Reliability in the whole life cycle of building systems

Purpose – The purpose of this research is to show that reliability analysis and its implementation will lead to an improved whole life performance of the building systems, and hence their life cycle costs (LCC). Design/methodology/approach – This paper analyses reliability impacts on the whole life cycle of building systems, and reviews the up-to-date approaches adopted in UK construction, based on questionnaires designed to investigate the use of reliability within the industry. Findings – Approaches to reliability design and maintainability design have been introduced from the operating environment level, system structural level and component level, and a scheduled maintenance logic tree is modified based on the model developed by Pride. Different stages of the whole life cycle of building services systems, reliability-associated factors should be considered to ensure the system's whole life performance. It is suggested that data analysis should be applied in reliability design, maintainability design, and maintenance policy development. Originality/value – The paper presents important factors in different stages of the whole life cycle of the systems, and reliability and maintainability design approaches which can be helpful for building services system designers. The survey from the questionnaires provides the designers with understanding of key impacting factors.

Critical reliability issues for building services systems

This paper addresses two critical issues associated with reliability and maintenance of building services systems. The first is the ratio of operating and/or maintenance costs to initial costs for building services systems. It is an important parameter for life cycle costing and maintenance policy development. The second is the proportion of items among building services systems that need preventive maintenance. In this paper, we estimate the ratios based on a cost dataset. It suggests that correctly estimating the ratio be important but using a constant ratio in life cycle costing may result in wrong decisions. It also estimates the proportion of preventive maintenance for building services systems on the basis of the distribution of failure patterns.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Swarms of computational resources and swarms of tasks for building reliable systems

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve autonomy for distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Control of a compliant two-axis robotic arm

It is common to make the links between actuators and robotic limbs as stiff as possible, in complete contrast to natural systems, where compliance is present. In the past, to create some compliance in a drive, springs have been added to the link between the actuator and load. Many of these springs have been in series with the drive, but recently a more 'biological' approach has been taken where two springs have been used in parallel to counteract each other. This paper describes the application of parallel extension springs in a robot arm in order to give it compliance. Advantages and disadvantages of this application are discussed, along with various control strategies.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Cancellation of residual ghost VSB components from video IIR deghoster systems

Certain deghosters suffer from the presence of distortion caused by the quadrature forming nature of the IF VSB filter operating on a ghosted IF signal. By analysing this a priori effect, a specific deghoster solution is found by using the phase quadrature detected IF signal to cancel the VSB induced ghost quadrature distortions from the detected inphase signal before deghosting takes place.

Using modelling tools to optimise the calculation and subsequent reduction of cable distortion parameters with application to oil well logging systems

This paper discusses how the use of computer-based modelling tools has aided the design of a telemetry unit for use with oil well logging. With the aid of modern computer-based simulation techniques, the new design is capable of operating at data rates of 2.5 times faster than previous designs.

Robotic multiwell planar patch-clamp for native and primary mammalian cells

Robotic multiwell planar patch-clamp has become common in drug development and safety programs because it enables efficient and systematic testing of compounds against ion channels during voltage-clamp. It has not, however, been adopted significantly in other important areas of ion channel research, where conventional patch-clamp remains the favored method. Here, we show the wider potential of the multiwell approach with the ability for efficient intracellular solution exchange, describing protocols and success rates for recording from a range of native and primary mammalian cells derived from blood vessels, arthritic joints and the immune and central nervous systems. The protocol involves preparing a suspension of single cells to be dispensed robotically into 4-8 microfluidic chambers each containing a glass chip with a small aperture. Under automated control, giga-seals and whole-cell access are achieved followed by preprogrammed routines of voltage paradigms and fast extracellular or intracellular solution exchange. Recording from 48 chambers usually takes 1-6 h depending on the experimental design and yields 16-33 cell recordings.

A feasible study of EEG-driven assistive robotic system for stroke rehabilitation

Stroke is a medical emergency and can cause a neurological damage, affecting the motor and sensory systems. Harnessing brain plasticity should make it possible to reconstruct the closed loop between the brain and the body, i.e., association of the generation of the motor command with the somatic sensory feedback might enhance motor recovery. In order to aid reconstruction of this loop with a robotic device it is necessary to assist the paretic side of the body at the right moment to achieve simultaneity between motor command and feedback signal to somatic sensory area in brain. To this end, we propose an integrated EEG-driven assistive robotic system for stroke rehabilitation. Depending on the level of motor recovery, it is important to provide adequate stimulation for upper limb motion. Thus, we propose an assist arm incorporating a Magnetic Levitation Joint that can generate a compliant motion due to its levitation and mechanical redundancy. This paper reports on a feasibility study carried out to verify the validity of the robot sensing and on EEG measurements conducted with healthy volunteers while performing a spontaneous arm flexion/extension movement. A characteristic feature was found in the temporal evolution of EEG signal in the single motion prior to executed motion which can aid in coordinating timing of the robotic arm assistance onset.

Introducing "ecoputation": a universal systems design process

A universal systems design process is specified, tested in a case study and evaluated. It links English narratives to numbers using a categorical language framework with mathematical mappings taking the place of conjunctions and numbers. The framework is a ring of English narrative words between 1 (option) and 360 (capital); beyond 360 the ring cycles again to 1. English narratives are shown to correspond to the field of fractional numbers. The process can enable the development, presentation and communication of complex narrative policy information among communities of any scale, on a software implementation known as the "ecoputer". The information is more accessible and comprehensive than that in conventional decision support, because: (1) it is expressed in narrative language; and (2) the narratives are expressed as compounds of words within the framework. Hence option generation is made more effective than in conventional decision support processes including Multiple Criteria Decision Analysis, Life Cycle Assessment and Cost-Benefit Analysis.The case study is of a participatory workshop in UK bioenergy project objectives and criteria, at which attributes were elicited in environmental, economic and social systems. From the attributes, the framework was used to derive consequences at a range of levels of precision; these are compared with the project objectives and criteria as set out in the Case for Support. The design process is to be supported by a social information manipulation, storage and retrieval system for numeric and verbal narratives attached to the "ecoputer". The "ecoputer" will have an integrated verbal and numeric operating system. Novel design source code language will assist the development of narrative policy. The utility of the program, including in the transition to sustainable development and in applications at both community micro-scale and policy macro-scale, is discussed from public, stakeholder, corporate, Governmental and regulatory perspectives.

Effect of feeding intensity and milking system on nutritionally relevant milk components in dairy farming systems in the North East of England

There is increasing concern that the intensification of dairy production reduces the concentrations of nutritionally desirable compounds in milk. This study therefore compared important quality parameters (protein and fatty acid profiles; α-tocopherol and carotenoid concentrations) in milk from four dairy systems with contrasting production intensities (in terms of feeding regimens and milking systems). The concentrations of several nutritionally desirable compounds (β-lactoglobulin, omega-3 fatty acids, omega-3/omega-6 ratio, conjugated linoleic acid c9t11, and/or carotenoids) decreased with increasing feeding intensity (organic outdoor ≥ conventional outdoor ≥ conventional indoors). Milking system intensification (use of robotic milking parlors) had a more limited effect on milk composition, but increased mastitis incidence. Multivariate analyses indicated that differences in milk quality were mainly linked to contrasting feeding regimens and that milking system and breed choice also contributed to differences in milk composition between production systems.