time. Finally, three numerical experiments based on real-world operational data are carried out to verify the effectiveness of the proposed method.

Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems and optimised fleet availability. This paper is a review of the state of the art in condition monitoring systems for rolling element bearings, especially the axlebox bearings. This includes exploring the sensing technologies, summarising the main signal processing methods and condition monitoring techniques, i.e. wayside and on-board. Examples of commercially available systems and outputs of current research work are presented. The effectiveness of the current monitoring technologies is assessed and the p– f curve is presented. It is concluded that the research and practical tests on axlebox bearing monitoring are limited compared to the generic bearing applications.

Inkjet printing of metal nanoparticles allows for design flexibility, rapid processing and enables the 3D printing of functional electronic devices through co-deposition of multiple materials. However, the performance of printed devices, especially their electrical conductivity, is lower than those made by traditional manufacturing methods and is not fully understood. Here, we reveal that anisotropic electrical conductivity of printed metal nanoparticles is caused by organic residuals from their inks. We employ a combination of electrical resistivity tests, morphological analysis and 3D nanoscale chemical analysis of printed devices using silver nanoparticles to show that the polymer stabiliser polyvinylpyrrolidone tends to concentrate between vertically stacked nanoparticle layers as well as at dielectric/conductive interfaces. Understanding the behaviour of organic residues in printed nanoparticles reveals potential new strategies to improve nanomaterial ink formulations for functional printed electronics. Inkjet printed metal nanoparticles are known to have lower electrical conductivity than those produced by traditional approaches. Here, anisotropic electrical conductivity is attributed to organic residuals from the metal nanoparticle ink, reducing conductivity.

In the pharmaceutical industry, the ability to improve the understanding of the effect of surface roughness on interparticulate interactions is critical. Dry powder inhalers often possess poor efficiency, as the powder formulations are inherently adhesive and cohesive due to their size. The complex interplay of factors that affect interparticulate interactions, means it has been difficult to isolate the effect of surface morphology. Using two photon polymerisation, this study shows the fabrication of bespoke sub-micron geometric structures, with a consistent surface chemistry. These are used to investigate the effect of surface morphologies on particle adhesion by utilising AFM force-volume mapping, to model spheres and carrier particles. This demonstrates the significant effect varying surface morphology can have on particle-surface adhesion. This approach allows for the first time an in-depth examination of the local variation effect of surface features on particle adhesion and may facilitate the design and optimisation of powder processes.

An extrusion-based 3D printer has been used for the manufacturing of sustained drug release poly(e-caprolactone) (PCL) implants. Such implants can address issues of reduced patient compliance due to the necessary frequent administration of conventional drug delivery systems, such as tablets, capsules and solutions. The selected model drug for this study was lidocaine. Polycaprolactone core-shell implants, as well as polymeric implants with no barrier shell were printed with different drug loading, without the addition of solvents or further excipients. Scanning Electron Microscopy (SEM) analysis revealed the structural integrity of the printed formulations, while Differential Scanning Calorimetry (DSC), X-Ray Diffraction (XRD) and Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) were used to detect potential chemical interactions or modifications. Raman spectroscopy was also used to study material distribution in the prints. The drug release rate of the differently printed formulations was evaluated using a USP4 flow-through cell apparatus. All printed implants demonstrated sustained lidocaine release and the effectiveness of the PCL barrier in this regard. The Korsmeyer-Peppas model was suggested as the best fit to drug release profiles for all the produced implants. This work demonstrates that hot-melt extrusion-based 3D printing is a robust and promising technology for the production of personalisable drug-eluting implants.

Inkjet printing of metal nanoparticles (MNPs) allows for design flexibility, rapid processing and enables 3D printing of functional electronic devices through co-deposition of multiple materials. However, the performance of printed devices, especially their conductivity, is lower than those made by traditional manufacturing methods and is previously not fully understood. Here, we revealed that anisotropic electrical conductivity of printed MNPs is caused by organic residuals from MNPs inks. We employed a combination of electrical resistivity tests, morphological analysis and novel 3D nanoscale chemical analysis of printed devices using silver nanoparticles (AgNPs) to show that the polymer stabiliser polyvinylpyrrolidone (PVP) tends to concentrate between vertically stacked AgNPs layers as well as at dielectric/conductive interfaces. The understanding of organic residues behaviour in printed nanoparticles reveal potential new strategies to improve nanomaterial ink formulations for functional printed electronics.

Recent developments in the area of condition monitoring research have been targeted towards predicting machinery health condition for the purpose of preventative maintenance. Typically, published research uses data collected from rotating components (bearings, cutting tools, etc.) working in an idealized lab environment as the case study for prognosis algorithm validations. However, the operational implementation in industry is still very sporadic, mainly owing to the lack of proper data allowing sufficiently mature development of comprehensive methodologies. The prognosis methodology presented herein bridges the gap between academic research and industrial implementations by employing a novel time period for prognosis and implementing random coefficients regression models. The definition of the remaining maintenance-free operating period (RMFOP) is proposed first, which helps to transform the usefulness of the degradation data that is readily available from data short of failure. Degradation patterns are subsequently extracted from the original degradation data, before fitting into either of two regression models (linear or exponential). The system residual life distributions are then computed and updated by estimating the parameter statistics within the model. This RMFOP-based methodology is validated using real-world degradation data collected from multiple operational railway switch systems across Great Britain. The results indicate that both the linear model and the exponential model can produce residual life distributions with a sufficient prediction accuracy for this specific application. The exponential model gives better predictions, the accuracy of which also improves as more of system life percentage has elapsed. By using the RMFOP methodology, switch system health condition affected by an incipient overdriving fault is recognized and predicted.

Work undertaken on the socio-economic assessment of the installation of under-sleeper pads (USPs) on a busy commuter railway route in Britain, as part of the Track to the Future (T2F) project, was presented at COMPRAIL 2018. This was followed by an expanded, comparative analysis of the same route, comparing the noise-reduction and other costs and benefits of USPs, rail dampers and noise barriers, published in the International Journal of Transport Development and Integration. Such analyses provide useful information and decision-making assistance to infrastructure managers, funders and other stakeholders, but typically require the repeated development of bespoke models of study locations, interventions and effects. The “one-off” nature of these models increases the time and costs required for such studies, and makes it more difficult to compare similar schemes in different locations, or different types of intervention in a single location. There is, therefore, a need for an improved, more generalised and standardised, transferable, replicable and comparison-enabling approach to the socioeconomic assessment of such interventions. Ongoing work on the T2F project entails the development and use of standardised templates to describe the input costs and expected impacts of different infrastructure, rolling stock or operational interventions. These form the inputs to, and part of the development for, T2F of an improved, generalised, ontology-based assessment framework, which is being developed using the West Coastway railway line between Portsmouth and Brighton on England’s south coast as an initial case study. This paper describes the context and rationale for the development of the assessment framework, its components and the methodology being adopted.

As our understanding of disease grows, it is becoming established that treatment needs to be personalized and targeted to the needs of the individual. In this paper we show that multi-material inkjet-based 3D printing, when backed with generative design algorithms, can bring a step change in the personalization of medical devices. We take cell-instructive materials known for their resistance to bacterial biofilm formation and reformulate for multi-material inkjet-based 3D printing. Specimens with customizable mechanical moduli are obtained without loss of their cell-instructive properties. The manufacturing is coupled to a design algorithm that takes a user-specified deformation and computes the distribution of the materials needed to meet the target under given load constraints. Optimisation led to a voxel map file defining where different materials should be placed. Manufactured products were assessed against the mechanical and cell-instructive specifications and ultimately showed how multifunctional personalization emerges from generative design driven 3D printing.

Controlling the microstructure of materials by means of phase separation is a versatile tool for optimizing material properties. In this study, we show that ink jet 3D printing of polymer blends gives rise to controllable phase separation that can be used to tailor the release of drugs. We predicted phase separation using high throughput screening combined with a model based on the Flory-Huggins interaction parameter, and were able to show that drug release from 3D printed structures can be predicted from observations based on single drops of mixtures. This new understanding gives us hierarchical compositional control, from droplet to device, allowing release to be ‘dialed up’ without any manipulation of geometry. This is an important advance for implants that need to be delivered by cannula, where the shape is highly constrained and thus the usual geometrical freedoms associated with 3D printing cannot be exploited, bringing a hitherto unseen level of understanding to emergent material properties of 3D printing.

Pattern recognition and classification have been recently applied in the area of condition monitoring to solve the problems of fault diagnosis and prognosis. Usually, the original condition monitoring data will be transformed into representative features that are of lower dimensionality, followed by the application of a classifier. However, most time-domain and frequency-domain features cannot provide good representations of non-stationary signals that are very common in machine faults. This research solves this problem by applying a Discrete Wavelet Transform (DWT) to extract the time-frequency features. Subsequently, a feed-forward artificial neural network is used as a method for fault diagnosis and classification. A railway switch system is used as a specific application in this research to validate the method using data from a field switch system. Features of the scale coefficients are then extracted using DWT for the 'Haar' wavelet type at decomposition level 9. Subsequently, the feature data are split into training, validation and testing sets, with each set containing one input feature and one corresponding desired output fault severity. After training and testing using a two-layer neural network, it is shown that in this case the switch overdriving fault with different severities could be diagnosed with 100% accuracy.

Organic-inorganic hybrid materials enable the design and fabrication of new materials with enhanced properties. The interface between the organic and inorganic materials is often critical to the device's performance; therefore, chemical characterization is of significant interest. Because the interfaces are often buried, milling by focused ion beams (FIBs) to expose the interface is becoming increasingly popular. Chemical imaging can subsequently be obtained using secondary-ion mass spectrometry (SIMS). However, the FIB milling process damages the organic material. In this study, we make an organic-inorganic test structure to develop a detailed understanding of the processes involved in FIB milling and SIMS imaging. We provide an analysis methodology that involves a "clean-up" process using sputtering with an argon gas cluster ion source to remove the FIB-induced damage. The methodology is evaluated for two additive manufactured devices, an encapsulated strain sensor containing silver tracks embedded in a polymeric material and a copper track on a flexible polymeric substrate created using a novel nanoparticle sintering technique.

The railway relay plays an important role in railway systems. Its reliability has a significant effect on the safety of passengers and train operation, which can be reflected using degradation parameters. In this paper, a novel hybrid method based on the multi-layer decomposition and the radial basis function neural network (RBFNN) is proposed for life prediction of railway relays. As the degradation parameter series are usually nonlinear and non-stationary, it is vital to develop an essential method to preprocess the degradation series. In order to improve the prediction accuracy, a multi-layer decomposition method is developed first for data pre-processing, which blends complete ensemble empirical mode decomposition (CEEMD) and an improved variational mode decomposition (IVMD) with a stopping criterion for determining the decomposition modes number. It is noted that IVMD is then used to decompose the high-frequency intrinsic mode functions (IMFs) obtained using CEEMD to improve the prediction accuracy. Furthermore, RBFNN is applied to all the components for prediction. And the prediction results of all the components are reconstructed as the predicted degradation series. Finally, the effectiveness and robustness of the proposed novel hybrid prediction method are verified on one-step prediction and multi-step prediction by comparing other commonly used prediction methods. The experimental results indicate that the proposed hybrid prediction method performs best on the complex degradation parameters of safety relays.

Smart grids are considered to be the ‘next generation’ of electricity supply systems, capable of increasing supply reliability, availability and energy efficiency through the use of information and communication technologies. Despite these advantages, however, the development of smart grids in rail has lagged behind the domestic sector and other industries, only recently becoming a focus of the future railway. Generally speaking, the technologies suitable for railway smart grids are already being used in other sectors, but the unique socio-political and technological environment of rail makes their implementation challenging. This review explores smart grids in the rail context, focusing on the specific drivers, benefits and challenges for the development of railway smart grids. The necessity of rail as a future transport mode is highlighted, before the following drivers and their related benefits are explored: fossil-fuel reliance, supply reliability, customer participation and the nature of rail traction demand. Finally, the railway power supply system is described and a simple railway smart grid architecture introduced before seven technical challenges are presented against the rail background. These are: interfacing new equipment, electromagnetic compatibility, developing communications, distributed generation, cybersecurity, data and standardisation and regulation. It is hoped that this review will stimulate discussion in the field of railway smart grids and direct research into addressing the railway specific challenges hindering smart grid implementation.

Railway pantographs are used around the world for collecting electrical energy to power railway vehicles from the overhead catenary. Faults in the pantograph system degrade the quality of the contact between the pantograph and catenary and reduce the reliability of railway operations. To maintain the pantographs in a good working condition, regular inspection tasks are carried out at rolling stock depots. The current pantograph inspections, in general, are only effective for the detection of major faults, providing limited incipient fault detection or fault diagnosis capabilities. Condition monitoring of pantographs has the potential to improve pantograph performance and reduce maintenance costs. As a first step in the realisation of practical pantograph condition monitoring, a laboratory-based pantograph test rig has been developed to gain an understanding of pantograph dynamic behaviours, particularly when incipient faults are present. In the first work of this kind, dynamic response data have been acquired from a number of pantographs that have allowed fault detection and diagnosis algorithms to be developed and verified. Three tests have been developed: (i) a hysteresis test that uses different excitation speeds, (ii) a frequency response test that uses different excitation frequencies, and (iii) a novel changing gradient test. Verification tests indicate that the hysteresis test is effective in detecting and diagnosing pneumatic actuator and elbow joint faults. The frequency response test is able to monitor the overall degradation in the pantograph. The changing gradient test provides fault detection and diagnosis in the pantograph head suspension and pneumatic actuator. The test rig and fault detection and diagnosis algorithms are now being developed into a depot-based prototype together with a number of industrial partners.