Mechanistic-based Design-Integrated Reliability Validation Framework for Mechanical Systems

Abstract: New product design challenges, related to customer needs, product usage and environments, face companies when they expand their product offerings to new markets; Some of the main challenges are: the lack of quantifiable information, product experience and field data. Designing reliable products under such challenges requires flexible reliability assessment processes that can capture the variables and parameters affecting the product overall reliability and allow different design scenarios to be assessed. These challenges also suggest a mechanistic (Physics of Failure-PoF) reliability approach would be a suitable framework to be used for reliability assessment. Mechanistic Reliability recognizes the primary factors affecting design reliability. This research views the designed entity as a “system of components required to deliver specific operations”; it addresses the above mentioned challenges by; Firstly: developing a design synthesis that allows a descriptive operations/ system components relationships to be realized; Secondly: developing component’s mathematical damage models that evaluate components Time to Failure (TTF) distributions given: 1) the descriptive design model, 2) customer usage knowledge and 3) design material properties; Lastly: developing a procedure that integrates components’ damage models to assess the mechanical system’s reliability over time. Analytical and numerical simulation models were developed to capture the relationships between operations and components, the mathematical damage models and the assessment of system’s reliability. The process was able to affect the design form during the conceptual design phase by providing stress goals to meet component’s reliability target. The process was able to numerically assess the reliability of a system based on component’s mechanistic TTF distributions, besides affecting the design of the component during the design embodiment phase. The process was used to assess the reliability of an internal combustion engine manifold during design phase; results were compared to reliability field data and found to produce conservative reliability results. The research focused on mechanical systems, affected by independent mechanical failure mechanisms that are influenced by the design process. Assembly and manufacturing stresses and defects’ influences are not a focus of this research.

The AGILE policy expression language for autonomic systems

This paper presents the AGILE policy expression language. The language enables powerful expression of self-managing behaviours and facilitates policy-based autonomic computing in which the policies themselves can be adapted dynamically and automatically. The language is generic so as to be deployable across a wide spectrum of application domains, and is very flexible through the use of simple yet expressive syntax and semantics. The development of AGILE is motivated by the need for adaptive policy mechanisms that are easy to deploy into legacy code and can be used by non autonomics-expert practitioners to embed self-managing behaviours with low cost and risk. A library implementation of the policy language is described. The implementation extends the state of the art in policy-based autonomics through innovations which include support for multiple policy versions of a given policy type, multiple configuration templates, and higher-level ‘meta-policies’ to dynamically select between differently configured business-logic policy instances and templates. Two dissimilar example deployment scenarios are examined.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Identification and control of Hammerstein systems via wireless networks

This paper investigates a systematic approach for the identification and control of Hammerstein systems over a physical IEEE 802.11b wireless channel.

Condition-Based Diagnosis of Mechatronic Systems Using a Fractional Calculus Approach

While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model’s complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

Modelling the growth and movement of cyanobacteria in river systems

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting the location and timing of the bloom events in lakes and rivers. In this article, a new deterministic model is introduced which simulates the growth and movement of cyanobacterial blooms in river systems. The model focuses on the mathematical description of the bloom formation, vertical migration and lateral transport of colonies within river environments by taking into account the four major factors that affect the cyanobacterial bloom formation in freshwaters: light, nutrients, temperature and river flow. The model consists of two sub-models: a vertical migration model with respect to growth of cyanobacteria in relation to light, nutrients and temperature; and a hydraulic model to simulate the horizontal movement of the bloom. This article presents the model algorithms and highlights some important model results. The effects of nutrient limitation, varying illumination and river flow characteristics on cyanobacterial movement are simulated. The results indicate that under high light intensities and in nutrient-rich waters colonies sink further as a result of carbohydrate accumulation in the cells. In turbulent environments, vertical migration is retarded by vertical velocity component generated by turbulent shear stress. (c) 2006 Elsevier B.V. All rights reserved.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Should system dynamics be described as a 'hard' or 'deterministic' systems approach?

This paper explores the criticism that system dynamics is a ‘hard’ or ‘deterministic’ systems approach. This criticism is seen to have four interpretations and each is addressed from the perspectives of social theory and systems science. Firstly, system dynamics is shown to offer not prophecies but Popperian predictions. Secondly, it is shown to involve the view that system structure only partially, not fully, determines human behaviour. Thirdly, the field's assumptions are shown not to constitute a grand content theory—though its structural theory and its attachment to the notion of causality in social systems are acknowledged. Finally, system dynamics is shown to be significantly different from systems engineering. The paper concludes that such confusions have arisen partially because of limited communication at the theoretical level from within the system dynamics community but also because of imperfect command of the available literature on the part of external commentators. Improved communication on theoretical issues is encouraged, though it is observed that system dynamics will continue to justify its assumptions primarily from the point of view of practical problem solving. The answer to the question in the paper's title is therefore: on balance, no.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

Changing the future of obesity : science, policy, and action

The global obesity epidemic has been escalating for four decades, yet sustained prevention efforts have barely begun. An emerging science that uses quantitative models has provided key insights into the dynamics of this epidemic, and enabled researchers to combine evidence and to calculate the effect of behaviours, interventions, and policies at several levels—from individual to population. Forecasts suggest that high rates of obesity will affect future population health and economics. Energy gap models have quantified the association of changes in energy intake and expenditure with weight change, and have documented the effect of higher intake on obesity prevalence. Empirical evidence that shows interventions are effective is limited but expanding. We identify several cost-effective policies that governments should prioritise for implementation. Systems science provides a framework for organising the complexity of forces driving the obesity epidemic and has important implications for policy makers. Many parties (such as governments, international organisations, the private sector, and civil society) need to contribute complementary actions in a coordinated approach. Priority actions include policies to improve the food and built environments, cross-cutting actions (such as leadership, healthy public policies, and monitoring), and much greater funding for prevention programmes. Increased investment in population obesity monitoring would improve the accuracy of forecasts and evaluations. The integration of actions within existing systems into both health and non-health sectors (trade, agriculture, transport, urban planning, and development) can greatly increase the influence and sustainability of policies. We call for a sustained worldwide effort to monitor, prevent, and control obesity.

Factories of the future : a system of systems engineering perspective

Factories of the Future will be distinguished by intelligent machines, automation, human factors integration and knowledge management. Modelling and simulation is recognised as a key enabling technology essential to economic, social and environmental sustainability of future manufacturing systems. This talk will explore the history, recent achievements and directions in modelling and simulation for 21st century factories and supply chains. A systems science approach is employed, from stakeholder engagement through participative modelling to self-tuning and self-assembling simulations. Our contributions lower the cost of the application of modelling and simulation to manufacturing processes, enabling real time planning, dynamic risk analysis, dashboards and 3D visualisation. This realisation of the virtual factory integrates human factors and decisions into the core technology platform. The implications to future manufacturing enterprises are explored through a series of case studies from aerospace, mining and small and medium manufacturing enterprises.