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

An ensemble-based change-point detection method for identifying unexpected behaviour of railway tunnel infrastructures

A large amount of data is generated by Structural Health Monitoring (SHM) systems and, as a consequence, processing and interpreting this data can be difficult and time consuming. Particularly, if work activities such as maintenance or modernization are carried out on a bridge or tunnel infrastructure, a robust data analysis is needed, in order to accurately and quickly process the data and provide reliable information to decision makers. In this way the service disruption can be minimized and the safety of the asset and the workforce guaranteed.In this paper a data mining method for detecting critical behaviour of a railway tunnel is presented. The method starts with a pre-processing step that aims to remove the noise in the recorded data. A feature definition and selection step is then performed to identify the most critical area of the tunnel. An ensemble of change-point detection algorithms is proposed, in order to analyse the critical area of the tunnel and point out the time when unexpected behaviour occurs, as well as its duration and location. The work activities, which are carried out at the time of occurrence of the critical behaviour and have caused this behaviour, are finally identified from a database of the work schedule and used for the validation of the results. Using the proposed method, fast and reliable information about infrastructure condition is provided to decision makers

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

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)

Transient identification by clustering based on Integrated Deterministic and Probabilistic Safety Analysis outcomes

open3noIn this work, we present a transient identification approach that utilizes clustering for retrieving scenarios information from an Integrated Deterministic and Probabilistic Safety Analysis (IDPSA). The approach requires: (i) creation of a database of scenarios by IDPSA; (ii) scenario post-processing for clustering Prime Implicants (PIs), i.e., minimum combinations of failure events that are capable of leading the system into a fault state, and Near Misses, i.e., combinations of failure events that lead the system to a quasi-fault state; (iii) on-line cluster assignment of an unknown developing scenario. In the step (ii), we adopt a visual interactive method and risk-based clustering to identify PIs and Near Misses, respectively; in the on-line step (iii), to assign a scenario to a cluster we consider the sequence of events in the scenario and evaluate the Hamming similarity to the sequences of the previously clustered scenarios. The feasibility of the analysis is shown with respect to the accidental scenarios of a dynamic Steam Generator (SG) of a NPP.Di Maio, Francesco; Vagnoli, Matteo; Zio, EnricoDI MAIO, Francesco; Vagnoli, Matteo; Zio, Enric

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

Determination of prime implicants by differential evolution for the dynamic reliability analysis of non-coherent nuclear systems

open4We present an original computational method for the identification of prime implicants (PIs) in non-coherent structure functions of dynamic systems. This is a relevant problem for dynamic reliability analysis, when dynamic effects render inadequate the traditional methods of minimal cut-set identification. PIs identification is here transformed into an optimization problem, where we look for the minimum combination of implicants that guarantees the best coverage of all the minterms. For testing the method, an artificial case study has been implemented, regarding a system composed by five components that fail at random times with random magnitudes. The system undergoes a failure if during an accidental scenario a safety-relevant monitored signal raises above an upper threshold or decreases below a lower threshold. Truth tables of the two system end-states are used to identify all the minterms. Then, the PIs that best cover all minterms are found by Modified Binary Differential Evolution. Results and performances of the proposed method have been compared with those of a traditional analytical approach known as Quine-McCluskey algorithm and other evolutionary algorithms, such as Genetic Algorithm and Binary Differential Evolution. The capability of the method is confirmed with respect to a dynamic Steam Generator of a Nuclear Power Plant.Di Maio, Francesco; Baronchelli, Samuele; Vagnoli, Matteo; Zio, EnricoDI MAIO, Francesco; Baronchelli, Samuele; Vagnoli, Matteo; Zio, Enric

A Bayesian Belief Network method for bridge deterioration detection

Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge