Accommodating repair actions into gas turbine prognostics

Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations, leading to significant uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy. In this paper, we adopt Bayesian on-line change point detection to detect the compressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life. To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compressor washing events for a gas turbine and thus provide demonstrably improved prognosis

Reliability and performance evaluation of systems containing embedded rule-based expert systems

A method for evaluating the reliability of real-time systems containing embedded rule-based expert systems is proposed and investigated. It is a three stage technique that addresses the impact of knowledge-base uncertainties on the performance of expert systems. In the first stage, a Markov reliability model of the system is developed which identifies the key performance parameters of the expert system. In the second stage, the evaluation method is used to determine the values of the expert system's key performance parameters. The performance parameters can be evaluated directly by using a probabilistic model of uncertainties in the knowledge-base or by using sensitivity analyses. In the third and final state, the performance parameters of the expert system are combined with performance parameters for other system components and subsystems to evaluate the reliability and performance of the complete system. The evaluation method is demonstrated in the context of a simple expert system used to supervise the performances of an FDI algorithm associated with an aircraft longitudinal flight-control system

Automatic generation of human machine interface screens from component-based reconfigurable virtual manufacturing cell

Increasing complexity and decreasing time-tomarket require changes in the traditional way of building automation systems. The paper describes a novel approach to automatically generate the Human Machine Interface (HMI) screens for component-based manufacturing cells based on their corresponding virtual models. Manufacturing cells are first prototyped and commissioned within a virtual engineering environment to validate and optimise the control behaviour. A framework for reusing the embedded control information in the virtual models to automatically generate the HMI screens is proposed. Finally, for proof of concept, the proposed solution is implemented and tested on a test rig

Engineering methods and tools for cyber–physical automation systems

Much has been published about potential benefits of the adoption of cyber–physical systems (CPSs) in manufacturing industry. However, less has been said about how such automation systems might be effectively configured and supported through their lifecycles and how application modeling, visualization, and reuse of such systems might be best achieved. It is vitally important to be able to incorporate support for engineering best practice while at the same time exploiting the potential that CPS has to offer in an automation systems setting. This paper considers the industrial context for the engineering of CPS. It reviews engineering approaches that have been proposed or adopted to date including Industry 4.0 and provides examples of engineering methods and tools that are currently available. The paper then focuses on the CPS engineering toolset being developed by the Automation Systems Group (ASG) in the Warwick Manufacturing Group (WMG), University of Warwick, Coventry, U.K. and explains via an industrial case study how such a component-based engineering toolset can support an integrated approach to the virtual and physical engineering of automation systems through their lifecycle via a method that enables multiple vendors' equipment to be effectively integrated and provides support for the specification, validation, and use of such systems across the supply chain, e.g., between end users and system integrators

Report of the panel on geopotential fields: Magnetic field, section 9

The objective of the NASA Geodynamics program for magnetic field measurements is to study the physical state, processes and evolution of the Earth and its environment via interpretation of measurements of the near Earth magnetic field in conjunction with other geophysical data. The fields measured derive from sources in the core, the lithosphere, the ionosphere, and the magnetosphere. Panel recommendations include initiation of multi-decade long continuous scalar and vector measurements of the Earth's magnetic field by launching a five year satellite mission to measure the field to about 1 nT accuracy, improvement of our resolution of the lithographic component of the field by developing a low altitude satellite mission, and support of theoretical studies and continuing analysis of data to better understand the source physics and improve the modeling capabilities for different source regions

CATHEDRAL: A Fast and Effective Algorithm to Predict Folds and Domain Boundaries from Multidomain Protein Structures

We present CATHEDRAL, an iterative protocol for determining the location of previously observed protein folds in novel multidomain protein structures. CATHEDRAL builds on the features of a fast secondary-structure–based method (using graph theory) to locate known folds within a multidomain context and a residue-based, double-dynamic programming algorithm, which is used to align members of the target fold groups against the query protein structure to identify the closest relative and assign domain boundaries. To increase the fidelity of the assignments, a support vector machine is used to provide an optimal scoring scheme. Once a domain is verified, it is excised, and the search protocol is repeated in an iterative fashion until all recognisable domains have been identified. We have performed an initial benchmark of CATHEDRAL against other publicly available structure comparison methods using a consensus dataset of domains derived from the CATH and SCOP domain classifications. CATHEDRAL shows superior performance in fold recognition and alignment accuracy when compared with many equivalent methods. If a novel multidomain structure contains a known fold, CATHEDRAL will locate it in 90% of cases, with <1% false positives. For nearly 80% of assigned domains in a manually validated test set, the boundaries were correctly delineated within a tolerance of ten residues. For the remaining cases, previously classified domains were very remotely related to the query chain so that embellishments to the core of the fold caused significant differences in domain sizes and manual refinement of the boundaries was necessary. To put this performance in context, a well-established sequence method based on hidden Markov models was only able to detect 65% of domains, with 33% of the subsequent boundaries assigned within ten residues. Since, on average, 50% of newly determined protein structures contain more than one domain unit, and typically 90% or more of these domains are already classified in CATH, CATHEDRAL will considerably facilitate the automation of protein structure classification

Managing for Stakeholders, Stakeholder Utility Functions, and Competitive Advantage

This paper integrates some of the central concepts of stakeholder theory with the literatures on organizational justice and trust to explain firm competitiveness. It provides a detailed explanation of factors that facilitate acquisition of knowledge about stakeholder utility functions. In addition, it offers a knowledge-based analysis of how firms that manage for stakeholders can enjoy sustainable competitive benefits. These explanations provide a strong rationale for including stakeholder theory in the discussion of firm competitiveness and performance

From facial mimicry to emotional empathy: A role for norepinephrine?

Tendency to mimic others’ emotional facial expressions predicts empathy and may represent a physiological marker of psychopathy. Anatomical connectivity between amygdala, cingulate motor cortex (M3, M4), and facial nucleus demonstrates a potential neuroanatomical substrate for mimicry, though pharmacological influences are largely unknown. Norepinephrine modulation selectively impairs negative emotion recognition, reflecting a potential role in processing empathy-eliciting facial expressions. We examined effects of single doses of propranolol (beta-adrenoceptor blocker) and reboxetine (selective norepinephrine reuptake inhibitor) on automatic facial mimicry of sadness, anger, and happiness, and the relationship between mimicry and empathy. Forty-five healthy volunteers were randomized to 40 mg propranolol or 4 mg reboxetine. Two hours after drug subjects viewed and rated facial expressions of sadness, anger, and happiness, while corrugator, zygomatic, and mentalis EMG were recorded. Trait emotional empathy was measured using the Balanced Emotional Empathy Scale. EMG confirmed emotion-specific mimicry and the relationship between corrugator mimicry and empathy. Norepinephrine modulation did not alter mimicry to any expression or influence the relationship between mimicry and empathy. Corrugator but not zygomaticus mimicry predicts trait empathy, consistent with greater anatomical connectivity between amygdala and M3 coding upper facial muscle representations. Although influencing emotion perception, norepinephrine does not influence emotional facial mimicry or its relationship with trait empathy

Ontology based semantic-predictive model for reconfigurable automation systems

Due to increasing product variety and complexity, capability to support reconfiguration is a key competitiveness indicator for current automation system within large enterprises. Reconfigurable manufacturing systems could efficiently reuse existing knowledge in order to decrease the required skills and design time to launch new products. However, most of the software tools developed to support design of reconfigurable manufacturing system lack integration of product, process and resource knowledge, and the design data is not transferred from domain-specific engineering tools to a collaborative and intelligent platform to capture and reuse design knowledge. The focus of this research study is to enable integrated automation systems design to support a knowledge reuse approach to predict process and resource changes when product requirements change. The proposed methodology is based on a robust semantic-predictive model supported by ontology representations and predictive algorithms for the integration of Product, Process, Resource and Requirement (PPRR) data, so that future automation system changes can be identified at early design stages