Aplicaciones terapéuticas potenciales

The automated generation of Fault Trees has received the most attention, based on literature findings. Several methods have been proposed for the automated construction of this reliability model, as reviewed in this section.

Decision table method, introduced by Salem et al. (1977), models the system behaviour describing the relation between the inputs, outputs and the states of components. This method acts as a matrix in which the columns of decision tables correspond to the inputs, outputs and states of the components and the rows present all the possible combinations between the inputs and component states along with their respective outputs. Therefore, the tables can present different states of the component such as working or failed and how the component behaves as a result of different inputs from other components within the system.

Salem et al. introduced the decision table method by developing in 1979 a computer package, the Computer Automated Tree (CAT). The CAT code takes as an input the decision tables, created by the user, and the TOP event specification to generate the fault trees. The software has been designed to cater for multiple FTs simultaneously. The CAT code has been successfully applied in a Residual Heat Removal (RHR) system, generating the required FT in a short time. The main limitations of this methodology are the effort the user should make to recognise and define the TOP event and create the components decision tables. Also, this method does not provide any facilities for the detection and classification of control loops or circuits.

A relatively new method based on decision tables is that introduced by Majdara and Wakabayashi (2010). This method introduces the concept of two types of tables to model system components. The first table is the function table, which is the same of the decision table introduced by Salem et al. 1977 and the second type is the novel state-transition table that describes the operational states of a component. The state transition table is created for components with different states and shows the state

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changes from initial state to final state. For example, the operational states of a switch are the open and close states. Both function and transition state tables are manually created by the user. Majdara and Wakabayashi also developed an algorithm to generate a FT based on an occurrence of an undesired event being defined. The code uses the input-output connections of components to trace the cause of the undesired event. The algorithm traces back from the occurrence and identifies the component states or outputs caused the event. Then the FT is generated based on the outputs. The FT is generated using the new tables following the same approach employed by Salem et al.

The digraph method is based on the construction of a directed graph, which consists of nodes that represent process variables. The process variables indicate properties of the flow such as mass flow rate, pressure, temperature and others. Any system or process can be described in terms of a flow such as flow of fluid, charge, data, information and signal. The nodes are connected by directed edges, where the direction is determined according to the relationship between the variables it joins. If a deviation in a variable A produces a deviation in variable B then the direction of the edge is from variable A to variable B. A directed edge can be characterised as: normal when the relationship is normally true; conditional when the relationship occurs only when a certain condition is satisfied; and mutually exclusive when several edges connect the same pair of nodes. Only one relationship is in operation at any one time. A number is assigned to the edge depending on the rate of change of the second deviation relative to the first. The values that can be used are: ‘-10’, ‘-1’, ‘0’, ‘+1’ and ‘+10’. The magnitude of deviation is indicated by none (0), moderate (1) or very large (10), by implying the following:

 None (0): if a moderate deviation in one process variable causes none/negligible deviation in another; there is no edge drawn between the two nodes.

 Moderate (1): if a moderate deviation in one causes a moderate deviation in another, the directed edge between the two nodes is denoted by 1 preceded by a sign.

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 Very large (10): if a moderate deviation in one causes a very large deviation in another, their relationship is denoted by 10 preceded by a sign.

The signs ‘+’ and ‘-’ depend on whether the deviations in the dependent variable increase, or decrease, when the independent variable increases. The number associated with directed edges is called gain and be considered as the partial derivative of the first variable with respect to the second variable.

Lapp and Powers 1977 were the first to integrate the digraph (directed graph) method into automated FT construction. This method starts with the development of the digraph by the user for a given system and then uses a programmed algorithm in order to transform the digraph into a fault tree. The digraph method constituted a remarkable achievement in the evolution of the automated generation of reliability models since it targeted modelling of complex systems including the identification and classification of control loops. In this approach, operators have been introduced which logically traverse fault propagation through Negative Feed-Back Loops (NFBL) and Negative Feed-Forward Loops (NFFL).

Andrews and Henry 1997 introduced the modified decision table method combining the decision table method due to its ability to identify the normal state of systems and the digraph method due to its ability to detect, classify and analyse control loops. The classification and analysis of control loops is accomplished by using two new circuit operators, one for tracing current and the other for tracing no current in circuits. These operators provide a more efficient FT development in terms of its logical consistency since the operators can significantly reduce the size of trees by eliminating repeated events. The modified decision tables include the inputs, outputs and states of the components as traditional decision tables.

AltaRica, a high-level formal description modelling language, first created at the Computer Science Laboratory of Bordeaux (LaBRI) by Point and Rauzy in 1999, is dedicated to safety analysis. A second version of this language, AltaRica Data-flow (ADF) (Rauzy, 2002; Boiteau et al., 2006), was developed to handle industrial scale models. This second version was improved in 2013 and AltaRica 3.0 was developed. In 2013, Prosvirnova and Rauzy proposed a method that compiles a mathematical model, the Guarded Transition System (GTS), which supports the representation of

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components with bidirectional flows, into the description of a system model using the AltaRica syntax in order to automatically generate FTs for systems containing control loops. GTS is a state/transition formalism that uses concepts from various reliability modes such as Reliability Block Diagrams, Markov chains and PNs. This formalism provides higher efficiency to the system making it possible to design acausal components, i.e. components for which the input and output flows are decided at run time, and to handle control loops in the system. The GTS formalism enables the generation of FTs by transforming the states/transitions of the behavioural model into a set of Boolean formulae and also enhances the reusability of FTs and facilitates their maintenance since it is considered a high level structure.

Papadopoulos et al. (2001) developed the Hierarchical Performed Hazard Origin and Propagation Studies (HiP-HOPS) tool that performs an automated reliability analysis. The main idea of the HiP-HOPS is the automatic synthesis of FTs and Failure Modes and Effects Analyses (FMEAs) using fast linear-time algorithms. Adachi et al. (2011) integrated system modelling, automated dependability (reliability, availability, safety maintainability and security) and optimization techniques using HiP-HOPS, rendering this tool applicable to highly interactive and dynamic systems. This work overcomes limitations such as difficulties in conducting automatic analysis of complex systems with multiple failure modes, dealing with the assumption of the previous work that the system behaviour remains stable over time. HiP-HOPS, commercialised in 2012 by University of Hull, has been used in several automotive and engineering industrial cases. This tool is compatible with a range of modelling notations and offers scalability of the analysis and unique capabilities for fault modelling.

Joshi et al. (2007) proposed a method in which Static Fault Tree (SFT) models are developed, taking as input AADL models. In this work, an AADL model that captures the architectural aspects of a system such as the properties of the components, features of the interactions between components, internal structure of components such as subcomponents’ connections and properties, is used as an input and an Error Model

Annex that captures the component faults and failure modes is manually developed by

the analyst. Using these two models, a Directed Graph (DG), including topology system information faults and failure modes, is created that is then used to generate an

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intermediate FT, by applying a recursive algorithm. Finally, Computer Aided Fault Tree Analysis (CAFTA), a commercial FTA tool, is applied to the intermediate FT and CAFTA FT is generated using software capabilities.