Railway bridge condition monitoring and fault diagnostics

The European transportation network is ageing continuously due to environmental threats, such as traffic, wind and temperature changes. Bridges are vital assets of the transportation network, and consequently their safety and availability need to be guaranteed to provide a safe transportation network to passenger and freights traffic. The main objective of this thesis is to develop a bridge condition monitoring and damage diagnostics method. The main element of the proposed Structural Health Monitoring (SHM) method is to monitor and assess the health state of a bridge continuously, by taking account of the health state of each element of the bridge. In this way, an early detection of the ongoing degradation of the bridge can be achieved, and a fast and cost-effective recovery of the optimal health state of the infrastructure can be achieved. A BBN-based approach for bridge condition monitoring and damage diagnostics is proposed and developed to assess and update the health state of the bridge continuously, by taking account of the health state of each element of the bridge. At the same time, the proposed BBN approach allows to detect and diagnose damage of the bridge infrastructure. Firstly, the BBN method is developed for monitoring the condition of two bridges, which are modelled via two Finite Element Models (FEMs). The Conditional Probability Tables (CPTs) of the BBN are defined by using an expert knowledge elicitation process. Results shows that the BBN allows to detect and diagnose damage of the bridges, however the performance of the BBN can be improved by pre-processing the data of the bridge behaviour and improving the definition of the CPTs. A data analysis methodology is then proposed to pre-process the data of the bridge behaviour, and to use the results of the analysis as an input to the BBN. The proposed data analysis methodology relies on a five-step process: i) remove of the outlier of the bridge data; ii) identify of the free-vibration of the bridge; iii) extract statistical, frequency-based and vibration -based features from the free-vibration behaviour of the bridge; iv) assess the features trend over time, by using the extracted features as an input to an Empirical Mode Decomposition (EMD) algorithm; v) evaluate of the Health Indicator (HI) of the bridge element. The proposed data analysis methodology is tested on two in-field bridges, a steel truss bridge and a post-tensioned concrete bridge, which are subject to a progressive damage test. A machine learning method is also developed in order to assess the health state of the bridge automatically. A Neuro Fuzzy Classifier (NFC) is adopted for this purpose. The results of the NFC can potentially be used as an input to the BBN nodes, to select the states of the BBN nodes, and improve the BBN performance. In fact, the NFC shows high accuracy in assessing the health state of bridge elements. An optimal set of HIs, which allows to maximize the accuracy of the NFC, is identified by adopting an iterative Modified Binary Differential Evolution (MBDE) method. The NFC is applied to the post-tensioned concrete in-field bridge that is subject to a progressive damage test. Hence, the performance of the BBN is improved significantly by pre-processing the bridge data, but also by developing a novel method to continuously update the CPTs of the BBN. The CPTs update process relies on the actual health state of the bridge element, and the knowledge of bridge engineers. Indeed, the CPT updating method aims to merge the expert knowledge with the analysis of the bridge behaviour. In this way, the diagnostic ability of the BBN is improved by merging the expertise of bridge engineer, who can analyse hypothetical damage scenarios of the bridge, and the analysis of a database of known bridge behaviour in different health states. The method is verified on the post-tensioned concrete in-field bridge, by developing a BBN to monitor the health state of the bridge continuously. The damages of the bridge are diagnosed by the proposed BBN. Finally, a method to analyse database of unknown infrastructure behaviour is finally proposed. An ensemble-based change-point detection method is presented to analyse a database of past unknown infrastructure behaviour. The method aims to identify the most critical change of the health state of the infrastructure, by providing the characteristics of such a change, in terms of time duration and possible causes. The method is applied to a database of tunnel behaviour, which is subject to renewal activities that influence the health state of the infrastructure

Towards a real-time Structural Health Monitoring of railway bridges

More than 350,000 railway bridges are present on the European railway network, making them a key infrastructure of the whole railway network. Railway bridges are continuously exposed to changing environmental threats, such as wind, floods and traffic load, which can affect safety and reliability of the bridge. Furthermore, a problem on a bridge can affect the whole railway network by increasing the vulnerability of the geographic area, served by the railway network. In this paper a Bayesian Belief Network (BBN) method is presented in order to move from visual inspection towards a real time Structural Health Monitoring (SHM) of the bridge. It is proposed that the health state of a steel truss bridge is continuously monitored by taking account of the health state of each bridge element. In this way, levels of bridge deterioration can be identified before they become critical, the risk of direct and indirect economic losses can be reduced by defining optimal bridge maintenance works, and the reliability of the bridge can be improved by identifying possible hidden vulnerabilities among different bridge elements

Risk-based clustering for near misses identification in integrated deterministic and probabilistic safety analysis

In integrated deterministic and probabilistic safety analysis (IDPSA), safe scenarios and prime implicants (PIs) are generated by simulation. In this paper, we propose a novel postprocessing method, which resorts to a risk-based clustering method for identifying Near Misses among the safe scenarios. This is important because the possibility of recovering these combinations of failures within a tolerable grace time allows avoiding deviations to accident and, thus, reducing the downtime (and the risk) of the system. The postprocessing risk-significant features for the clustering are extracted from the following: (i) the probability of a scenario to develop into an accidental scenario, (ii) the severity of the consequences that the developing scenario would cause to the system, and (iii) the combination of (i) and (ii) into the overall risk of the developing scenario. The optimal selection of the extracted features is done by a wrapper approach, whereby a modified binary differential evolution (MBDE) embeds a K-means clustering algorithm. The characteristics of the Near Misses scenarios are identified solving a multiobjective optimization problem, using the Hamming distance as a measure of similarity. The feasibility of the analysis is shown with respect to fault scenarios in a dynamic steam generator (SG) of a nuclear power plant (NPP)

A data mining tool for detecting and predicting abnormal behaviour of railway tunnels

The UK railway network is subjected to an electrification process that aims to electrify most of the network by 2020. This upgrade will improve the capacity, reliability and efficiency of the transportation system by providing cleaner, quicker and more comfortable trains. During this process, railway infrastructures, such as tunnels, require to be adapted in order to provide the necessary clearance for the overhead line equipment, and consequently, a rigorous real-time health monitoring programme is needed to assure safety of workforce. Large amounts of data are generated by the real-time monitoring system, and automated data mining tools are then required to process this data accurately and quickly. Particularly, if an unexpected behaviour of the tunnel is identified, decision makers need to know: i) activities at the worksite at the time of movement occurring; ii) the predicted behaviour of the tunnel in the next few hours. In this paper, we propose a data mining method which is able to automatically analyse the database of the real-time recorded displacements of the tunnel by detecting the unexpected tunnel behaviour. The proposed tool, first of all, relies on a step of data pre-processing, which is used to remove the measurement noise, followed by a feature definition and selection process, which aims to identify the unexpected critical behaviours of the tunnel. The most critical behaviours are then analysed by developing a change-point detection method, which detects precisely when the tunnel started to deviate from the predicted safe behaviour. Finally, an Artificial Neural Network (ANN) method is used to predict the future displacements of the tunnel by providing fast information to decision makers that can optimize the working schedule accordingly

Ensembles of climate change models for risk assessment of nuclear power plants

Climate change affects technical Systems, Structures and Infrastructures (SSIs), changing the environmental context for which SSI were originally designed. In order to prevent any risk growth beyond acceptable levels, the climate change effects must be accounted for into risk assessment models. Climate models can provide future climate data, such as air temperature and pressure. However, the reliability of climate models is a major concern due to the uncertainty in the temperature and pressure future projections. In this work, we consider five climate change models (individually unable to accurately provide historical recorded temperatures and, thus, also future projections), and ensemble their projections for integration in a probabilistic safety assessment, conditional on climate projections. As case study, we consider the Passive Containment Cooling System (PCCS) of two AP1000 Nuclear Power Plants (NPPs). Results provided by the different ensembles are compared. Finally, a risk-based classification approach is performed to identify critical future temperatures, which may lead to PCCS risks beyond acceptable levels

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

Updating conditional probabilities of Bayesian belief networks by merging expert knowledge and system monitoring data

Bayesian Belief Network (BBN) methods can be adopted for reliability analysis and real-time monitoring of critical infrastructure. Conditional Probability Tables (CPTs) are used to describe the dependencies between nodes in the BBN. The CPTs are usually defined by using either expert knowledge elicitation process or data-driven learning methods, and in particular, these methods define the CPTs statically, i.e. the CPTs are defined based only on the information available at the beginning of the analysis process. Therefore, the CPTs represent the correlation between the different components at one point in time, and potential correlations, evolving over time, are neglected. In this paper, a novel method to update the CPTs over time by merging expert judgment and the data about the system behaviour is proposed, which allows a process of continuous updating of CPTs. First of all, the method defines the CPTs by using the expert knowledge elicitation process. Then the CPTs are continuously updated whenever new data-driven evidence about the system becomes available. The proposed method is verified by assessing the health state of an in-field bridge, which experiences different magnitudes of damage. The BBN analysis of the health state of the bridge shows that the proposed method improves the accuracy of the BBN analysis, improving the diagnostics capabilities of the BBN up to 20%, by relying on a more robust and continuous updating of the CPTs

A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection

More than 35% of the European railway bridges are over 100 years old and the increasing traffic loads are pushing the railway infrastructure to its limits. Bridge condition-monitoring strategies can help the railway industry to improve safety, availability and reliability of the network. In this paper, a Bayesian Belief Network method for condition monitoring and fault detection of a truss steel railway bridge is proposed by relying on a fuzzy analytical hierarchy process of expert knowledge. The BBN method is proposed for obtaining the bridge health state and identifying the most degraded bridge elements. A Finite Element model is developed for simulating the bridge behaviour and studying a degradation mechanism. The proposed approach originally captures the interactions existing between the health state of different bridge elements and, furthermore, when the evidence about the displacement is introduced in the BBN, the health state of the bridge is updated