Mohamed El Badaoui

Electrical signals analysis of an asynchronous motor for bearing fault detection

We present in this paper the detection of a rolling defect in an asynchronous machine by analysis of the electric signals. For this purpose, we used a Wiener filter to decrease the dynamics of the 50 Hz and to increase the frequencies associated to the mechanical load. Thus, we could detect the presence of a ball defect. This result is corroborated by an envelope analysis of the vibratory signals. The suggested method exploits the cyclostationarity of electrical signals (voltage and current) via their cyclic statistics to resynchronize the signals according to the electrical cycle (50 Hz) in order to recover frequency fluctuations. We then estimate the Wiener filter which is highly adapted to our application in order to obtain a signal corresponding to the electrical part only, which allows extracting the mechanical part on the measured current. An experimental result, in the presence of fault in rolling element bearings, illustrates the high performance of the proposed method

Chatter detection in milling machines by neural network classification and feature selection

In modern industry, milling is an important tool when a high material removal rate is required. Chatter detection in this situation is a crucial step for improving surface quality and reducing both noise and rapid wear of the cutting tool. This paper proposes a new methodology for the chatter detection in computer numerical control milling machines. This methodology is based on vibratory signal analysis and artificial intelligence. The methodology consists of five major steps: (1) data acquisition, (2) signal processing, (3) features generation, (4) features selection and (5) classification. As chatter components occur around system resonance frequencies, a multiband resonance filtering method is proposed at the processing step. The process is then followed by envelope analysis. This allows the signal-to-noise ratio to be increased and the sensitivity of generated features to be increased. Extracted features are then ranked based on their entropy in which only best features are selected and presented to the system for classification. At the classification step, the selected features are classified into two classes: stable and unstable utilizing neural networks. Two neural network approaches, radial basis function and multi-layer perceptrons, are tested. The developed approach is applied for chatter detection in a Huron K2X10 milling machine. This approach is tested on a milling machine at different depths of cut and various rotational speeds. Discussions are made and the results confirm the accuracy of the proposed methodology.

Cyclostationary modeling of ground reaction force signals

The importance of the measurement of human locomotion for the processes of diagnosis and treatment of locomotion disorders is increasingly being recognized. Human locomotion, in particular walking and running are defined by sequences of cyclic gestures. The variability of these sequences can reveal abilities or motorskill failures. The purpose of this study is to analyze and to characterize a runners step from the ground reaction forces (GRF) measured during a run on a treadmill. Traditionally, the analysis of GRF signals is performed by the use of signal processing methods, which assume statistically stationary signal features. The originality of this paper consists in proposing an alternative framework for analyzing GRF signals, based on cyclostationary analysis. This framework, being able to model signals with periodically varying statistics, is better at showing the development of runners fatigue.

Hidden Markov Models for the Prediction of Impending Faults

Reliability and safety are two important concepts in industrial applications. Thus, the development of monitoring tools, which are able to ensure the continuity of service by predicting faults, should improve competitiveness. This paper presents two probabilistic methods based on hidden Markov models (HMMs) for the prediction of impending faults. This paper shows that a prediction of faults is not limited to the estimation of the remaining useful life but is also extended to the estimation of the risk of an imminent appearance of faults in the future. The first method consists in modeling the degradation process of the studied system by a single HMM. A probabilistic model is proposed to predict an imminent appearance of a fault. The second method consists in modeling the degradation states by a set of HMMs. Another probabilistic model is proposed to predict an imminent appearance of a fault. An experimental application is proposed to demonstrate their applicability. The obtained results show their effectiveness to predict the imminent appearance of faults.

Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal

The purpose of this paper is to address the question of the existence of auto regressive moving average (ARMA) models with reduced order for neurodegenerative disorder signals by using Huberian approach. Since gait rhythm dynamics between Parkinsons disease (PD) or Huntingtons disease (HD) and healthy control (CO) differ, and since the stride interval presents great variability, we propose a different ARMA modeling approach based on a Huberian function to assess parameters. Huberian function as a mixture of L2 and L1 norms, tuned with a threshold γ from a new curve, is chosen to deal with stride signal disorders. The choice of γ is crucial to ensure a good treatment of NO and allows to reduce the model order. The disorders induce disturbances in the classical estimation methods and increase of the number of parameters of the ARMA model. Here, the use of the Huberian function reduces the number of parameters of the estimated models leading to a disease transfer function with low order for PD and HD. Mathematical approach is discussed and experimental results based on a database containing 16 CO, 15 PD, and 19 HD are presented. Author-HighlightsReduced order ARMA Modeling.New curve to deal with Natural Outliers in the estimated residuals involving reduced order.Proof of the asymptotic convergence in law of the robust estimator including stochastic differentiability and m-dependence approaches.Application to the ARMA modeling of neurodegenerative disorder signals such that Parkinson and Huntington.

Blind Separation of Cyclostationary Signals

Thermal CVD process for forming Si3N4-type films on substrates by reaction of gaseous NF3 with gaseous disilane at a temperature in the range of 250 DEG -500 DEG C., at pressures of 0.1-10 Torr. The mole ratio of NF3 to silane is in the range of 0.5-3.0 and the reaction zone is preferably isothermal (T controlled to within +5 DEG C.). The resulting films have RIs in the range of 1.4 to 3.0. The process parameters can be controlled to dope the film with H and/or F, or to create zones of differing properties within the film. The process does not cause radiation damage, metal migration, stored charge dissipation or high levels of impurities. Control of distance between adjacent wafers and wafer-to-wall spacing combined with laminar gas flow gives excellent film thickness uniformity, on the order of below about +/-5% across the wafer face, both within (across) wafers and from wafer to wafer (batch uniformity).

Using the Cyclostationarity of Electrical Signal for Bearing Fault Detection in Induction Machine

We present in this paper the detection of a rolling defect in an asynchronous machine by analysis of the electric signals. For this purpose, we used a Wiener filter to decrease the dynamics of the 50 Hz and to increase the frequencies associated to the mechanical load. Thus, we could detect the presence of a ball defect. This result is corroborated by an envelope analysis of the vibratory signals. The suggested method exploits the cyclostationarity of electrical signals (voltage and current) via their cyclic statistics to resynchronization the signals according to the electrical cycle (50 Hz) in order to recover frequency fluctuations. We then estimate the Wiener filter which is highly adapted to our application in order to obtain a signal corresponding to the electrical part only, which allows extracting the mechanical part on the measured current. An experimental result, in the presence of fault in rolling element bearings, illustrates the high performance of the proposed method.

Bootstrap for almost cyclostationary processes with jitter effect

In this paper we consider almost cyclostationary processes with jitter effect. We propose a boot-strap approach based on the Moving Block Bootstrap method to construct pointwise and simultaneous confidence intervals for the Fourier coefficients of the autocovariance function of such processes. In the simulation study we show how our results can be used for second-order frequency detection. We compare behavior of our approach for jitter effects caused by perturbations from two distributions, namely uniform and truncated normal. Finally, we present a real data application of our methodology.

A modified filtering model of VGRF gait signals

In order to understand the movement ecology of species, preprocessing is considered a fundamental step to in treating any signal contaminated with noise. Thus it is performed on data to remove undesirable characteristics that were introduced during acquisition. A modified filtering model of gait vertical ground reaction force signals is highlighted in this work in the light of the commonly applied fixed filters on the signals from various sources. This common practice is pointed out to harm the signals content. The alternative technique is based on Empirical Mode Decomposition.

Synchrosqueezing Characterize Non-stationary Signals: Application on Gait-Vertical Ground Reaction Force

Signal is a physical quantity we can measure like gait vertical ground reaction force. However, the latter are such non- stationary signals require a deep understanding of their instantaneous amplitude, phase and frequency. From this, one can model its stationary and non- stationary part and approximate the noise. In addition, one can practice such features for inter-subject classification of the vertical ground reaction force signals like between normal and pathological. Not to add, one objective could also concentrate in intra-subject classification like between usual gait and gait associated with cognitive tasks for the same subject as this paper mainly concerns. For that purpose, Synchrosqueezing of time-frequency representation is being used to spot its power in non-stationary signal analysis and classification. This technique also helped in developing an accurate detection of outliers within such time series signal like when subjects encounter turning points during walking. All this would help in a correct assessing treatment effectiveness and précising the stage of disease. In addition, this would be a starting point for having accurate parameters in elderly fall detection.