Patrick Lyonnet

A Kalman Optimization Approach for Solving Some Industrial Electronics Problems

This paper is concerned with solving nonconvex optimization problems arising in various engineering sciences. In particular, we focus on the design of a robust flux estimator of induction machines and the optimal design of on-chip spiral inductors. To solve these problems, a recently developed optimization method, called the heuristic Kalman algorithm (HKA), is employed. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other stochastic optimization methods, lies in the small number of parameters that need to be set by the user. Based on HKA a simple but effective design strategy for robust flux estimator and on-chip spiral inductors is developed. Numerical studies are conducted to demonstrate the validity of the proposed design procedure.

Robustness analysis and synthesis of a multi-PID controller based on an uncertain multimodel representation

This paper presents an effective method for robustness analysis and synthesis of a multi-PID controller for non-linear systems where desirable robustness and performance properties must be maintained across a large range of operating conditions. The robustness analysis problem is solved using an uncertain multimodel of the original non-linear system. The model of uncertainties used is an interval matrix modeled by a stochastic matrix which gives poor conservatism in the analysis of stability robustness. Moreover, the robust stability margin is interpreted as a smallest interval matrix that causes instability. This stability margin is evaluated using a random search algorithm. Simulation studies are used to demonstrate the effectiveness of the proposed method.

A methodological approach ball bearing damage prediction under fretting wear conditions.

The industrial demand for higher reliability of various components is one of the main flywheels of the research and development in the field of modelling of complex phenomena. There is a need to characterize the wear behaviour of the interface under fretting wear conditions in ball bearing application. Pre-treated experimental data was used to determine the wear of contacting surfaces as a criterion of damage that can be useful for a life-time prediction. The benefit of acquired knowledge can be crucial for the industrial expert systems and the scientific feature extraction that cannot be underestimated. Wear is a very complex and partially-formalized phenomenon involving numerous parameters and damage mechanisms. To correlate the working conditions with the state of contacting bodies and to define damage mechanisms different techniques are used. The use of our approaches in the prediction of the response of the system to different test conditions is validated. Two physical models, based on Archard and Energetic approach, are compared with artificial neural network model and genetic programming. Decisive factors for a comparison of used AI techniques are their: performance, generalization capabilities, complexity and time-consumption. Optimization of the structure of the model is done to reach high robustness of field applications. Finally, application of the wear level information to forecast a probability of damage is presented.

Parameterization of a fuzzy classifier for the diagnosis of an industrial process

Abstract The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method, which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization, which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a DC motor showing the utility of this method. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system (FDS) is not, in general, directly available. It must be extracted from an often-considerable mass of information. For this reason, a general methodology for the design of a FDS is presented and illustrated on a non-linear plant.

Mixed H2/H∞ residual generator design via Heuristic Kalman Algorithm

Abstract This paper presents a simple but effective synthesis strategy for observers based faults detection in linear time-invariant (LTI) systems which are simultaneously affected by two classes of unknown inputs: Noises having fixed spectral densities and unknown finite energy disturbances. The problem of designing such an observer, also called a residual generator, is formulated as a mixed H 2 / H ∞ optimization problem. This is done to obtain an optimal residual generator, i.e. with minimal sensitivity to unknown inputs. Unfortunately, there is no known solution to this difficult optimization problem. Finding such a residual generator is known to be computationally intractable via the conventional techniques. This is mainly due to the non-convexity of the resulting optimization problem. To solve this kind of problem easily and directly, without using any complicated mathematical manipulations, we utilize the Heuristic Kalman Algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. A numerical example is given to illustrate the advantage of the mixed H 2 / H ∞ optimization approach against techniques based on optimization of H 2 or H ∞ criteria.

Dynamic Preventive Maintenance, Optimization of Time between Overhaul

We present in this paper an method for evaluating the reliability in real time applied to the optimization of preventive maintenance and evaluation of the parameters of dependability. This approach is based on a function Z (t), which assesses the damage from the history of real operating conditions. This assessment is used to calculate the residual reliability, and can then be used to optimize the preventive maintenance and in particular optimize the time between overhaul (TBO). This approach can be used to take more realistic decisions about preventive change and thus led to a better risk management.