Guy Clerc

Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique

The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.

Prognosis of Bearing Failures Using Hidden Markov Models and the Adaptive Neuro-Fuzzy Inference System

Prognostics and health management (PHM) play a key role in increasing the reliability and safety of systems especially in key sectors (military, aeronautical, aerospace, nuclear, etc.). This paper presents a new methodology which combines data-driven and experience-based approaches for the PHM of roller bearings. The proposed methodology uses time domain features extracted from vibration signals as health indicators. The degradation states in bearings are detected by an unsupervised classification technique called artificial ant clustering. The imminence of the next degradation state in bearings is given by hidden Markov models, and the estimation of the remaining time before the next degradation state is given by the multistep time series prediction and the adaptive neuro-fuzzy inference system. A set of experimental data collected from bearing failures is used to validate the proposed methodology. Experimental results show that the use of data-driven and experience-based approaches is a suitable strategy to improve the PHM of roller bearings.

Classification of Induction Machine Faults by Optimal Time–Frequency Representations

This paper presents a new diagnosis method of induction motor faults based on time-frequency classification of the current waveforms. This method is based on a representation space, a selection criterion, and a decision criterion. In order to define the representation space, an optimized time-frequency representation (TFR) is designed from the time-frequency ambiguity plane. The selection criterion is based on Fishers discriminant ratio, which allows one to maximize the separability between classes representing different faults. A distinct TFR is designed for each class. The following two classifiers were used for decision criteria: the Mahalanobis distance and the hidden Markov model. The flexibility of this method allows an accurate classification independent from the level of load. This method is validated on a 5.5-kW induction motor test bench.

Study of Supercapacitor Aging and Lifetime Estimation According to Voltage, Temperature, and RMS Current

Due to its capacity to store or supply energy with high power, the supercapacitor is becoming an attractive component. Because of the electrostatic nature of energy storage, the endurance of this component toward repetitive charge and discharge cycles is relatively high. The goal of this paper is to demonstrate that cycling has an impact on the degradation of the supercapacitor and, as a result, on its lifetime. Based on accelerated cycling tests, some supercapacitors were studied using a dedicated test bench. Temperature, voltage, and current, which are the parameters that accelerate aging, are monitored. In fact, observations during the cycling tests show an important acceleration in the degradation compared with a similar static test having the same voltage and core temperature but without cycling. This paper proposes a method to quantify the acceleration of aging during a cycling phase.

Statistical and Neural-Network Approaches for the Classification of Induction Machine Faults Using the Ambiguity Plane Representation

A novel hybrid feature-reduction methodology is proposed as a contribution to the induction motor fault classification, to improve the classification rate of the current waveform events related to varieties of induction machine faults. This methodology relies on the combination of a feature-extraction technique based on the smoothed ambiguity plane designed for maximizing the separability between classes using Fishers discriminant ratio, with the feature-selection technique, based on the proposed error-probability model to select an optimal number of the extracted features. This model depends on two parameters, namely, the smoothing kernel used to derive the features and the distance measurement. The proposed methodology is validated experimentally on a 5.5-kW induction motor test bench, and their performances are compared with the classification algorithm based on neural networks with sigmoid and wavelets in hidden neurons, known as a flexible tool for learning and recognizing system faults. The results obtained show an accurate classification independent from the load level.

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.

Supercapacitors ageing prediction by neural networks

Supercapacitors are devices used in wide range of applications, for example in automotive applications. Therefore, it is important to monitor and track their ageing. This paper presents a new approach for predicting the ageing of supercapacitors based on the neo-fuzzy neuron in association with the one-step ahead time series prediction. Ageing information collected from the measurement of the equivalent series resistance and the double layer capacitance are used to train the neo-fuzzy neuron. The obtained model is used as a prognostic tool in order to forecast the ageing of supercapacitors. The performance of the proposed approach is evaluated by using an experimental platform for ageing supercapacitors. The experimental results show that the neo-fuzzy prediction model can track the ageing of supercapacitors.

Detailed study of the rotor asymmetry effects of induction machine under different supply conditions

This paper presents a detailed study of the rotor asymmetry, caused by broken bars, and their effects on the stator current of an induction machine under different supply conditions. The analytical models dedicated to the rotor faults are often penalized by simplifying assumptions, particularly the distribution of the magnetic flux density in the air-gap. The important information is likely to be omitted partially or completely if one considers only the fundamental of the magnetic flux density. In this study a detailed analysis by finite element method (FEM) of two induction motors in both healthy and faulty cases were presented. Experimental tests corroborate with the simulation results

Comparison of the Induction Motors Stator Fault Monitoring Methods Based on Current Negative Symmetrical Component

Summary This paper presents a survey of the diagnosis methods detecting the stator faults of the induction machines without modeling. The fault signature is based on the analysis of the negative sequence symmetrical component of the current. Two approaches are described and compared: the spectral analysis of the signature using the Discrete Fourier Transforms (DFT), the Short-time Discrete Fourier Transform (SDFT), the Gortzel algorithm and the synchronous demodulation of the current space vector. The theoretical principles of these methods are presented and their performances compared. The main purpose of this study lies in the investigation of the computational tools dedicated to the diagnosis and minimization of computing time. These methods are applied to an induction machine of 1.1 kW.

Industrial Time Series Modelling by Means of the Neo-Fuzzy Neuron

Industrial process monitoring and modeling represent a critical step in order to achieve the paradigm of zero defect manufacturing. The aim of this paper is to introduce the neo-fuzzy neuron method to be applied in industrial time series modeling. Its open structure and input independence provide fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modeled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the neo-fuzzy neuron is configured and trained accordingly by means of the auxiliary signal, past instants, and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modeled. The obtained results indicate the suitability of the neo-fuzzy neuron method for industrial process modeling.