Michelle Vieira

Induction motors' faults detection and localization using stator current advanced signal processing techniques

The knowledge about fault mode behavior of an induction motor drive system is extremely important from the standpoint of improved system design, protection, and fault-tolerant control. This paper addresses the application of motor current spectral analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. Intensive research effort has been for some time focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult since distortions of the current waveform caused by the abnormalities in the induction motor are usually minute. This paper takes the initial step to investigate the efficiency of current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequencies determined. In the present investigation, the frequency signature of some asymmetrical motor faults are well identified using advanced signal processing techniques, such as high-resolution spectral analysis. This technique leads to a better interpretation of the motor current spectra. In fact, experimental results clearly illustrate that stator current high-resolution spectral analysis is very sensitive to induction motor faults modifying main spectral components, such as voltage unbalance and single-phasing effects.

Estimation of the instantaneous amplitude and frequency of non-stationary short-time signals

We consider the modeling of non-stationary discrete signals whose amplitude and frequency are assumed to be nonlinearly modulated over very short-time duration. We investigate the case where both instantaneous amplitude (IA) and instantaneous frequency (IF) can be approximated by orthonormal polynomials. Previous works dealing with polynomial approximations refer to orthonormal bases built from a discretization of continuous-time orthonormal polynomials. As this leads to a loss of the orthonormal property, we propose to use discrete orthonormal polynomial bases: the discrete orthonormal Legendre polynomials and a discrete base we have derived using Gram-Schmidt procedure. We show that in the context of short-time signals the use of these discrete bases leads to a significant improvement in the estimation accuracy. We manage the model parameter estimation by applying two approaches. The first is maximization of the likelihood function. This function being highly nonlinear, we propose to apply a stochastic optimization technique based on the simulated annealing algorithm. The problem can also be considered as a Bayesian estimation which leads us to apply another stochastic technique based on Monte Carlo Markov Chains. We propose to use a Metropolis Hastings (MH) algorithm. Both approaches need an algorithm parameter tuning that we discuss according our application context. Monte Carlo simulations show that the results obtained are close to the Cramer-Rao bounds we have derived. We show that the first approach is less biased than the second one. We also compared our results with the higher ambiguity function-based method. The methods proposed outperform this method at low signal to noise ratios (SNR) in terms of estimation accuracy and robustness. Both proposed approaches are of a great utility when scenarios in which signals having a small sample size are non-stationary at low SNRs. They provide accurate system descriptions which are achieved with only a reduced number of basis functions.

A New Flexible Approach to Estimate the IA and IF of Nonstationary Signals of Long-Time Duration

In this paper, we propose an original strategy for estimating and reconstructing monocomponent signals having a high nonstationarity and long-time duration. We locally apply to short-time duration intervals the strategy developed in our previous work about nonstationary short-time signals. This paper describes a nonsequential time segmentation that provides segments whose lengths are suitable for modeling both the instantaneous amplitude and frequency locally with low-order polynomials. Parameter estimation is done independently for each segment by maximizing the likelihood function by means of the simulated annealing technique. The signal is then reconstructed by merging the estimated segments. The strategy proposed is sufficiently flexible for estimating a large variety of nonstationarity and specifically applicable to high-order polynomial phase signals. The estimation of a high-order model is not necessary. The error propagation phenomenon occurring with the known approach, the higher ambiguity function (HAF)-based method, is avoided. The proposed strategy is evaluated using Monte Carlo noise simulations and compared with the Cramer-Rao bounds (CRBs). The signal of a songbird is used as a real example of its applicability.

A AM/FM single component signal reconstruction using a nonsequential time segmentation and polynomial modeling

Summary form only given. The problem of estimating nonstationary signals has been considered in many previous publications. In this paper we propose an alternative algorithm in order to accurately estimate AM/FM signals. Only single component signals are considered. We perform local polynomial modeling on short time segments using a nonsequential strategy. The degree of polynomial approximation is limited due to the shortness of each time segment. The time support of a segment is controlled by a criterion defined on the spectrogram. To keep optimality a maximum likelihood procedure estimates the local model parameters leading to a nonlinear equation system in R7. This is solved by a simulated annealing technique. Finally, the local polynomial models are merged to reconstruct the entire signal model. The proposed algorithm enables highly nonlinear AM/FM estimation and shows robustness even when signal to noise ratio (SNR) is low. The appropriate Cramer Rao bounds (CRB) are presented for both polynomial phase and amplitude signals. Monte Carlo simulations show that the proposed algorithm performs well. Finally, our proposed method is illustrated using both numerical simulations and a real signal of whale sound.

Maximum likelihood parameter estimation of short-time multicomponent signals with nonlinear AM/FM modulation

Parameter estimation for closely spaced or crossing frequency trajectories is a difficult signal processing problem, especially in the presence of both nonlinear amplitude and frequency modulations. In this paper, polynomial models are assumed for the instantaneous frequencies and amplitudes (IF/IA). We suggest two different strategies to process multicomponent signals. In the first one, which is optimal, all model parameters are simultaneously estimated using a maximum likelihood procedure (ML), maximized via a stochastic technique called simulated annealing (SA). In the second strategy, which is suboptimal, the signal is iteratively reconstructed component by component. At each iteration, the IF and IA of one component are estimated using the ML procedure and the SA technique. To evaluate the accuracy of the proposed strategies, Monte Carlo simulations are presented and compared to the derived Cramer-Rao bounds for closely spaced and crossing frequency trajectories. The results show the proposed algorithms perform well compared to existing techniques

Bayesian analysis for the fault detection of three-phase induction machine

One of the most widely used techniques for obtaining information on the state of health of three-phase induction machines is based on the processing of stator current. In fact, in the case of steady state operations, anomalous current spectral components, that increase if a fault occurs, allow diagnosis of the presence and, in some case, the type of fault. In this paper, a Bayesian approach is proposed using a simulation technique, the Markov chain Monte Carlo (MCMC), to estimate the amplitude of some spectral components modified by machine faults and the slip, a parameter related to the load conditions, with a view to automatically detecting faults. Results on real stator current waveform are given.

Parameter estimation of short-time multi-component signals using damped-amplitude & polynomial-frequency model

This paper concerns the parameter estimation of multi-component damped oscillations having non-linear frequency. In this paper, the instantaneous frequency is approximated by polynomials while the amplitude is characterized by damped exponentials to connect directly to its physical interpretations. A maximum likelihood procedure is developed via an adaptive simulated annealing technique which helps to speed up the convergence. Results on simulated signals show that the proposed algorithm is more efficient than the algorithm based on polynomial amplitude models, and allows the estimation of damping coefficients over a very short time duration. Finally, the proposed algorithm is applied for characterizing the ambient vibrations of a building.

On the choice of prior for induction motor parameters estimation using MCMC methods

Processing of the stator current of three-phase induction machines is a widely used technique for obtaining health state information. Most of the spectral components of the current depend on the slip, a parameter related to the load. A Bayesian approach associated with a Monte Carlo Markov chain algorithm is proposed to analyze the stator current of the healthy machine during steady-state operation, i.e., to estimate the slip and the noise variance. This approach allows us to take into account a priori information on the signal and to eliminate all the unknown and uninteresting stator current parameters. Several parameter prior assignments are discussed and results on real data are given.