Data-driven extraction and analysis of repairable fault trees from time series data

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event

Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

Reliability assessment refers to the process of evaluating reliability of components or systems during their lifespan or prior to their implementation. In the manufacturing industry, the reliability of systems is directly linked to production efficiency, product quality, energy consumption, and other crucial performance indicators. Therefore, reliability plays a critical role in every aspect of manufacturing. In this review, we provide a comprehensive overview of the most significant advancements and trends in the assessment of manufacturing system reliability. For this, we also consider the three main facets of reliability analysis of cyber–physical systems, i.e., hardware, software, and human-related reliability. Beyond the overview of literature, we derive challenges and opportunities for reliability assessment of manufacturing systems based on the reviewed literature. Identified challenges encompass aspects like failure data availability and quality, fast-paced technological advancements, and the increasing complexity of manufacturing systems. In turn, the opportunities include the potential for integrating various assessment methods, and leveraging data to automate the assessment process and to increase accuracy of derived reliability models

Avaliação da confiança no funcionamento de redes de campo : contribuição no domínio dos sistemas industriais de controlo

Tese de Doutoramento. Engenharia electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Proxel-based simulation of stochastic petri nets

This paper discusses the analysis of stochastic Petri nets using the proxelbased simulation method. The paradigm of the proxel (”probability element”) was recently introduced in order to provide a new algorithmic approach to analysing discrete-state stochastic models such as are represented by stochastic Petri nets (SPNs) or queuing systems. Proxel-based simulation is not related to either of the standard simulation approaches: it is in no way analogous to discrete-event simulation, and, although it is based on the model′s underlying stochastic process and makes use of supplementary variables, it does not require the use of differential equations. Instead, the proxels trace the movement of probability from one state of the model to another using discretised time steps. Since stochastic Petri nets are a powerful and widespread tool for modelling stochastic processes, we are interested in finding out how the proxel-based method applies to them. The analysis of SPNs using the proxel-based method is the subject of this paper, with an emphasis on the treatment of immediate transitions. 1 Goals of the Paper The goal of this paper is to extend the analysis of stochastic Petri nets using the recently introduced proxel-based method. The forma

A NEW PARADIGM FOR THE NUMERICAL SIMULATION OF STOCHASTIC PETRI NETS WITH GENERAL FIRING TIMES

This paper is concerned with the simulation analysis of discrete-state stochastic models such as queueing systems or stochastic Petri nets, in which arbitrary probability distributions may be assigned to the activities. The analysis is performed on the state space using a numerical approach, rather than the usual discrete-event simulation at the model level. A new computational paradigm, the so-called Proxel (probability element) is introduced, which allows an approximation to the continuous stochastic process of the Petri net to be developed which does not require the use of differential equations. This proxel-based computational model directly yields a simulation algorithm which is readily understood and implemented. Simulation experiments are used to illustrate the behaviour of th

The proxel-based method: Formalisation . . .

The proxel-based method is an intuitive approach to analysing discrete stochastic models, such as are described by stochastic Petri nets or queuing systems for example. The approach analyses models in a deterministic manner, avoiding the typical problems of discrete-event simulation (e.g. finding good-quality pseudo-random-number generator) and partial differential equations (difficult to set up and solve). The underlying stochastic process is a discrete-time Markov chain which is constructed on-the-fly by inspecting all possible behaviours of the model. The proxel-based simulation is shown to be very useful in analysing some classes of reliability models and fault-trees. In particular it is more efficient than the discrete-event approach applied to the same models, because the proxel-based method is less sensitive to the stiffness of the models. The goal of the thesis is to formally define this new method, study its behaviour under different circumstances, as well as show that it can be more suitable than some existing methods for certain classes of problems. Further, the thesis examines some of the application areas of the proxel-based method

G.: An experimental study of the behaviour of the proxel-based simulation algorithm

The paradigm of the proxel ("probability element") was recently introduced in order to provide a new algorithmic approach to analysing discrete-state stochastic models such as are represented by stochastic Petri nets or queueing systems. Proxel-based simulation is not related to either of the standard simulation approaches: it is in no way analogous to discrete-event simulation, and, although it is based on the model’s underlying stochastic process, it does not require the use of differential equations. Instead, the proxels dynamically trace the movement of probability from one state of the model to another using discretized time steps. Since the method is new, it still needs to be studied and experiments must be performed in order to fully understand its properties and behaviour. Results of such experiments will be presented and analysed in this paper. 1 Goals of the Paper Proxels are a new method for the simulation of discrete-state stochastic models. The goal of this paper is to present the method, and to complement theoretical research into the proxel-based method by experiments which study its behaviour. When a new algorithm is proposed, its usefulness must be determined, in particular i

Combining Proxels and Discrete Phases

The analysis of discrete stochastic models such as generally distributed stochastic Petri nets can be done by using state spacebased methods. They describe the behavior of a model by a Markov chain that can be solved mathematically. Formerly this approach often had to be discarded as unfeasible due to high memory and runtime costs. The recently developed Proxel-based algorithm is a state space-based simulation method, and has already performed well in several application fields. Experiments suggest, that the selective use of discrete phase approximations could further improve the method, because they can often represent infinite support distribution functions with considerably fewer Markov chain states than proxels. By replacing certain on-the-fly proxel approximations by predetermined phase-type approximations, the total runtime and memory requirement of the simulation method could be drastically reduced for some test models. An efficient algorithm for the approximation of discrete phase-type distributions based on common optimization methods was recently introduced. The formal inclusion of discrete phases into the proxel paradigm is another step toward a practically usable state spacebased simulation method. Our hope is that such a combination of both approaches will lead to a competitive simulation algorithm. 1

Approximation of discrete phase-type distributions

The analysis of discrete stochastic models such as generally distributed stochastic Petri nets can be done using state space-based methods. The behavior of the model is described by a Markov chain that can be solved mathematically. The phase-type distributions that are used to describe non-Markovian distributions have to be approximated. An approach for the fast and accurate approximation of discrete phase-type distributions is presented. This can be a step towards a practical state space-based simulation method, whereas formerly this approach often had to be discarded as unfeasible due to high memory and runtime costs. Discrete phases also fit in well with current research on proxel-based simulation. They can represent infinite support distribution functions with considerably fewer Markov chain states than proxels. Our hope is that such a combination of both approaches will lead to a competitive simulation algorithm. 1