Extended Bow-Tie model for asset risk and reliability modeling: application to a passenger lift

A risk and reliability modelling framework for railway assets based on the Petri Net and the Bow-Tie models is proposed in this paper. A Petri Net model together with the Monte Carlo simulation is used to replicate the projected operational usage of the asset, inspection and maintenance policies and degra-dation of the asset and to estimate the future condition of the asset over time. Statistics obtained from the Petri Net are used as inputs to the Bow-Tie model, which is then used to estimate the risk of a hazardous event. The paper reports on the proposed methodology and the results of a case study of an underground passenger lift. In particular, the likelihood and the consequences of a lift getting stuck in shaft between landings are calculated

Qualitative analysis of complex modularized fault trees using binary decision diagrams

Fault tree analysis is commonly used in the reliability assessment of industrial systems. When complex systems are studied conventional methods can become computationally intensive and require the use of approximations. This leads to inaccuracies in evaluating system reliability. To overcome such disadvantages, the binary decision diagram (BDD) method has been developed. This method improves accuracy and efficiency, because the exact solutions can be calculated without the requirement to calculate minimal cut sets as an intermediate phase. Minimal cut sets can be obtained if needed. BDDs are already proving to be of considerable use in system reliability analysis. However, the difficulty is with the conversion process of the fault tree to the BDD. The ordering of the basic events can have a crucial effect on the size of the final BDD, and previous research has failed to identify an optimum scheme for producing BDDs for all fault trees. This paper presents an extended strategy for the analysis of complex fault trees. The method utilizes simplification rules that are applied to the fault tree to reduce it to a series of smaller subtrees whose solution is equivalent to the original fault tree. The smaller subtree units are less sensitive to the basic event ordering during BDD conversion. BDDs are constructed for every subtree. Qualitative analysis is performed on the set of BDDs to obtain the minimal cut sets for the original top event. It is shown how to extract the minimal cut sets from complex and modular events in order to obtain the minimal cut sets of the original fault tree in terms of basic events

Prime implicants for modularised non-coherent fault trees using binary decision diagrams

This paper presents an extended strategy for the analysis of complex fault trees. The method utilises simplification rules, which are applied to the fault tree to reduce it to a series of smaller subtrees, whose solution is equivalent to the original fault tree. The smaller subtree units are less sensitive to the basic event ordering during BDD conversion. BDDs are constructed for every subtree. Qualitative analysis is performed on the set of BDDs to obtain the prime implicant sets for the original top event. It is shown how to extract the prime implicant sets from complex and modular events in order to obtain the prime implicant sets of the original fault tree in terms of basic events

An efficient real-time method of analysis for non-coherent fault trees

Fault tree analysis is commonly used to assess the reliability of potentially hazardous industrial systems. The type of logic is usually restricted to AND and OR gates which makes the fault tree structure coherent. In non-coherent structures not only components’ failures but also components’ working states contribute to the failure of the system. The qualitative and quantitative analyses of such fault trees can present additional difficulties when compared to the coherent versions. It is shown that the Binary Decision Diagram (BDD) method can overcome some of the difficulties in the analysis of non-coherent fault trees. This paper presents the conversion process of non-coherent fault trees to BDDs. A fault tree is converted to a BDD that represents the system structure function (SFBDD). A SFBDD can then be used to quantify the system failure parameters but is not suitable for the qualitative analysis. Established methods, such as the meta-products BDD method, the zero-suppressed BDD (ZBDD) method and the labelled BDD (L-BDD) method, require an additional BDD that contains all prime implicant sets. The process using some of the methods can be time consuming and not very efficient. In addition, in real time applications the conversion process is less important and the requirement is to provide an efficient analysis. Recent uses of the BDD method are for real time system prognosis. In such situations as events happen, or failures occur the prediction of mission success is updated and used in the decision making process. Both qualitative and quantitative assessment are required for the decision making. Under these conditions fast processing and small storage requirements are essential. Fast processing is a feature of the BDD method. It would be advantageous if a single BDD structure could be used for both the qualitative and quantitative analyses. Therefore, a new method, the ternary decision diagram (TDD) method, is presented in this paper, where a fault tree is converted to a TDD that allows both qualitative and quantitative analyses and no additional BDDs are required. The efficiency of the four methods is compared using an example fault tree library

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

System failure modelling using binary decision diagrams

The aim of th1s thesis is to develop the Binary Decision Diagram method for the analysis of coherent and non-coherent fault trees. At present the well-known ite technique for converting fault trees to BDDs is used Difficulties appear when the ordering scheme for basic events needs to be chosen, because it can have a crucial effect on the size of a BDD An alternative method for constructing BDDs from fault trees which addresses these difficulties has been proposed The Binary Decision Diagram method provides an accurate and efficient tool for analysing coherent and non-coherent fault trees. The method is used for the qualitative and quantitative analyses and it is a lot faster and more efficient than the conventional techniques of Fault Tree Analysis The Simplification techniques of fault trees prior to the BDD conversion have been applied and the method for the qualitative analysis of BDDs for coherent and non-coherent fault trees has been developed A new method for the qualitative analysis of non-coherent fault trees has been proposed An analysis of the efficiency has been carried out, comparing the proposed method with the other existing methods for calculating prime implicant sets. The main advantages and disadvantages of the methods have been identified. The combined method of fault tree Simplification and the BDD approach has been applied to Phased Missions This application contains coherent and non-coherent fault trees Methods to perform thmr simplification, conversion to BDDs, minimal cut sets/prime implicant sets calculation, and the mission unreliability evaluation have been produced.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analysis of non-coherent fault trees using ternary decision diagrams

Risk and safety assessments performed on potentially hazardous industrial systems commonly utilise Fault Tree Analysis (FTA) to forecast the probability of system failure. The type of logic for the top event is usually limited to AND and OR gates which leads to a coherent fault tree structure. In non-coherent fault trees components’ working states as well as components’ failures contribute to the failure of the system. The qualitative and quantitative analyses of non-coherent fault trees can introduce further difficulties over and above those seen in the coherent case. It is shown that the Binary Decision Diagram (BDD) method can be used for this type of assessment. The BDD approach can improve the accuracy and efficiency of the quantitative analysis of non-coherent fault trees. This article demonstrates the value of the Ternary Decision Diagram method (TDD) for the qualitative analysis of non-coherent fault trees. Such analysis can be used to provide information to a decision making process for future actions of an autonomous system and therefore it must be performed in real time. In these circumstances fast processing and small storage requirements are very important. The TDD method provides a fast processing capability and small storage is achieved when a single structure is used for both qualitative and quantitative analyses. The efficiency of the TDD method is discussed and compared to the performance of the established methods for analysis of non-coherent fault trees

An efficient real-time method of analysis for non-coherent fault trees

Fault tree analysis is commonly used to assess the reliability of potentially hazardous industrial systems. The type of logic is usually restricted to AND and OR gates which makes the fault tree structure coherent. In non-coherent structures not only components’ failures but also components’ working states contribute to the failure of the system. The qualitative and quantitative analyses of such fault trees can present additional difficulties when compared to the coherent versions. It is shown that the Binary Decision Diagram (BDD) method can overcome some of the difficulties in the analysis of non-coherent fault trees. This paper presents the conversion process of non-coherent fault trees to BDDs. A fault tree is converted to a BDD that represents the system structure function (SFBDD). A SFBDD can then be used to quantify the system failure parameters but is not suitable for the qualitative analysis. Established methods, such as the meta-products BDD method, the zero-suppressed BDD (ZBDD) method and the labelled BDD (L-BDD) method, require an additional BDD that contains all prime implicant sets. The process using some of the methods can be time consuming and not very efficient. In addition, in real time applications the conversion process is less important and the requirement is to provide an efficient analysis. Recent uses of the BDD method are for real time system prognosis. In such situations as events happen, or failures occur the prediction of mission success is updated and used in the decision making process. Both qualitative and quantitative assessment are required for the decision making. Under these conditions fast processing and small storage requirements are essential. Fast processing is a feature of the BDD method. It would be advantageous if a single BDD structure could be used for both the qualitative and quantitative analyses. Therefore, a new method, the ternary decision diagram (TDD) method, is presented in this paper, where a fault tree is converted to a TDD that allows both qualitative and quantitative analyses and no additional BDDs are required. The efficiency of the four methods is compared using an example fault tree library

Fault propagation modelling for fluid system health monitoring

Fault diagnostics systems are incorporated to determine the health of the system they monitor. There are however times when the diagnostics system reports faults which do not exist. This situation commonly arises at system start-up when high vibration levels exist and the systems are not performing in the same way as when they are operational. Unnecessary shutdowns can occur due to transient behaviour of the system. On autonomous vehicles, such as Unmanned Aerial Vehicles (UAVs), information about the health of the system can be used to support the decision making process and to plan the future system operation. When faults are reported on autonomous systems, where there is no pilot to interpret the conditions reported, a method is needed to establish whether the reported faults do exist. Utilising a fault propagation modelling technique deviations in system variables can be propagated through the system until further evidence of fault presence is observed. If some evidence that contradicts the fault presence is found, the fault can be cancelled and unnecessary shutdowns can be avoided. In this paper a propagation table method is developed to model fault propagation through a system. The system is broken down into its constituent components and each model shows how process variables depend not only on the state of the component but also on the state of the entire system. The outputs of the two-way fault propagation modelling are values of process variables at different locations in the system. These values can be compared with the symptoms observed and used to cancel or confirm faults. This comparison process is accomplished at each phase that the system goes through during its defined mission. The illustration of the fault propagation methodology is given using an example system, and its application for the fault cancellation process is discussed

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