A novel framework for the reliability modelling of repairable multistate complex mechanical systems considering propagation relationships

Due to the propagation, amplification, and concatenation in a failure process, the reliabilities of repairable multistate complex mechanical systems (RMCMSs) may be affected by a significant fluctuation due to a small exception associated with a reliability indicator. Focused on the problems arising from the lack of propagation relationships among fault modes, functional components, and failure causes in conventional reliability models, a novel framework for reliability modelling is proposed to comprehensively analyse the reliabilities of RMCMSs. First, the reliability models are abstracted as weighted and directed networks with five layers. Second, an improved failure mode and effects analysis (IFMEA) method combined with the D‐number method and VIKOR approach is presented to determine the importance of reliability nodes. Third, a cut set of the reliability model is generated by any exception of a reliability indicator by considering the propagation relationships, and the reliability sensibility index is defined to characterize the fluctuations in system reliability. The effectiveness of the proposed framework is demonstrated in an actual reliability modelling application. As an intuitive method, the proposed framework inherits the advantages of conventional models but overcomes the drawbacks of these existing methods. Therefore, this method can be flexibly and efficiently used in the reliability modelling of RMCMSs. Moreover, the approach provides a foundation for comprehensive and dynamic reliability analysis and the failure mechanism mining of RMCMSs, and it can be used in other engineering applications.     

Enterprise modelling: from early languages to models transformation

During the last 30 years, enterprise modelling has been recognised as an efficient tool to externalise the knowledge of companies in order to understand their operations, to analyse their running and to design new systems from several points of view: functions, processes, decisions, resources and information technology. This paper aims at describing the long evolution of enterprise modelling techniques as well as one of the future challenges of these techniques: the transformation of enterprise models. So, in a first part, the paper describes the evolution of enterprise modelling techniques from the divergence era to the convergence period. In a second time, the paper focuses on the recent advances in the use of enterprise models through model-driven approaches, interoperability problem-solving and simulation, all these advances having the same characteristic to use the transformation of enterprise models.     

The use of neural networks in understanding and predicting oxidation and corrosion behaviour in advanced energy conversion systems

Abstract

Neural networks can be a useful tool to analyse the oxidation and corrosion behaviour of materials at high temperature. Examples are given of the use of neural network models to analyse datasets of material behaviour after exposure to combustion, gasification and steam atmospheres. The use of networks to identify changes in mechanism, additional significant experimental parameters and the onset of spallation is demonstrated.

The limitations of neural network modelling are briefly discussed. Although they can be trained to fit any existing dataset, care must be taken in using the networks to predict a time sequence of events.     

Parameter estimation of an aeroelastic aircraft using neural networks

Application of neural networks to the problem of aerodynamic modelling and parameter estimation for aeroelastic aircraft is addressed. A neural model capable of predicting generalized force and moment coefficients using measured motion and control variables only, without any need for conventional normal elastic variables or their time derivatives, is proposed. Furthermore, it is shown that such a neural model can be used to extract equivalent stability and control derivatives of a flexible aircraft. Results are presented for aircraft with different levels of flexibility to demonstrate the utility of the neural approach for both modelling and estimation of parameters.     

A model based approach to assess the performance of production systems in degraded mode

Nowadays production systems are asked to perform their activities in a high uncertainty environment and to guarantee their performance in this environment. Therefore, they are asked to master risks that are part of their daily activities, to maintain the performance which is considered as their key success factor. Risks may cause serious effects that threaten the production systems and degrade their performance. Nevertheless, we cannot estimate the degradation that a risk may cause to system performance, since risk analysis methods found in the literature do not allow simulating the behaviour of the system in degraded mode. In order to help production systems to assess their performance in risk situations, we propose in this paper a model-based approach that enables assessing the performance of production systems in degraded mode. Our approach is based on function, interaction, structure (FIS) modelling framework that enables modelling complex system and its failures. The resulting model is converted into an executable simulation model based on a new class of Petri Nets (PNs) called predicate-transition, prioritised, synchronous (PTPS) PN. The obtained simulation model is then executed in order to obtain performance indicators in degraded mode. This tool is used during the system design, in order to study the impact of risks on the designed production system performance. It is also used to study an existing production system in order to analyse and optimise its behaviour in degraded mode. In this article, we present our tool and apply it to a special case of production systems which is a hospital sterilisation system.     

Search strategies at the European Patent Office

Prior-art search is a critical step towards determining whether a patent can be granted or not. In 2016, an internal project called Search Workflow Modelling (SWM) was launched at the European Patent Office (EPO) for building a search knowledgebase, which contains a set of models that record not only the current situation on how patent examiners deal with prior-art search (i.e. the as-is models), but also their requirements of being able to do a more efficient and effective search (i.e. the to-be models). We use the Fact-based Modelling (FBM) approach for formalizing search ontologies, which cover a common vocabulary, relations between concepts related to search, and constraints applicable to these relations. We use a hybrid modelling approach of Business Process Modelling Notations and Case Management Model and Notations (BPMN/CMMN) to model search work flows. A patent search strategy typically involves at least one FBM model and at least one BPMN/CMMN model. In this paper, we will illustrate 5 types of existing search strategies (including recursive flow patterns and FBM models for future search features), and future search strategies. The SWM empirical studies in this paper are being put into practice in the ongoing projects concerning search tools at the EPO.     

Subject-orientation as design language for integration across organisational control layers

Recent research and development in production industry reveals a movement to cyber-physical production systems and Internet of things enabled manufacturing. In this context, ways in which enterprise processes are conceptualised and executed are changing. Decentralising production by applying interlinked cyber-physical production resources breaks up the traditional boundaries between different process abstraction layers. Heterogeneous smart devices and processes at all levels in the industrial control need to interact in such systems. In this paper, requirements from the field of Business Process Management towards the vertical integration of business and production processes are derived. With respect to the identified requirements the eligibility of the Subject-oriented Process Management (S-BPM) approach for vertical process integration is depicted. At the core, the paper investigates the applicability of S-BPM to achieve vertical process integration and reports lessons learnt from two industrial application scenarios. The first scenario aims to encode human properties to adapt workplaces to human needs. The second scenario aims towards supporting the execution and tracking of dynamic production processes.     

Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways.

Mathematical models of highly interconnected and multivariate signalling networks provide useful tools to understand these complex systems. However, effective approaches to extracting multivariate regulation information from these models are still lacking. In this study, we propose a data-driven modelling framework to analyse large-scale multivariate datasets generated from mathematical models. We used an ordinary differential equation based model for the Fas apoptotic pathway as an example. The first step in our approach was to cluster simulation outputs generated from models with varied protein initial concentrations. Subsequently, decision tree analysis was applied, in which we used protein concentrations to predict the simulation outcomes. Our results suggest that no single subset of proteins can determine the pathway behaviour. Instead, different subsets of proteins with different concentrations ranges can be important. We also used the resulting decision tree to identify the minimal number of perturbations needed to change pathway behaviours. In conclusion, our framework provides a novel approach to understand the multivariate dependencies among molecules in complex networks, and can potentially be used to identify combinatorial targets for therapeutic interventions.     

Eigenanalysis and reduced-order modelling of unsteady partial cavity flows using the boundary element method

This paper presents an efficient reduced-order modelling approach to predict unsteady behaviour of partial cavity flows (PCROM). The boundary element method (BEM) along with the potential flow is used to analyze unsteady partial cavity flows. Partial cavity flow is modelled based on a new non-iterative approach and the PCROM is based on fluid eigenmodes. To construct fluid eigenmodes the spatial iterative scheme to find cavity extent is removed. The eigenvalue problem of the unsteady flows is defined based on the unknown wake singularities. Eigenanalysis and reduced-order modelling of unsteady flows over a NACA 16-006 section are performed using the PCROM. Numerical examples are presented to demonstrate the accuracy of the proposed method. Comparison between the obtained results of the proposed method and those of other and conventional method indicates that the present algorithm works well with sufficient accuracy. Moreover, it is shown that the PCROM is computationally more efficient than the conventional one for unsteady sheet cavitations analysis on hydrofoils.     

Fractional derivatives and recurrent neural networks in rheological modelling – part I: theory

The aim of this paper was to develop a general approach based on fractional time derivatives and recurrent neural networks to model the rheological behaviour of asphalt materials. The paper focuses on elastic and viscoelastic material characteristics. It consists of two parts. In this first part, the theoretical aspects of modelling are discussed. A brief introduction into the theory of rheological elements based on fractional time derivatives is provided. The fractional differential equation of a general rheological element (base element) is developed from which a huge variety of other rheological elements can be derived, e.g. fractional Newton, Kelvin and standard solid elements. A new approach is presented for solving the fractional differential equations. Artificial neural networks are developed to compute the stress–strain–time behaviour of fractional rheological elements in a numerical efficient way. The approach is tested and verified. The second part of this work will appear later. It will be focused on applications of the new theoretical work to pavement engineering problems.     

Towards the derivation of an integrated process cost-modelling technique for complex manufacturing systems

Cost modelling is used to support business decisions, especially, when the objective is to remain competitive on price and be able to realise outputs at low cost. Many researchers and industrialists have proposed and experimented with different cost-modelling techniques with a view to influencing design and production decisions at an early stage of the development process. This has led to cost-modelling methods which have been broadly classified in this paper as qualitative and quantitative. The paper identifies current best practice cost-modelling techniques and their performance in complex and dynamic manufacturing environments. The review served as a platform to support the recommendation for an integrated cost-modelling methodology. The integrated methodology is based on the strengths of cost engineering, enterprise modelling, system dynamics and discrete event simulation modelling techniques. The method can help in the redesign and re-engineering of products and processes for better cost and value indications; support investment decision analysis; help determine appropriate business and manufacturing paradigms; influence ‘make, buy or outsourcing’ decisions and serve as a key process improvement tool.     

Translating a typical business process modelling language to a web services ontology through lightweight mapping

Bridging the gap between enterprise modelling methods and Semantic Web services is an important yet challenging task. For organisations with business goals, the automation of business processes as Web services is increasingly important, especially with many business transactions taking place within the Web today. Taking one approach to address this problem, a lightweight mapping between Fundamental Business Process Modelling Language (FBPML) and the Web Services Ontology (OWL-S) is outlined. The framework entails a data model translation and a process model translation via the use of ontologies and mapping principles. Several working examples of the process model translations are presented together with the implementation of an automated translator. FBPML constructs and process models that could not be translated to OWL-S equivalents highlight the differences between the languages of the two domains. It also implies that evolving Semantic Web technologies, in particular OWL-S, are not adequate for all service modelling needs and could thus benefit from the more traditional and mature BPM methods. On a more interesting note, this is effectively the first step towards enabling a semantic-based business workflow system     

Fabrication and modelling of the macro-mechanical properties of cross-ply laminated fibre-reinforced polymer composites using artificial neural network

The mechanical behaviour of fibre-reinforced polymer composites (FRPCs) is considered very complex due to many factors such as composition, material type, manufacturing process and end user applications. This article presents the mechanical properties and artificial neural network (ANN) modelling results of cross-ply laminated FRPCs. Twenty composite samples were fabricated by varying the number of layers of carbon fibre and glass fibre as reinforcement and polyphenylene sulphide and high-density polyethylene as matrix. Mechanical properties were measured in terms of flexural modulus, hardness, impact and transverse rupture strength. Multilayer feed-forward backpropagation ANN approach was used to predict the mechanical properties by using material type, composition and number of reinforcement and matrix layers as input variables. From 20 data patterns, 16 were used for network training and remaining 4 were used to test the models. Furthermore, trend analysis was also performed to understand the influence of inputs on developed models. It is evident from the ANN prediction results that there is good correlation between predicted and actual values within acceptable mean absolute error. The outcomes of this research will help to reduce cost and time by eliminating tedious composite property measurements and to fabricate tailored composites meeting application requirements.     

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.     

Distributed supply chain network modelling and simulation: integration of agent-based distributed simulation and improved SCOR model

Agent-based distributed simulation is an efficient methodology for modelling and analysing such complex adaptive systems as dynamic supply chain networks. However, it lacks an acceptable generic standard. Supply chain operations reference (SCOR) model is a cross-functional framework widely accepted as an industry standard. It provides the standard processes, performance metrics, best practices and associated software functionalities for modelling, evaluating and improving supply chain networks. However, it is a static tool. Integration of agent-based distributed simulation and SCOR model can exploit their advantages to form a generic methodology for modelling and simulation of a wide range of supply chain networks. Therefore, this paper proposes a methodology for distributed supply chain network modelling and simulation by means of integration of agent-based distributed simulation and an improved SCOR model. The methodology contains two components: a hierarchical framework for modelling supply chain network based on the improved SCOR model and agent building blocks integrating the standard processes from the SCOR model. The hierarchical framework provides an approach for structure modelling in any level with different granularities based on the improved SCOR model, and allows rapidly mapping a supply chain network into the structure model of a multi-agent system; while agent building blocks are quite useful and convenient to fill the structure model to fulfil its function modelling. With the approach of structure modelling and function filling, not only can the process of agent-based supply chain network modelling be accelerated, but also the built models can be reused and expanded. Because the hierarchical framework is based on the conceptual framework of SCOR model and agent building blocks integrate the standard processes from SCOR model, the proposed methodology is more generic. In addition, the issues of sub-model synchronisation and data distribution management in the agent-based distributed simulation implementation are taken into consideration and the corresponding solutions for these issues are proposed. Finally, an example of a supply chain network is modelled and implemented to illustrate the proposed methodology and related solutions.     

A structured approach to evaluating the impact of implementing a component-based system in the automotive engine manufacturing domain

Component-based (CB) technology applied to the control system of production machinery is one of the new research developments in the automotive manufacturing sector. Although it is important to evaluate the technical aspects of this new paradigm, an appreciation of the impact from the business and human aspects is equally important to the stakeholders in the industry. Current evaluation approaches do not offer a method to capture and analyse the impact of CB technology that is simple to use and produces results that are readily understood by all the interested parties. The definition of an approach for evaluating the business and human aspects of implementing a CB system in the automotive sector is discussed. An evaluation strategy has been formulated comprising (1) knowledge elicitation, (2) investigation of future implementation scenarios, (3) data representation and analysis using enterprise modelling approaches, and (4) simulation and model analysis using proprietary software toolkits.     

An evolutionary algorithm for designing microbial communities via environmental modification

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.