Determination of set-membership identifiability sets

International audienceThis paper concerns the concept of set-membership identifiability introduced in \cite{jauberthie}. Given a model, a set-membership identifiable set is a connected set in the parameter domain of the model such that its corresponding trajectories are distinct to trajectories arising from its complementary. For obtaining the so-called set-membership identifiable sets, we propose an algorithm based on interval analysis tools. The proposed algorithm is decomposed into three parts namely {\it mincing}, {\it evaluating} and {\it regularization} (\cite{jaulin2}). The latter step has been modified in order to obtain guaranteed set-membership identifiable sets. Our algorithm will be tested on two examples

Zonotopic set-membership state estimation for discrete-time descriptor LPV systems

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This technical note proposes a novel set-membership state estimation approach based on zonotopes for discrete-time descriptor linear parameter-varying systems. The consistency test between the system model and measured outputs is implemented to construct a parameterized intersection zonotope with respect to a correction matrix. With a defined zonotope minimization criterion, we propose a novel offline optimization problem to obtain the optimal correction matrix. In addition, with the proposed approach, an adaptive bound of the radius of the intersection zonotope is also provided. Finally, a case study with a truck-trailer system is shown to illustrate the proposed approach.Peer ReviewedPostprint (author's final draft

Architecture for privacy-preserving brokerage of analytics using Multi Party Computation, Self Sovereign Identity and Blockchain

In our increasingly digitized world, the value of data is clear and proved, and many solutions and businesses have been developed to harness it. In particular, personal data (such as health-related data) is highly valuable, but it is also sensitive and could harm the owners if misused. In this context, data marketplaces could enhance the circulation of data and enable new businesses and solutions. However, in the case of personal data, marketplaces would necessarily have to comply with existing regulations, and they would also need to make users privacy protection a priority. In particular, privacy protection has been only partially accomplished by existing datamarkets, as they themselves can gather information about the individuals connected with the datasets they handle. In this thesis is presented an architecture proposal for KRAKEN, a new datamarket that provides privacy guarantees at every step in the data exchange and analytics pipeline. This is accomplished through the use of multi-party computation, blockchain and self-sovereign identity technologies. In addition to that, the thesis presents also a privacy analysis of the entire system. The analysis indicated that KRAKEN is safe from possible data disclosures to the buyers. On the other hand, some potential threats regarding the disclosure of data to the datamarket itself were identified, although posing a low-priority risk, given their rare chance of occurrence. Moreover the author of this thesis elaborated remarks on the decentralisation of the architecture and possible improvements to increase the security. These improvements are accompanied by the solutions identified in the paper that proposes the adoption of a trust measure for the MPC nodes. The work on the paper and the thesis contributed to the personal growth of the author, specifically improving his knowledge of cryptography by learning new schemes such as group signatures, zero knowledge proof of knowledge and multi-party computation. He improved his skills in writing academic papers and in working in a team of researchers leading a research area

Fault Diagnosis & Field Measurement Prediction Techniques for a Gas Metering System

This report discusses on research regarding fault diagnosis system for a process plant. In this project, the process studied is Petronas gas metering system to Kapar Power Plant. There are two parts to this project. The first part is focused on proposing a backup fault diagnosis method for this gas metering system. The second part of the project is to propose suitable field measurement prediction techniques, which could be used in the event of a fault or intermediate condition. In order to achieve the first objective, this report first discusses the potential fault diagnosis methods which can be applied to the metering system. The advantages and disadvantages of each method were evaluated. From evaluation, it was chosen to propose fault diagnosis system using Adaptive Neuro Fuzzy Inference System (ANFIS). In order to carry out fault diagnosis, data is first filtered into fault data and healthy data. The faults filtered in this report include transmitter fault and hang fault for parameters of Temperature, Pressure and Gross Volume. Once healthy data was identified, it was further classified into normal and intermediate categories. This process was done through three different methods, which are the hyperbox model, linear model and ANFIS model. Once these models were analysed, the writer has chosen to proceed with ANFIS model for data classification. Classified data was then grouped into clusters. The second part of the project is focused on proposing suitable field measurement prediction technique using ANFIS that can be used in the event of fault or intermediate conditions. Six different ANFIS models were developed to estimate parameters Temperature, Pressure and Gross Volume during transmitter and hang fault. Five variables such as ANFIS input, data division, number of epoch for training, type of membership function and randomisation of data were varied in order to develop the best model. ANFIS prediction model for Temperature produced satisfactory results of less than 1% error. ANFIS prediction model for Pressure and Gross Volume on the other hand need to be further developed to meet industrial requirements

Model-based fault diagnosis for aerospace systems: a survey

http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

DevOps for Trustworthy Smart IoT Systems

ENACT is a research project funded by the European Commission under its H2020 program. The project consortium consists of twelve industry and research member organisations spread across the whole EU. The overall goal of the ENACT project was to provide a novel set of solutions to enable DevOps in the realm of trustworthy Smart IoT Systems. Smart IoT Systems (SIS) are complex systems involving not only sensors but also actuators with control loops distributed all across the IoT, Edge and Cloud infrastructure. Since smart IoT systems typically operate in a changing and often unpredictable environment, the ability of these systems to continuously evolve and adapt to their new environment is decisive to ensure and increase their trustworthiness, quality and user experience. DevOps has established itself as a software development life-cycle model that encourages developers to continuously bring new features to the system under operation without sacrificing quality. This book reports on the ENACT work to empower the development and operation as well as the continuous and agile evolution of SIS, which is necessary to adapt the system to changes in its environment, such as newly appearing trustworthiness threats

Qualitative Adaptive Identification for Powertrain Systems. Powertrain Dynamic Modelling and Adaptive Identification Algorithms with Identifiability Analysis for Real-Time Monitoring and Detectability Assessment of Physical and Semi-Physical System Parameters

A complete chain of analysis and synthesis system identification tools for detectability assessment and adaptive identification of parameters with physical interpretation that can be found commonly in control-oriented powertrain models is presented. This research is motivated from the fact that future powertrain control and monitoring systems will depend increasingly on physically oriented system models to reduce the complexity of existing control strategies and open the road to new environmentally friendly technologies. At the outset of this study a physics-based control-oriented dynamic model of a complete transient engine testing facility, consisting of a single cylinder engine, an alternating current dynamometer and a coupling shaft unit, is developed to investigate the functional relationships of the inputs, outputs and parameters of the system. Having understood these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended algorithms is illustrated with three novel practical applications. These are, the development of an on-line health monitoring system for engine dynamometer coupling shafts based on recursive estimation of shaft’s physical parameters, the sensitivity analysis and adaptive identification of engine friction parameters, and the non-linear recursive parameter estimation with parameter estimability analysis of physical and semi-physical cyclic engine torque model parameters. The findings of this research suggest that the combination of physics-based control oriented models with adaptive identification algorithms can lead to the development of component-based diagnosis and control strategies. Ultimately, this work contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control for vehicular systems

Fault Diagnosis & Field Measurement Prediction Techniques for a Gas Metering System

This report discusses on research regarding fault diagnosis system for a process plant. In this project, the process studied is Petronas gas metering system to Kapar Power Plant. There are two parts to this project. The first part is focused on proposing a backup fault diagnosis method for this gas metering system. The second part of the project is to propose suitable field measurement prediction techniques, which could be used in the event of a fault or intermediate condition. In order to achieve the first objective, this report first discusses the potential fault diagnosis methods which can be applied to the metering system. The advantages and disadvantages of each method were evaluated. From evaluation, it was chosen to propose fault diagnosis system using Adaptive Neuro Fuzzy Inference System (ANFIS). In order to carry out fault diagnosis, data is first filtered into fault data and healthy data. The faults filtered in this report include transmitter fault and hang fault for parameters of Temperature, Pressure and Gross Volume. Once healthy data was identified, it was further classified into normal and intermediate categories. This process was done through three different methods, which are the hyperbox model, linear model and ANFIS model. Once these models were analysed, the writer has chosen to proceed with ANFIS model for data classification. Classified data was then grouped into clusters. The second part of the project is focused on proposing suitable field measurement prediction technique using ANFIS that can be used in the event of fault or intermediate conditions. Six different ANFIS models were developed to estimate parameters Temperature, Pressure and Gross Volume during transmitter and hang fault. Five variables such as ANFIS input, data division, number of epoch for training, type of membership function and randomisation of data were varied in order to develop the best model. ANFIS prediction model for Temperature produced satisfactory results of less than 1% error. ANFIS prediction model for Pressure and Gross Volume on the other hand need to be further developed to meet industrial requirements