Frédéric Kratz

Finite Memory Observer for State Estimation of Hybrid System

Abstract Both industry and the academic world are becoming more and more interested in techniques to model, analyze, control and supervise complex systems that contain both analog and logical components. Such systems are called hybrid systems. This paper presents extensions and improvements on the finite memory observer. The main contribution is to incorporate the parity relation design and observer into a diagnosis scheme. The application of the finite memory observer to the sensor fault detection problem is illustrated by a numerical example. Simulation results show that the method is very effective for robust fault detection.

Use of HMM for evaluation of maintenance activities

This paper deals with a tool which may help maintenance managers to schedule maintenance activities. To help them, we show that by using events which can be observed on a process, like maintenance events, we can predict failures before they occur. Principles are based on hypothesis that failure is preceded by a typical sequence of events. We also show that hidden Markov models can be used according to the right choice of parameters.

Bilateral Teleoperation System Using QoS and Secure Communication Networks for Telemedicine Applications

The next generation of telesurgical robotics systems presents significant challenges related to network performance and data security. It is known that packet transmission in wide area networks is a complex stochastic process; thus, low bandwidth as well as high delay, jitter, and packet loss will greatly affect the quality of service (QoS) of teleoperation control, which is unacceptable in this kind of sensitive application. Furthermore, a relevant but more serious issue is the network attacks, particularly denial of service as well as data alteration or disclosure. The main motivation of this study is to deploy suitable security mechanisms while preserving the QoS of network-based bilateral teleoperation. We propose and apply a protocol that secures our teleoperation system while preserving its real-time constraints. More precisely, we present in this paper a bilateral generalized predictive controller coupled to a QoS-friendly IP security protocol. The experimental results demonstrate that the telerobotic system is able to satisfy both QoS and security requirements of real-time and sensitive teleoperation tasks. In fact, our teleoperation security protocol provides priority treatment while preventing attacks and avoiding potential deadline misses due to increased security cost.

FAULT DETECTION OF A NONLINEAR SWITCHING SYSTEM USING FINITE MEMORY OBSERVERS

Abstract This paper is devoted to the design of a fault detection strategy for nonlinear switching discrete-time systems using Finite Memory Observers (FMO). Using both the past input/output measurements and the model equations, state estimation is expressed as a minimization problem of a cost function. Gauss-Newtons optimization method is investigated in a diagnosis purpose. Performances of FMO observers for residual generation on hybrid systems will be illustrated through numerical simulations on a benchmark two tank system.

Fault detection of uncertain models using polytope projection

In this paper, a fault detection procedure for linear MIMO models with respect to the observations and the parameters is studied. The various undesirable phenomena being able to affect any physical system (parameter fluctuations, noise,..) are taken into account by describing them as uncertainties. Thus a fault detection procedure is established by providing a more robust decision with respect to the various imperfections. A more general case consists in using polytopes instead of the parallelotope, is treated in order to bring a better precision, by exploiting the Fourier- Motzkin elimination algorithm which is used to compute the projection of a polytope. Copyright

Hidden Markov model framework for industrial maintenance activities

This article deals with modelization of industrial process by using hidden Markov model. The process is seen as a discrete event system. We propose different structures based on Markov automata, called topologies. A synthetic hidden Markov model is designed in order to match to a real industrial process. The models are intended to decode industrial maintenance observations (also called “symbol”). Symbols are produced with a corresponding degradation level (also called “state”). These 2-tuple (symbol, state) are known as Markov chains, also called “a signature.” Hence, these various 2-tuple are implemented in the proposed topologies by using the Baum–Welch learning algorithm (decoding by forward variable) and the segmental K-means learning (decoding by Viterbi). We assess different frameworks (topology, learning and decoding algorithm, distribution) by relevancy measurements on model outputs. Then, we determine the most relevant framework for use in maintenance activities. Afterward, we try to minimize the size of the learning data. Thus, we could evaluate the model by using “sliding windows” of data. Finally, an industrial application is developed and compared with this framework. Our goal is to improve worker safety, maintenance policy, process reliability and reduce CO2 emissions in the industrial sector.

Towards a Maintenance and Servicing Indicator

This paper deals with a tool which may help maintenance manager to schedule maintenance activities. To help him, we show that by using events which can be observed on a process, like maintenance events, we can predict failures before they occur. Principles are based on the hypothesis that failure is preceded by a typical sequence of events. We also show that Hidden Markov Models can be used according to a good choice of parameters.

Diagnosis of Diesel Injection Systems Using Finite Memory Observers

Abstract Generally, faults are easily detected in linear systems. There exist a large number of proven methods for linear systems resolution. On the other hand, methods in non-linear cases are much more difficult to find and apply only to a limited number of situations. In a great majority of cases, the system is transposable to a piecewise linear structure and the fault detection can be done using linear system methods. The approach under study uses Finite Memory Observers (FMO) to detect sensor faults and actuator faults.