Mario Eltabach

A comparison of external and internal methods of signal spectral analysis for broken rotor bars detection in induction motors

Like all mechanical devices, motors are subject to failures, which can sometimes lead to the shutting down of an entire industrial process. This paper looks at failure predictions in three-phase line-operated induction machines through spectral analysis or electric and electromagnetic signals. Fault characteristics frequencies generated in the estimated and the measured signal spectrum, as a result of mechanical abnormalities such as broken rotor bars, are analyzed. Spectral analyses of simple stator current, of the currents Park vector modulus, and or total and partial instantaneous electric powers are considered as external diagnosis. Internal methods of diagnosis are usually based on a mathematical model of the motor. This requires knowledge of the motors electrical parameters, which are affected by a number of physical phenomena such as temperature variations, skin effects, core losses, and saturation. As internal diagnosis, we examine different approaches to the spectral analysis of electromagnetic torque computed by stator and rotor flux estimation. To this end, the open loop method, the Luenberger observer and the Kalman filter are employed. Finally, experimental results enable us to draw up a table of comparison of internal and external methods in the detection of rotor imperfections, using two criteria under different load levels.

Comparative Investigation of Electric Signal Analyses Methods for Mechanical Fault Detection in Induction Motors

Abstract This article presents a comparative investigation of various media for non-invasive diagnosis of mechanical abnormalities in induction motors. Stator voltages and stator currents as well as noises on these signals are simulated first for a fault-free motor then for a motor with mechanical abnormalities. These signals are subsequently employed to compute the instantaneous powers P ab , P cb , P abc and last the current Park vector modulus known as the extended Park vector approach (EPVA) method. Waveforms of these simulated signals were analyzed using the power spectral density transformation. Commercially available diagnostics systems use the fact that the amplitude of some components known as fault characteristic frequencies in these electrical signals increase when mechanical abnormalities occur. The amplitude increase of these characteristic frequencies is employed as a criterion in order to investigate noise immunity of the five diagnosis methods, thus making possible their classification. Simulation and experimental results show that the EPVA reveals the highest noise immunity. Utilization of the EPVA is thus enhancing the reliability of diagnostics of induction motor drives.

Detection of broken rotor bar of induction motors by spectral analysis of the electromagnetic torque using the Luenberger observer

Rotor asymmetries lead to perturbations of airgap flux patterns in induction machines. These perturbations in flux components affect the electromagnetic torque, as well as stator currents and voltages. The supervision of these signals enables the detection of underlying mechanical asymmetries. The effect of mechanical imperfections on stator signals can be negligible in certain cases. Thus, this makes the detection of minor rotor imperfections a difficult task when the load is weak. In this paper we study the detection of rotor imperfections by spectral analysis of the electromagnetic torque. This torque is computed by rotor flux observers based on two induction motor models : fourth-order and second-order model. A comparison of spectral analysis methods applied to the torque shows the significance of the method we describe, which makes use of mechanical velocity, in order to detect low levels of asymmetry in the rotor cage at low level load. Finally, it is to be noted that this method is applied to real signals with and without imperfections.

KALMAN FILTERING AND TORQUE SPECTRAL ANALYSIS FOR BROKEN BAR DETECTION IN INDUCTION MOTORS

Abstract Rotor asymmetries in induction machines perturb many components such as, flux patterns and electromagnetic torque. The supervision of these signals enables early detection of such faults and help to machine diagnostic. This paper studies the detection of rotor imperfection by spectral analysis of the electromagnetic torque computed by two rotor flux estimators. In the first approach, the Kalman filter is used assuming to be known the mechanical velocity. The second approach uses Extended Kalman Filter (EKF) for speed estimation. Experimental results show the great capability of these methods to detect this type of faults.

Control of Snake Type Biomimetic Structure

Robotic cooperative tasks impose, in many cases, a grasping action. Grasping by coiling it is one of the most versatile action. The present article propose a frequency stability criterion based on the Kahman – Yakubovich – Popov Lemma for the hyper-redundant arms with continuum element that performs the grasping function by coiling. Dynamics of the biomimetical robot during non-contact and contact operations, for the position control, is studied. An extension of the Popov criterion is developed. The P control algorithms based on SMA snake-type robot actuators are introduced. Numerical simulations and experimental results of the snake type robot motion toward an imposed target are presented.

Induction motor fault detection by spectral principal components analysis of the supply currents

A new method of obtaining diagnostic data from induction motors, derived from the three supply currents using principal components analysis, is presented in this paper. The techniques presented here focus on extracting relevant information from spectral matrices. These techniques are qualified as parsimonious tools for exploring the behaviour of current vector valued signals in the frequency domain with minimal loss of information. In fact, the new diagnostic method obtains data from the three stator currents by exploring special fault characteristic frequencies in the power spectral density of the first principal component. The main advantage of this new diagnostic tool is its ability to extract automatically the characteristic frequencies relative to the different machine operating modes. This is accomplished using the proportion of the power attributed to the first principal component and/or using the sensor contribution to the power at specific frequencies. Thus, the new diagnostic method gives a good basis for an automatic non intrusive condition monitoring for rotating machinery.

Application of cepstrum prewhitening on non-stationary signals

In the field of vibration based condition monitoring a trusted symptom of a defective bearing is the observation of peaks, at characteristic frequencies, in the squared envelope spectrum (SES). If a machine is operating in a varying speed regime the SES is computed on the order tracked signal, i.e. the signal resampled at constant angular increments, and the SES can still be used for diagnostic. Despite its versatility a common problem with the SES is that peaks from other sources of vibrations, as for instance gears, can prevent the diagnosis of a defective bearing. Therefore pre-processing techniques are applied to the vibrational signal before the computation of the SES to enhance the signal from the bearings. Among these techniques cepstral pre-whitening (CPW) has gained much attention offering a remarkable capability of eliminating, in a blind way, both harmonics and modulation side-bands of the unwanted components. In the case of a varying speed regime the usual procedure consists of three steps: order track the signal, calculate the CPW, evaluate the SES. In this paper on the contrary the CPW is applied before the step of order tracking; therefore the proposed approach is: CPW the raw time signal, order tracking, evaluation of the SES. The remarkable observation is that for this approach the cepstrum does not present peaks at characteristic quefrencies, being the raw signal acquired in a varying speed regime. However this paper shows by means of numerical simulations and analysis of experimental data, that with the proposed methodology the masking components coming from the gears are suppressed and the signal from the defective bearing is enhanced.

Vibration Monitoring of Winch Epicyclic Gearboxes Using Cyclostationarity and Autoregressive Signal Model

This paper proposes a model-based technique using a combination of cyclostationary and autoregressive signal modelling in order to detect wear in a multistage planetary gear of lifting cranes. The first-order cyclostationarity is exploited by the analysis of the Time Synchronous Average part (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores the efficiency of a number of methods commonly used in vibration monitoring. Condition monitoring indicators are then extracted from different treated signals. In the experimental part, all these techniques are applied to a test bench data of a lifting winch. The goal is to trend the evolution of the extracted features during the test. This study reveals that the proposed procedure using this combination enhances the ability to detect and diagnose mechanical wear of winch planetary gears.