A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators

To increase the dependability of complex systems, one solution is to assess their state of health continuously through the monitoring of variables sensitive to potential degradation modes. When computed in an operating environment, these variables, known as health indicators, are subject to many uncertainties. Hence, the stochastic nature of health assessment combined with the lack of data in design stages makes it difficult to evaluate the efficiency of a health indicator before the system enters into service. This paper introduces a method for early validation of health indicators during the design stages of a system development process. This method uses physics-based modeling and uncertainties propagation to create simulated stochastic data. However, because of the large number of parameters defining the model and its computation duration, the necessary runtime for uncertainties propagation is prohibitive. Thus, kriging is used to obtain low computation time estimations of the model outputs. Moreover, sensitivity analysis techniques are performed upstream to determine the hierarchization of the model parameters and to reduce the dimension of the input space. The validation is based on three types of numerical key performance indicators corresponding to the detection, identification and prognostic processes. After having introduced and formalized the framework of uncertain systems modeling and the different performance metrics, the issues of sensitivity analysis and surrogate modeling are addressed. The method is subsequently applied to the validation of a set of health indicators for the monitoring of an aircraft engine's pumping unit

Beurteilung der Resttragfähigkeit von Bauwerken mit Hilfe der Fuzzy-Logik und Entscheidungstheorie

Whereas the design of new structures is almost completely regulated by codes, there are no objective ways for the evaluation of existing facilities. Experts often are not familiar with the new tasks in system identification and try to retrieve at least some information from available documents. They therefore make compromises which, for many stakeholders, are not satisfying. Consequently, this publication presents a more objective and more realistic method for condition assessment. Necessary basics for this task are fracture mechanics combined with computational analysis, methods and techniques for geometry recording and material investigation, ductility and energy dissipation, risk analysis and uncertainty consideration. Present tools for evaluation perform research on how to analytically conceptualize a structure directly from given loads and measured response. Since defects are not necessarily visible or in a direct way detectable, several damage indices are combined and integrated in a model of the real system. Fuzzy-sets are ideally suited to illustrate parametric/data uncertainty and system- or model uncertainty. Trapezoidal membership functions may very well represent the condition state of structural components as function of damage extent or performance. Tthe residual load-bearing capacity can be determined by successively performing analyses in three steps. The "Screening assessment" shall eliminate a large majority of structures from detailed consideration and advise on immediate precautions to save lives and high economic values. Here, the defects have to be explicitly defined and located. If this is impossible, an "approximate evaluation" should follow describing system geometry, material properties and failure modes in detail. Here, a fault-tree helps investigate defaults in a systematic way avoiding random search or negligence of important features or damage indices. In order to inform about the structural system it is deemed essential not only due to its conceptual clarity, but also due to its applicational simplicity. It therefore represents an important prerequisite in condition assessment though special circumstances might require "fur-ther investigations" to consider the actual material parameters and unaccounted reserves due to spatial or other secondary contributions. Here, uncertainties with respect to geometry, material, loading or modeling should in no case be neglected, but explicitly quantified. Postulating a limited set of expected failure modes is not always sufficient, since detectable signature changes are seldom directly attributable and every defect might -together with other unforeseen situations- become decisive. So, a determination of all possible scenarios to consider every imaginable influence would be required. Risk is produced by a combination of various and ill-defined failure modes. Due to the interaction of many variables there is no simple and reliable way to predict which failure mode is dominant. Risk evaluation therefore comprises the estimation of the prognostic factor with respect to undesir-able events, component importance and the expected damage extent.Während die Bemessung von Tragwerken im allgemeinen durch Vorschriften geregelt ist, gibt es für die Zustandsbewertung bestehender Bauwerken noch keine objektiven Richtlinien. Viele Experten sind mit der neuen Problematik (Systemidentifikation anhand von Belastung und daraus entstehender Strukturantwort) noch nicht vertraut und begnügen sich daher mit Kompromißlösungen. Für viele Bauherren ist dies unbefriedigend, weshalb hier eine objektivere und wirklichkeitsnähere Zustandsbewertung vorgestellt wird. Wichtig hierfür sind theoretische Grundlagen der Schadensanalyse, Methoden und Techniken zur Geometrie- und Materialerkundung, Duktilität und Energieabsorption, Risikoanalyse und Beschreibung von Unsicherheiten. Da nicht alle Schäden offensichtlich sind, kombiniert man zur Zeit mehrere Zustandsindikatoren, bereitet die registrierten Daten gezielt auf, und integriert sie vor einer endgültigen Bewertung in ein validiertes Modell. Werden deterministische Nachweismethoden mit probabilstischen kombiniert, lassen sich nur zufällige Fehler problemlos minimieren. Systematische Fehler durch ungenaue Modellierung oder vagem Wissen bleiben jedoch bestehen. Daß Entscheidungsträger mit unsicheren, oft sogar widersprüchlichen Angaben subjektiv urteilen, ist also nicht zu vermeiden. In dieser Arbeit wird gezeigt, wie mit Hilfe eines dreistufigen Bewertungsverfahrens Tragglieder in Qualitätsklassen eingestuft werden können. Abhängig von ihrem mittleren Schadensausmaß, ihrer Strukturbedeutung I (wiederum von ihrem Stellenwert bzw. den Konsequenzen ihrer Schädigung abhängig) und ihrem Prognosefaktor L ergibt sich ihr Versagensrisiko mit. Das Risiko für eine Versagen der Gesamtstruktur wird aus der Topologie ermittelt. Wenn das mittlere Schadensausmaß nicht eindeutig festgelegt werden kann, oder wenn die Material-, Geometrie- oder Lastangaben vage sind, wird im Rahmen "Weitergehender Untersuchungen" ein mathematisches Verfahren basierend auf der Fuzzy-Logik vorgeschlagen. Es filtert auch bei komplexen Ursache-Wirkungsbeziehungen die dominierende Schadensursache heraus und vermeidet, daß mit Unsicherheiten behaftete Parameter für zuverlässige Absolutwerte gehalten werden. Um den mittleren Schadensindex und daraus das Risiko zu berechnen, werden die einzelnen Schadensindizes (je nach Fehlermodus) abhängig von ihrer Bedeutung mit Wichtungsfaktoren belegt,und zusätzlich je nach Art, Bedeutung und Zuverlässigkeit der erhaltenen Information durch Gamma dividiert. Hiermit wurde ein neues Verfahren zur Analyse komplexer Versagensmechanismen vorgestellt, welches nachvollziehbare Schlußfolgerungen ermöglicht

Automatic programming methodologies for electronic hardware fault monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea

Predictive Model for Likelihood of Detecting Chronic Kidney Failure and Disease Using Fuzzy Logic

Fuzzy logic is highly appropriate and valid basis for developing knowledge-based systems in medicine for different tasks and it has been known to produce highly accurate results. Examples of such tasks include syndrome differentiation, likelihood survival for sickle cell anaemia among paediatric patients, diagnosis and optimal selection of medical treatments and real time monitoring of patients. For this paper, a Fuzzy logic-based system is untaken used to provide a comprehensive simulation of a prediction model for determining the likelihood of detecting Chronic Kidney failure/diseases in humans. The Fuzzy-based system uses a 4-tuple record comprising of the following test taken: Blood Urea Test, Urea Clearance Test, Creatinine Clearance test and Estimated Glomerular Filtrate rate (eGFR). Understanding of the test was elicited from a private hospital in Ibadan through the help of an experienced and qualified nurse which also follows same test according to National Kidney Foundation. This knowledge was then used in the developing the simulated and rule-base prediction model using MATLAB software. The paper also follows the 3 major stages of Fuzzy logic. The results of fuzzification of variables, inference, model testing and defuzzification of variables was also presented. This in turn simplifies the complication involved in detecting Chronic Kidney failure/disease using Fuzzy logic based model. Keywords: Fuzzy logic, prediction model, likelihood, chronic kidney disease/failure DOI: 10.7176/JIEA/9-3-04 Publication date:May 31st 201

Smart Detection of Cardiovascular Disease Using Gradient Descent Optimization

The Internet of Medical Things (IoMT) is the networking of health things or equipment that communicate data over the internet without the need for human involvement in the healthcare field. A large quantity of data is collected from numerous sensors in the health field, and it is all transferred and stored on the cloud. This data is growing bigger here all time, and it's becoming increasingly challenging to secure it on the cloud with real-time storage and computing. Data security problem can be addressed with the aid of machine algorithms and fog computing. For data security in IoMT gadgets correspondence in an intelligent fashion, an intelligent encryption algorithm (IEA) is proposed using blockchain technology in cloud based system framework (CBSF). It is applied on patient’s database to provide immutable security, tampering prevention and transaction transparency at the fog layer in IoMT.  The suggested expert system's results indicate that it is suitable for use in for the security. In the fog model, the blockchain technology approach also helps to address latency, centralization, and scalability difficulties

Quantitative maritime security assessment: a 2020 vision

Maritime security assessment is moving towards a proactive risk-based regime. This opens the way for security analysts and managers to explore and exploit flexible and advanced risk modelling and decision-making approaches in maritime transport. In this article, following a review of maritime security risk assessment, a generic quantitative security assessment methodology is developed. Novel mathematical models for security risk analysis and management are outlined and integrated to demonstrate their use in the developed framework. Such approaches may be used to facilitate security risk modelling and decision making in situations where conventional quantitative risk analysis techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in risk and uncertainty modelling technology are suggested with respect to maritime security risk quantification and management

An Optical Machine Vision System for Applications in Cytopathology

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image focusing on problem in Cytopathology. A unique self learning procedure is presented in order to incorporate expert knowledge. The classification method is based on the application of a set of features which includes fractal parameters such as the Lacunarity and Fourier dimension. Thus, the approach includes the characterisation of an object in terms of its fractal properties and texture characteristics. The principal issues associated with object recognition are presented which include the basic model and segmentation algorithms. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and a novel technique for the creation and extraction of information from a membership function considered. The methods discussed and the algorithms developed have a range of applications and in this work, we focus the engineering of a system for automating a Papanicolaou screening test