An aggregation method of Markov graphs for the reliability analysis of hybrid systems

To meet always increasing safety requirements in car industry, design and safety assessment methods are developed in order to fit the complexity of new embedded mecatronic systems. Hybrid (discrete/continuous) and dynamic features, specific to these systems, require choosing a suitable formalism. These features should also be considered in safety studies made all through the system design. The aim of this paper is to propose a quantitative analysis method based on the construction of an aggregated Markov graph, which allows a limitation of the combinatorial expansion. This graph is directly deducted from the Petri net modelling of the system. It is composed by a set of functional modes and a set of transitions to which statistical information regarding the system dynamics has been added.     

Reliability models for computer systems: An overview including dataflow graphs

The reliability of a system is the probability that the system will perform its intended mission under given conditions. This paper provides an overview of the approaches to reliability modelling and identifies their strengths and weaknesses. The models discussed include structure models, simple stochastic models and decomposable stochastic models. Ignoring time-dependence, structure models give reliability as a function of the topological structure of the system. Simple stochastic models make direct use of the properties of underlying stochastic processes, while decomposable models consider more complex systems and analyse them through subsystems. Petri nets and dataflow graphs facilitate the analysis of complex systems by providing a convenient framework for reliability analysis.     

Analysis of sequential failures for assessment of reliability and safety of manufacturing systems

Assessment of reliability and safety of a manufacturing system with sequential failures is an important issue in industry, since the reliability and safety of the system depend not only on all failed states of system components, but also on the sequence of occurrences of those failures. Methods that are currently available in sequential failure analysis always start with given sequences of the failures in the system, which is not the case in real life situations; therefore, the sequences of the failures should be identified and the probability of their occurrence should be determined. In this paper, we represent a methodology that can be used for identifying the failure sequences and assessing the probability of their occurrence in a manufacturing system. The method employs Petri net modeling and reachability trees constructed based on the Petri nets. The methodology is demonstrated on an example of an automated machining and assembly system.     

Modelling and simulation of a bottling plant using hybrid Petri nets

In this paper we use First–Order Hybrid Petri nets (FOHPN), an hybrid model that combines fluid and discrete event dynamics, to model the concurrent activities of manufacturing systems. In particular we consider an existing mineral water bottling plant and we show how the FOHPN model is extremely suited to describe the high-throughput production lines, that are one of the main components in the considered plant. Some variations with respect to the previous definition of the FOHPN model are also introduced here to better describe the behavior of the conveyor lines. Finally, we show that, thanks to the fluid approximation of some discrete-event dynamics, the considered plant may also be efficiently simulated using FOHPN. This also enabled us to identify, among the most commonly used configurations of the plant, which are the optimal working conditions in terms of maximal throughput and net profit.     

Automatic creation of Markov models for reliability assessment of safety instrumented systems

After the release of new international functional safety standards like IEC 61508, people care more for the safety and availability of safety instrumented systems. Markov analysis is a powerful and flexible technique to assess the reliability measurements of safety instrumented systems, but it is fallible and time-consuming to create Markov models manually. This paper presents a new technique to automatically create Markov models for reliability assessment of safety instrumented systems. Many safety related factors, such as failure modes, self-diagnostic, restorations, common cause and voting, are included in Markov models. A framework is generated first based on voting, failure modes and self-diagnostic. Then, repairs and common-cause failures are incorporated into the framework to build a complete Markov model. Eventual simplification of Markov models can be done by state merging. Examples given in this paper show how explosively the size of Markov model increases as the system becomes a little more complicated as well as the advancement of automatic creation of Markov models.     

Hybrid Subset Simulation method for reliability estimation of dynamical systems subject to stochastic excitation

A hybrid Subset Simulation approach is proposed for reliability estimation for general dynamical systems subject to stochastic excitation. This new stochastic simulation approach combines the advantages of the two previously proposed Subset Simulation methods, Subset Simulation with Markov Chain Monte Carlo (MCMC) algorithm and Subset Simulation with splitting. The new method employs the MCMC algorithm before reaching an intermediate failure level and splitting after reaching the level to exploit the causality of dynamical systems. The statistical properties of the failure probability estimators are derived. Two examples are presented to demonstrate the effectiveness of the new approach and to compare with the previous two Subset Simulation methods. The results show that the new method is robust to the choice of proposal distribution for the MCMC algorithm and to the intermediate failure events selected for Subset Simulation.     

An efficient hybrid reliability analysis method with random and interval variables

Random and interval variables often coexist. Interval variables make reliability analysis much more computationally intensive. This work develops a new hybrid reliability analysis method so that the probability analysis (PA) loop and interval analysis (IA) loop are decomposed into two separate loops. An efficient PA algorithm is employed, and a new efficient IA method is developed. The new IA method consists of two stages. The first stage is for monotonic limit-state functions. If the limit-state function is not monotonic, the second stage is triggered. In the second stage, the limit-state function is sequentially approximated with a second order form, and the gradient projection method is applied to solve the extreme responses of the limit-state function with respect to the interval variables. The efficiency and accuracy of the proposed method are demonstrated by three examples.     

Deadlock modeling and control of semiconductor track systems using resource-oriented Petri nets

This paper addresses the deadlock avoidance problem in track systems in semiconductor fabrication. For the system without buffer space in it, the existing deadlock avoidance policies tend to be too conservative. Routing flexibility provides a chance to develop better ones, but makes their computation more complex. This paper models a track system using coloured resource-oriented Petri net (CROPN). Based on the model, a sufficient condition for deadlock-free operation and the corresponding control law are presented. This proposed policy is shown computationally efficient and less conservative than existing methods. An example is presented to demonstrate its application.     

New reliability modeling methods for structural systems with hybrid uncertainty

The present study investigates the hybrid reliability modeling of structures in which the inputs contain both random variables and interval variables. Hybrid uncertainty is divided into three categories, including random variables mixed with random variables, interval variables mixed with interval variable, and random variables mixed with interval variables. In order to perform the reliability analysis of structural systems, first, the Bayes method is proposed in the present study to obtain distribution parameters of random variables. Moreover, the self-sample method is introduced to obtain the interval boundaries based on the least available measuring data. Then, the reliability models are established for three situations and the reliability indices are defined and derived accordingly. The abovementioned three types of reliability indices outline the general situation of structural systems. Finally, the specific calculation process is described in details through different examples. Furthermore, the accuracy and efficiency of the proposed method is discussed by comparing the results obtained from the Monte Carlo simulation and those of other methods. The obtained results indicate that the performance of the proposed model in solving reliability modeling problems is better.     

Constructing the closed-form solution of control-related states real-time information for insufficient k-th order systems with one non-sharing resource to realize dynamic modeling large TNCS systems of petri nets

Since the contribution from the closed-form solution (CFS) of the number of control-related states (CRSs) of the variant k-th order system can enumerate the number of each type of CRS in real time, we can apply this real-time information to enhance the capability for dynamic modeling of such systems. For example, allocating a non-sharing waiting dummy resource (known as a deadlock thread-holder [DTH]) in each forbidden substate at that location will increase the maximum number of reachable states that can be derived using a CFS in real time. We can thus avoid the occurrence of deadlock without implementing an additional controller. To extend this capability to model a k-th order system with one non-sharing circle subnet allocated at the top position of the left-hand process (denoted as a top non-sharing circle subnet [TNCS] k-th order system) by Petri net (PN), which is the fundamental manufacturing model for different products sharing common parts, this paper extends the existing research on CFS for PNs to an insufficient k-th order system, which is the essential element for a TNCS system with one non-sharing resource. The proposed deadlock avoidance algorithm can be employed to realize the allocation of dynamic non-sharing processes.     

An integrated IDEF0-3/CTPN/SFC approach for design and analysis of discrete event control systems

Due to its low cost and increased reliability, the programmable logical controller (PLC) plays an important role in industry automation. However, as systems become larger and more complex, efficient and systematic analysis and design of PLCs become a more important issue. In this research, an integrated IDEF0-3/CTPN/SFC (IPS) for developing discrete event control systems (DECSs) is presented. The proposed integrated methodology provides a complete and systematic development process for the DECSs. The development process is divided into four stages: functional analysis, system behaviour analysis, system design, and system implementation. The IDEF0-3, coloured timed Petri net (CTPN) and sequential function chart (SFC) are integrated into the corresponding stages based on their characteristics. In addition, the transformation rules between the IDEF0-3 and CTPN and between CTPN and SFC are also conducted for developing DECSs. The transformation rules provide a straightforward mapping from the IDEF0-3 to CTPN and from the CTPN to SFC. The result of the integrated IPS systematically leads to SFC for PLC implementation. Finally, a ball assorting system is given to illustrate how the integrated IPS approach is implemented for developing the DECSs.     

Petri nets and genetic algorithms for complex manufacturing systems scheduling

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach.     

Computer-aided reliability analysis of fault-tolerant systems

We present an overview of the major problems inherent in reliability modelling of fault-tolerant systems. The problems faced while modelling such systems include the need to consider a very large state space, non-exponential distributions, error analysis, the need to perform a combined evaluation of performance and reliability, and the need to include the details of fault/error handling behaviour. Some of the proposed solutions are discussed and current tools (harp, save, deep andsharpe) to facilitate evaluation of such systems are described. References are provided to many of the important techniques utilized in reliability, availability, and performance modelling of such systems. This research was supported in part by the Air Force Office of Scientific Research under grant AFOSR-84-0132, by the Army Research Office under contract DAAG29-84-0045 and by the National Aeronautics and Space Administration under grant NAG1-70.     

Construction of event-tree/fault-tree models from a Markov approach to dynamic system reliability

While the event-tree (ET)/fault-tree (FT) methodology is the most popular approach to probability risk assessment (PRA), concerns have been raised in the literature regarding its potential limitations in the reliability modeling of dynamic systems. Markov reliability models have the ability to capture the statistical dependencies between failure events that can arise in complex dynamic systems. A methodology is presented that combines Markov modeling with the cell-to-cell mapping technique (CCMT) to construct dynamic ETs/FTs and addresses the concerns with the traditional ET/FT methodology. The approach is demonstrated using a simple water level control system. It is also shown how the generated ETs/FTs can be incorporated into an existing PRA so that only the (sub)systems requiring dynamic methods need to be analyzed using this approach while still leveraging the static model of the rest of the system.     

Modelling and simulation of automated manufacturing systems for evaluation of complex schedules

Automated manufacturing systems have been studied widely in terms of scheduling. As technology evolves, the behaviour of tools in automated manufacturing systems has become complicated. Therefore, mathematical approaches to the analysis of complex schedules no longer reflect reality. In this paper, we propose a systematic way of conducting simulation experiments to evaluate the complex operating schedules of automated manufacturing systems. A simulation model is based on a timed Petri net to take advantage of its mathematical strength. Since a Petri net cannot itself have token firing rules, we introduce additional states called operational states. Operational states are not directly related to a Petri net, and are only used for decision making. In addition, a decision function that is responsible for the conflict resolution of a Petri net model and an operational state transition function are introduced. The parallel simulation concept is also suggested by dividing a Petri net into several independent decision sub-nets. A multi-cluster tool system for semiconductor manufacturing is analysed as an application.     

Evaluation of the logistic model of the reconfigurable manufacturing system based on generalised stochastic Petri nets

To reveal the influence on system performance by the logistic model of reconfigurable manufacturing system (RMS), the generalised stochastic Petri nets (GSPN) modular modelling approach is presented in this paper. It is based upon the characteristics of a bottleneck service. According to this approach, the bottleneck service in the production process is found first. By corresponding different resources in the service to different modules of the GSPN, the module is reconfigured. The analysis of the model using the Markov chain is hereby presented, as is the average utilisation factor of RMS. Following this, the production capacity of different products and the average productivity of reconfigurable manufacturing cells (RMCs) are discussed.     

Scheduling of flexible manufacturing systems based on Petri nets and hybrid heuristic search

This paper proposes and evaluates a hybrid search strategy and its application to flexible manufacturing system (FMS) scheduling in a Petri net framework. Petri nets can concisely model multiple lot sizes for each job, the strict precedence constraint, multiple kinds of resources, and concurrent activities. To cope with the complexities for FMS scheduling, this paper presents a hybrid heuristic search strategy, which combines the heuristic A* strategy with the DF strategy based on the execution of the Petri nets. The search scheme can invoke quicker termination conditions, and the quality of the search result is controllable. To demonstrate this, the scheduling results are derived and evaluated through a simple FMS with multiple lot sizes for each job. The algorithm is also applied to a set of randomly generated more complex FMSs with such characteristics as limited buffer sizes, multiple resources, and alternative routings.     

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm’s performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.