Development of FRAM Model Based on Structure of Complex Adaptive Systems to Visualize Safety of Socio-Technical Systems

FRAM: Functional Resonance Analysis Method is an effective way to know about the safety of complex Socio-Technical Systems. However, it is a method rather than model and required to be extended for its practical usage. According to this, in this paper, a latest version of FRAM simulator based on our developed model is presented. The model simulates a process in which variabilities exiting in a working environment induce variability of FRAM functions that emerge out of the dynamic interactions among the functions as well as with the environment. Moreover, the model simulates a process where a specific context composed of variabilities existing in a working environment "shakes" FRAM functions, while the context is "shaken" by those functions vice versa, which is a typical dynamics specific to complex adaptive systems. This is implemented by integrating FRAM and Fuzzy CREAM which is an extended model of CREAM: Cognitive Reliability and Error Analysis Method with fuzzy reasoning. It enables to parameterize the variabilities, define the context, and formulate their interactions quantitatively, whose result is given as a dynamical change of state in each FRAM function

Reliability Evaluation of Manufacturing Systems: Methods and Applications

The measurement and optimization of the efficiency level of a manufacturing system, and in general of a complex systems, is a very critical challenge, due to technical difficulties and to the significant impact towards the economic performance. Production costs, maintenance costs, spare parts management costs force companies to analyse in a systematic and effective manner the performance of their manufacturing systems in term of availability and reliability (Manzini et al. 2004, 2006, 2008). The reliability analysis of the critical components is the basic way to establish first and to improve after the efficiency of complex systems. A number of methods (i.e. Direct Method, Rank Method, Product Limit Estimator, Maximum likelihood Estimation, and others (Manzini et. Al., 2009) all with reference to RAMS (Reliability, Availability, Maintainability and Safety) analysis, have been developed, and can bring a significant contribution to the performance improvement of both industrial and non-industrial complex systems. Literature includes a huge number of interesting methods, linked for example to preventive maintenance models; these models can determine the best frequency of maintenance actions, or the optimization of spare parts consumption or the best management of their operating costs (Regattieri et al., 2005, Manzini et al., 2009). Several studies (Ascher et al..1984, Battini et al., 2009, Louit et al., 2007, Persona et al. 2007) state that often these complex methodologies are applied using false assumptions such as constant failure rates, statistical independence among components, renewal processes and others. This common approach results in poor evaluations of the real reliability performance of components. All subsequent analysis may be compromised by an incorrect initial assessment relating to the failure process. A correct definition of the model describing the failure mode is a very critical issue and requires efforts which are often not sufficiently focused on. In this chapter the author discusses the model selection failure process, from the fundamental initial data collection phase to the consistent methodologies used to estimate the reliability of components, also considering censored data. This chapter introduces the basic analytical models and the statistical methods used to analyze the reliability of systems that constitute the basis for evaluation and prediction of the stochastic failure and repair behavior of complex manufacturing systems, assembled using a variety of components. Consequently, the first part of the chapter presents a general framework for components which describes the procedure for the solution of the complete Failure Process Modeling (FPM) problem, from data collection to final failure modeling, that, in particular, develops the fitting analysis in the renewal process and the contribution of censored data throughout the whole process. The chapter discusses the main methods provided in the proposed framework. Applications, strictly derived from industrial case studies, are presented to show the capability and the usefulness of the framework and methods proposed

Automatic phased mission system reliability model generation

There are many methods for modelling the reliability of systems based on component failure data. This task becomes more complex as systems increase in size, or undertake missions that comprise multiple discrete modes of operation, or phases. Existing techniques require certain levels of expertise in the model generation and calculation processes, meaning that risk and reliability assessments of systems can often be expensive and time-consuming. This is exacerbated as system complexity increases. This thesis presents a novel method which generates reliability models for phasedmission systems, based on Petri nets, from simple input files. The process has been automated with a piece of software designed for engineers with little or no experience in the field of risk and reliability. The software can generate models for both repairable and non-repairable systems, allowing redundant components and maintenance cycles to be included in the model. Further, the software includes a simulator for the generated models. This allows a user with simple input files to perform automatic model generation and simulation with a single piece of software, yielding detailed failure data on components, phases, missions and the overall system. A system can also be simulated across multiple consecutive missions. To assess performance, the software is compared with an analytical approach and found to match within 5% in both the repairable and non-repairable cases. The software documented in this thesis could serve as an aid to engineers designing new systems to validate the reliability of the system. This would not require specialist consultants or additional software, ensuring that the analysis provides results in a timely and cost-effective manner

DESIGN AND DEVELOPMENT OF A RELIABILITY ANALYSIS TOOL BASED ON MULTILEVEL FLOW MODELS

ABSTRACT This paper proposes a universal graphical tool for the modeling and reliability analysis of complex industrial process system based on Multilevel Flow Models (MFM). An Extensible Markup Language (XML) is used for structuring the MFM model. An editor is developed and an executor can implement reliability analysis in terms of the established MFM models. The proposed reliability analysis tool is meaningful for further research of the MFM based system reliability analysis and will be useful for more practical applications such as online risk monitoring by integrating the existed algorithms of alarm analysis and fault diagnosis based on MFM. INTRODUCTION Reliability analysis is the key to effective, reliable and safe design and operation of nuclear power system. Several system reliability analysis techniques have been proposed and commonly used to characterize the probabilistic behavior of nuclear power plant. Among these, the well-known and the one extensively employed is Fault Tree Analysis (FTA). However, the main limitation of FTA is the difficulty of handing the problems of systems with multiple states and /or timesequential signals. In addition, a fault tree would become too large and too complex and different analysts may use different representations, which cause the difficulties in building, validating and modifying fault tree logic models. Multilevel Flow Models is a new and promising system modeling method which is developed by Lin

Probabilistic Reliability Analysis of the Water-energy Nexus Using Monte Carlo Simulation

Nowadays, with the development of science and technologies, our modern society is more and more dependent on the reliable performance of the critical infrastructures. Both water systems and power systems are national critical infrastructure supporting our daily life and the development of economic growth. These two types of systems are highly interconnected and complex networks, which consist of various system elements. Similarly, the core function of water and power system is to deliver satisfactory quality water and power to consumers, and at the same time it should satisfy all the demands at all load points. The reliable performance of these critical infrastructure is becoming more and more important. Therefore, it is very urgent to develop a comprehensive reliability evaluation algorithm to quantify the reliability of these critical systems. When it comes to quantitatively assessing reliability of the facility infrastructure, there is a need to develop a comprehensive method to consider a comprehensive set of variables and uncertainties such as the random failures of mechanical components, the amount of water demands, the power supply reliability, maintenance scheduling, and so forth. The rapidly growing urban population is also a great challenge to the aging drinking water distribution networks. The water facilities are aging and in need of expensive repairs. Therefore, this thesis will aid in making informed decisions on infrastructure repair, maintenance, and staffing planning when the available budgets are limited. This thesis proposes a probabilistic reliability evaluation methodology for water distribution systems considering the impact of power supply reliability based on the sequential Monte Carlo simulation (MCS), which can guide cost-effective preventative measures before system failures. A previously developed C++ software tool is used to help perform the simulation. The probabilistic reliability assessment algorithm can be appropriately applied for both the electric power systems and water distribution system is due to the similar stochastic system nature and modeling manner of the system elements. First, the reliability characteristic of each system component in electric power system can be modeled by a two-state model (i.e., up state and down state). Then, the probability of failure for each component can be calculated and a chronological operating sequence can be further determined based on the sequential Monte Carlo Simulation. Likewise, the reliability models for the water distribution system components can be represented using this method. All these similarities result in the similar reliability assessment procedure. The commonly used deterministic criteria in industrial circles lacked the ability to model and quantify the stochastic nature of system behaviors such as the mechanical failure of system elements. Besides the uncertainties come from water distribution system itself, power supply may also affect the performance of the water distribution network and system reliability. Therefore, the two systems are interactive and physically connected. The purpose of this study is to develop a suitable algorithm to evaluate the water sector and power system as an integrated Water-Energy Nexus (WEN) system. This thesis proposes an integrated, probabilistic reliability evaluation method for the WEN model based on the sequential Monte Carlo Simulation. In the proposed evaluation procedure, both mechanical failures and hydraulic analysis are taken into consideration. Case studies are performed base on a representative water-energy nexus system to demonstrate the effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed probabilistic methodology is appropriate to integrated quantitative reliability modeling and assessment of coupled critical infrastructures (i.e., electrical power networks and water distribution networks) by incorporating the emerging smart grid technologies such as electrical microgrids

Development of novel methods for municipal water main infrastructure integrity management

Water Distribution Network (WDN) is an important component of municipal infrastructure. Many municipal water distribution systems are exposed to harsh environment and subjected to corrosion with age. Many of the water mains in North America are close to or have exceeded their design life and are experiencing a number of issues associated with leaks and breakage of the water mains. Maintaining structural integrity of the water infrastructure with the limited municipal budget has been a challenge. Under this circumstance, the municipalities are focusing on prioritizing their infrastructure for maintenance with optimum utilization of the resources. In this regard, an effective method for prioritizing is required for optimally maintaining the infrastructure integrity. The proposed research focuses on developing risk/reliability based prioritizing methods for water main infrastructure maintenance. Historic water main break data (i.e. number of breaks per km) is often used to identify breakage patterns in the attempts to reliability assessments of deteriorating water mains. This statistical modelling approach is unable to identify the failure mechanism and have limited use. Physical/mechanistic models are therefore desired for better understanding of the failure mechanisms and reliability assessment of WDN. In the proposed research, mechanics-based model is developed for the reliability assessment of water mains. Existing models for remaining strength assessment of the deteriorating pipelines are first examined to develop improved models. Pipe stress analysis is then performed for the reliability assessment of the pipes based on a stochastic analysis using Monte Carlo simulation. For prioritizing water mains, system reliability and risk assessment methods are employed. For small WDN, the system failure of the pipeline network is modeled using Fault-Tree Analysis (FTA). The FTA is however tedious for large complex network. For large WDN, a complex network analysis method is employed to determine the potential of network disconnection due to water main break. Algebraic Connectivity (AC) of a complex network analysis is found to effectively represent the robustness and redundancy of WDN. The fluctuation in AC due to water main break could be used to assess the criticality of each pipe segment to the overall structure of the network. The AC then used as a part of overall consequence of the network due to water main breaks. A Fuzzy Inference System is proposed to combine network consequence with other consequence for risk assessment of complex WDN. In summary, a novel risk/reliability-based method for maintenance of water distribution system is developed in this thesis. In developing this method, mechanics-based failure is considered for reliability assessment and AC from graph theory is used for the consequence assessment of water main break on the overall network. A framework is developed for risk assessment considering the reliability and various consequences

Enhancing the performance of automated guided vehicles through reliability, operation and maintenance assessment

Automated guided vehicles (AGVs), a type of unmanned moving robots that move along fixed routes or are directed by laser navigation systems, are increasingly used in modern society to improve efficiency and lower the cost of production. A fleet of AGVs operate together to form a fully automatic transport system, which is known as an AGV system. To date, their added value in efficiency improvement and cost reduction has been sufficiently explored via conducting in-depth research on route optimisation, system layout configuration, and traffic control. However, their safe application has not received sufficient attention although the failure of AGVs may significantly impact the operation and efficiency of the entire system. This issue becomes more markable today particularly in the light of the fact that the size of AGV systems is becoming much larger and their operating environment is becoming more complex than ever before. This motivates the research into AGV reliability, availability and maintenance issues in this thesis, which aims to answer the following four fundamental questions: (1) How could AGVs fail? (2) How is the reliability of individual AGVs in the system assessed? (3) How does a failed AGV affect the operation of the other AGVs and the performance of the whole system? (4) How can an optimal maintenance strategy for AGV systems be achieved? In order to answer these questions, the method for identifying the critical subsystems and actions of AGVs is studied first in this thesis. Then based on the research results, mathematical models are developed in Python to simulate AGV systems and assess their performance in different scenarios. In the research of this thesis, Failure Mode, Effects and Criticality Analysis (FMECA) was adopted first to analyse the failure modes and effects of individual AGV subsystems. The interactions of these subsystems were studied via performing Fault Tree Analysis (FTA). Then, a mathematical model was developed to simulate the operation of a single AGV with the aid of Petri Nets (PNs). Since most existing AGV systems in modern industries and warehouses consist of multiple AGVs that operate synchronously to perform specific tasks, it is necessary to investigate the interactions between different AGVs in the same system. To facilitate the research of multi-AGV systems, the model of a three-AGV system with unidirectional paths was considered. In the model, an advanced concept PN, namely Coloured Petri Net (CPN), was creatively used to describe the movements of the AGVs. Attributing to the application of CPN, not only the movements of the AGVs but also the various operation and maintenance activities of the AGV systems (for example, item delivery, corrective maintenance, periodic maintenance, etc.) can be readily simulated. Such a unique technique provides us with an effective tool to investigate larger-scale AGV systems. To investigate the reliability, efficiency and maintenance of dynamic AGV systems which consist of multiple single-load and multi-load AGVs traveling along different bidirectional routes in different missions, an AGV system consisting of 9 stations was simulated using the CPN methods. Moreover, the automatic recycling of failed AGVs is studied as well in order to further reduce human participation in the operation of AGV systems. Finally, the simulation results were used to optimise the design, operation and maintenance of multi-AGV systems with the consideration of the throughputs and corresponding costs of them.The research reported in this thesis contributes to the design, reliability, operation, and maintenance of large-scale AGV systems in the modern and rapidly changing world.</div

Advanced Techniques for Assets Maintenance Management

16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018 Bergamo, Italy, 11–13 June 2018. Edited by Marco Macchi, László Monostori, Roberto PintoThe aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry