Antoine Picot

Current-Based Detection of Mechanical Unbalance in an Induction Machine Using Spectral Kurtosis With Reference

This paper explores the design, online, of an electrical machines healthy reference by means of statistical tools. The definition of a healthy reference enables the computation of normalized fault indicators whose value is independent of the systems characteristics. This is a great advantage when diagnosing a broad range of systems with different power, coupling, inertia, load, etc. In this paper, an original method called spectral kurtosis with reference is presented to design a systems healthy reference. Its principle is first explained on a synthetic signal. This approach is then evaluated for mechanical unbalance detection in an induction machine using the stator current instantaneous frequency. The normalized behavior of the proposed indicator is then confirmed for different operating conditions, and its robustness with respect to load variations is demonstrated. Finally, the advantages of using a statistical indicator based on a healthy reference compared with a raw fault signature are discussed.

Using retina modelling to characterize blinking: comparison between EOG and video analysis

A large number of car crashes are caused by drowsiness every year. The analysis of eye blinks provides reliable information about drowsiness. This paper proposes to study the relation between electrooculogram (EOG) and video analysis for blink detection and characterization. An original method to detect and characterize blinks from a video analysis is presented here. The method uses different filters based on the human retina modelling. A illumination robust filter is first used to normalize illumination variations of the video input. Then, Outer and an Inner Plexiform Layer filters are used to extract energy signals from eye area. The eye detection is processed mixing gradient and projection methods which makes it able to detect even closed eyes. The illumination robust filter makes it possible to detect the eyes even in night conditions, without using external lighting. The video analysis extracts two signals from the eye area measuring the quantity of static edges and moving edges. Blinks are then detected and characterized from these two signals. A comparison between the features extracted from the EOG and the same features extracted from the video analysis is then performed on a database of 14 different people. This study shows that some blink features extracted from the video are highly correlated with their EOG equivalent: the duration, the duration at 50%, the frequency, the percentage of eye closure at 80% and the amplitude velocity ratio. The frame rate influence on the accuracy of the different features extracted is also studied and enlightens on the need of a high frame rate camera to detect and characterize accurately blinks from a video analysis.

Improvements on lifespan modeling of the insulation of low voltage machines with response surface and analysis of variance

The aim of this paper is to present some improvements of the method for modeling the lifespan of insulation materials in a partial discharge regime. The first step is based on the design of experiments which is well-known for reducing the number experiments and increasing the accuracy of the model. Accelerated aging tests are carried out to determine the lifespan of polyesteimide insulation films under different various stress conditions. A lifespan model is achieved, including an original relationship between the logarithm of the insulation lifespan and that of electrically applied stress and an exponential form of the temperature. The significance of the resulting factor effects are tested through the analysis of variance. Moreover, response surface is helpful to take into account some second order terms in the model and to improve its accuracy. Finally, the model validity is tested with additional points which have not been used for modeling.

On the use of Spectral Kurtosis for diagnosis of electrical machines

This paper explores the efficiency of Spectral Kurtosis (SK) in the area of electrical machines diagnosis. In the literature, Spectral Kurtosis is mainly presented as a tool used to detect non-stationary components in a signal. However, classical use of SK is unsuitable for detection of new stationary components or slow evolutions in a spectrum. In order to detect different types of faults, three indicators are designed from the original definition of the Spectral Kurtosis. These indicators are first tested and compared on synthetic signals. Then, their performance are demonstrated for unbalance detection in a Induction Machine (IM) using current signal.

Experimental comparison between diagnostic indicators for bearing fault detection in synchronous machine by spectral Kurtosis and energy analysis

In this paper, some indicators are developed for efficient detection of bearing defaults in high speed synchronous machines. These indicators are based on the analysis of stator current. As bearing defect signatures can be tracked through amplitude increase of some current harmonics, two specific indicators have been built based on energy considerations and on the Spectral Kurtosis analysis. These indicators are tested on a real industrial fan equipped with ceramic balls, in its environment. Several measurements for different operating points are tested to validate the approach and to its robustness during long time tests. From an experimental comparison between a healthy fan and another with damaged bearings, a frequency selection is performed to identify the frequency ranges where the energy is the most sensitive to the considered faults. This actuator is used in an air conditioning fan in aeronautic applications.

Bearing fault detection in synchronous machine based on the statistical analysis of stator current

In this paper, an original method to compute an indicator for efficient detection of bearing defaults in high speed synchronous machines is presented. This indicator is based on the statistical analysis of the stator current spectrum. The principle of the method is to compare the current spectrum to a healthy reference spectrum. The reference spectrum is used to center and reduce the current spectrum and provides an indication on its difference from the reference spectrum. A statistical based indicator is then constructed as the sum of the contributions of the centered reduced spectrum for different interesting frequency bands. This indicator has been tested on 2 different test campaigns, for 4 different speeds and compared a vibratory indicator. Results show that the proposed indicator evolves the same way than the vibratory indicator and provides with an efficient detection of bearing fault with only very few false alarms.

Variable importance assessment in lifespan models of insulation materials: A comparative study

This paper presents and compares different methods for evaluating the relative importance of variables involved in insulation lifespan models. Parametric and non-parametric models are derived from accelerated aging tests on twisted pairs covered with an insulating varnish under different stress constraints (voltage, frequency and temperature). Parametric models establish a simple stress-lifespan relationship and the variable importance can be evaluated from the estimated parameters. As an alternative approach, non-parametric models explain the stress-lifespan relationship by means of regression trees or random forests (RF) for instance. Regression trees naturally provide a hierarchy between the variables. However, they suffer from a high dependency with respect to the training set. This paper shows that RF provide a more robust model while allowing a quantitative variable importance assessment. Comparisons of the different models are performed on different training and test sets obtained through experiments.

Regression methods for improved lifespan modeling of low voltage machine insulation

This paper deals with the modeling of insulation material lifespan in a partial discharge regime under certain accelerated electrical stresses (voltage, frequency and temperature). An original model, relating the logarithm of the insulation lifespan, the logarithm of the electrical stress and an exponential form of the temperature, is considered. An estimation of the model parameters is performed using three methods: the design of experiments (DoE) method, the response surface method (RSM) and the multiple linear regression (MLR) method. The estimation is obtained on learning sets determined according to each method specification. The performance, in terms of estimation, of each of the three methods is evaluated on a test set composed of additional experiments. For economic reasons and flexibility, the learning and test sets are composed of experiments carried out on twisted pairs of wires covered by an insulator varnish. The ability of the DoE and the RSM methods to organize and to limit the number of experiments is confirmed. The MLR method, however, shows more flexibility with regard to the studied configurations. Thus, it offers an efficient solution when organization is not required or not possible. Moreover, the flexibility of MLR allows specific ranges for the factors to be explored. A local analysis of the estimation performance shows that very short and long lifespans cannot be simultaneously represented by the same model.

Parametric and Nonparametric Models for Lifespan Modeling of Insulation Systems in Electrical Machines

This paper describes an original statistical approach for the lifespan modeling of electric machine insulation materials. The presented models aim to study the effect of three main stress factors (voltage, frequency, and temperature) and their interactions on the insulation lifespan. The proposed methodology is applied to two different insulation materials tested in partial discharge regime. Accelerated ageing tests are organized according to experimental optimization methods in order to minimize the experimental cost while ensuring the best model accuracy. In addition to classical parametric models, the life–stress relationship is expressed through original nonparametric and hybrid models that have never been investigated in insulation aging studies before. These two models present the original contribution of this paper. For each material, models are computed from organized sets of experiments and applied on a randomly configured test set for validity checking. The different models are evaluated and compared in order to define their optimal use.

Lifespan and aging modeling methods for insulation systems in electrical machines: A survey

This paper deals primarily with modeling lifespan and, to a lesser degree, aging in insulation systems of electrical machines. The different aging processes involved, including partial discharges, are described. Previous works, in conjunction with the current study, are described and lead to the conclusion that there is no single method, even with accelerated lifetime tests, able to provide lifetime models taking into account all the stress factors in a simple way. Consequently, the principles of different regression methods are described (design of experiments, response surface, multilinear and robust regression) and applied with a limited number of experiments, thus reducing experimental cost. Several types of insulation material were tested in order to confidently recommend one in particular.