Maintenance and Inspection of Fiber-Reinforced Polymer (FRP) Bridges: A Review of Methods

Fiber-reinforced polymers (FRPs) are materials that comprise high-strength continuous fibers and resin polymer, and the resins comprise a matrix in which the fibers are embedded. As the technique of FRP production has advanced, FRPs have attained many incomparable advantages over traditional building materials such as concrete and steel, and thus they play a significant role in the strengthening and retrofitting of concrete structures. Bridges that are built out of FRPs have been widely used in overpasses of highways, railways and streets. However, damages in FRP bridges are inevitable due to long-term static and dynamic loads. The health of these bridges is important. Here, we review the maintenance and inspection methods for FRP structures of bridges and analyze the advantages, shortcomings and costs of these methods. The results show that two categories of methods should be used sequentially. First, simple methods such as visual inspection, knock and dragging-chain methods are used to determine the potential damage, and then radiation, modal analysis and load experiments are used to determine the damage mode and degree. The application of FRP is far beyond the refurbishment, consolidation and construction of bridges, and these methods should be effective to maintain and inspect the other FRP structures.

Motion Planning for Vibration Reduction of a Railway Bridge Maintenance Robot with a Redundant Manipulator

Motivated by the potential applications of maintenance and inspection tasks for railway bridges, we have developed a biped climbing robot. The biped climbing robot can climb on the steel guardrail of the railway bridge with two electromagnetic feet and implement the maintenance and inspection tasks by a redundant manipulator with 7 degrees of freedom. To reduce the vibration of the manipulator caused by the low rigidity of the guardrail and the discontinuous trajectories of joints, a motion planning algorithm for vibration reduction is proposed in this paper. A geometric path accounting for obstacle avoidance and the manipulator’s center of gravity is determined by the gradient projection method with a singularity-robust inverse. Then, a piecewise quintic polynomial S shape curve with a smooth jerk (derivative of joint angular acceleration) profile is used to interpolate the sequence of joint angular position knots that are transformed from the via-points in the obstacle-avoidance path. The parameters of the quintic polynomial S-curve are determined by a nonlinear programming problem in which the objective function is to minimize the maximus of the torque exerted by the manipulator on the guardrail throughout the jerk-continuous trajectory. Finally, a series of simulation experiments are conducted to validate the effectiveness of the proposed algorithm. The simulation results show that the tracking errors of the trajectory with the proposed optimization algorithm are significantly smaller than the tracking errors of the trajectory without optimization. The absolute values of mean deviation of the tracking errors of the three coordinate axes decreased by at least 48.3% compared to the trajectory without vibration-reduction in the triangle working path and linear working path trajectory following simulations. The analysis results prove that the proposed algorithm can effectively reduce the vibration of the end effector of the manipulator.

Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

Detecting anomalies in the Brake Operating Unit (BOU) braking system of metro trains is very important for trains’ reliability and safety. However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. First, we extracted brake cylinder (BC) pressure data from the BOU data since one of the anomaly cases of metro trains is that BC pressure relief time is delayed by 4 s. After that, extracted BC pressure data is split into subsequences which are fed into our proposed one-class LSTM autoencoder which consists of two LSTM blocks (encoder and decoder). The one-class LSTM autoencoder is trained using training data which only consists of normal subsequences. To detect anomalies from test data that contain abnormal subsequences, the mean absolute error (MAE) for each subsequence is calculated. When the error is larger than a predefined threshold which was set to the maximum value of MAE in the training (normal) dataset, we can declare that example an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental results show that our proposed method can detect anomalies of the BOU data well.

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.

Engineering Interoperable, Plug-and-Play, Distributed, Robotic Control Systems for Futureproof Fusion Power Plants

Maintenance and inspection systems for future fusion power plants (e.g., STEP and DEMO) are expected to require the integration of hundreds of systems from multiple suppliers, with lifetime expectancies of several decades, where requirements evolve over time and obsolescence management is required. There are significant challenges associated with the integration, deployment, and maintenance of very large-scale robotic systems incorporating devices from multiple suppliers, where each may utilise bespoke, non-standardised control systems and interfaces. Additionally, the unstructured, experimental, or unknown operational conditions frequently result in new or changing system requirements, meaning extension and adaptation are necessary. Whilst existing control frameworks (e.g., ROS, OPC-UA) allow for the robust integration of complex robotic systems, they are not compatible with highly efficient maintenance and extension in the face of changing requirements and obsolescence issues over decades-long periods. We present the CorteX software framework as well as results showing its effectiveness in addressing the above issues, whilst being demonstrated through hardware that is representative of real-world fusion applications.

The analysis of the impact of environmental factors on turbine performance, develpoment of the models relating the periods between maintenances with selected maintenance factors

There are currently around 18,000 commissioned Gas Turbines in use worldwide, with almost 7,500 long-term service agreements[1]. At the same time, orders for new units increase year by year, and after a decrease in production in 2020 from 353 to 328 new units, from 2022 onwards, the level is planned to rise to the previous level of growth. Gas turbines operate worldwide and are exposed to variations in environmental conditions, such as changes in humidity, temperature, and salinity, which can significantly affect the efficiency and faster degradation of individual components. Based on the unit's maintenance report, there are more than 1,940 event alerts annually. A need exists to create a more dynamic analytical and numerical model that determines the impact of environmental variables on gas turbine stability. It is necessary to analyze and improve existing reliability models, which vary due to configurations and the impact of working conditions. The first step should be an analysis of the impact of environmental factors on turbine performance. This paper describes how the maintenance and inspection model developed from an average value over time model to a model tracking the actual degradation of gas turbines. It includes a comparison ofthree models used in the research, considering the developed methodology for selecting input parameters, their correlation, and their appropriateness for use in further analyses.

Analysis of Maintenance and Inspection Status of Aeronautical Machinery and Improvement Countermeasures

In the daily operation process of airlines, aviation machinery maintenance and inspection work play an important role. At present, the quality of life of the people in our country is constantly improving. In daily travel, we have not only used cars, trains and other means of transportation, but also started to choose airplanes as the first choice in a higher frequency. In recent years, with the improvement of living standards, more and more people choose to travel during holidays. Therefore, the traffic volume of aviation aircraft is increasing year by year, but it also increases the risk of aviation aircraft failure, so it is particularly important to do a good job in the maintenance and inspection of aviation machinery. In this paper, according to the current situation of aviation machinery maintenance and inspection, put forward targeted improvement measures to ensure the safety and stability of China’s aviation aircraft operation.

Using expert judgement techniques to assess reliability for long service-life components: An application to railway wheelsets

Due to uncertainties in the deterioration process of long service-life assets, assessing reliability and planning maintenance and inspection activities are often difficult tasks. Most of the methods developed use operational or historical data from the manufacturer to predict the deterioration of the asset and estimate its reliability. However, in practice, such failure data is often scarce (e.g. very rare events) and the reliability prediction models might not be adapted to the operation of the user. In addition, available data gathered from maintenance or inspection tasks might only inform about a short period of time, and it might be difficult to obtain the corresponding reliability predictions as the associated uncertainty is still significant. Hence, this paper introduces a reliability assessment method that quantifies uncertainties regarding the failure of long service-life components. It combines Cooke’s classical method (also known as Structured Expert Judgement) with a histogram technique, creating a performance-based method to assess uncertainty in components lifetime distributions. A case study on railway wheelsets is analysed to illustrate the effectiveness of such approach. The results show that the presented method is able to assess failure rates, which can support decisions in reliability-based maintenance of engineering systems.