Daniel Morinigo-Sotelo

Practical Aspects of Mixed-Eccentricity Detection in PWM Voltage-Source-Inverter-Fed Induction Motors

In this paper, we present the results of an ongoing investigation relating to the detection of static and dynamic eccentricity in cage induction motors fed by a pulsewidth-modulation frequency converter using line-current spectral analysis. Two different motors fed by different supply sources (utility voltage and two different voltage converters at different assigned frequencies) were tested. A statistical analysis of the results obtained was carried out. These results allow us to present practical conclusions relating to the detection of mixed eccentricity.

Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

Eccentricity diagnosis in Inverter - Fed Induction Motors via the Analytic Wavelet Transform of transient currents

In this paper a recently developed induction motors diagnosis methodology is applied to detect mixed eccentricity in Inverter-Fed Induction Motors (IFIMs). The classic FFT method can not be applied when the stator current captured is not in steady state (which is common in IFIMs). The approach is based on obtaining a 2D time - frequency plot representing the time - frequency evolution of the main components in a stator transient current. The time-frequency maps are generated with high detail using the Analytic Wavelet Transform. Thanks to this, the evolutions of the main Winding Harmonics, Principal Slot Harmonics and Eccentricity Related Harmonics are traced precisely. As a consequence, the time-frequency plane characteristic patterns produced by the Eccentricity Related Harmonics are easily and clearly identified enabling a reliable diagnosis. The methodology capabilities have been shown successfully diagnosing a healthy IFIM and an IFIM with mixed eccentricity. The transients analyzed consist of a startup and a decrease in the assigned frequency.

Early broken rotor bar detection techniques in VSD-fed induction motors at steady-state

Condition monitoring has become necessary to detect failures in induction motors (IM) where the detection of incipient faults is of great concern. However, the detection of partially-broken rotor bars at an early stage is not so easily achieved. Therefore, it is necessary to use suitable condition monitoring accompanied with signal processing techniques to detect partially-broken rotor bar. This paper presents a comparative study of various condition monitoring methods accomplished for IM with the aim of early detection of one partially-broken rotor bar by steady-state current spectrum analysis and different supply conditions, such as two different variable speed drives providing three fundamental supply frequencies, and the line supply case. The study includes three different load conditions for each case. Results show that the most accurate and robust analysis methodology for early detection of broken rotor bars under different supply conditions, fundamental supply frequencies and load conditions during steady-state analysis, are the subspace methods.

Broken Rotor Bar Detection in VSD-fed Induction Motors at Startup by High-Resolution Spectral Analysis

The fault detection in an induction motor (IM) operated by a variable speed drive (VSD) is an actual industrial need as most of the line-fed machines are replaced by a VSD, due to their improved speed regulation and fast dynamic response. However, undesired harmonics are always present when the IM is fed through a VSD. Under this operating condition, most developed techniques are unable to detect faults in the IM. This paper presents a technique based on the multiple signal classification (MUSIC) method, and it is applied to a VSD-fed IM during the startup transient; in order to verify the capability of the method to identify one broken rotor bar. From the experimental results, the proposed method is proven to be sensitive enough to detect one broken rotor bar, enabling a reliable diagnosis under different fundamental supply frequencies and load conditions.

Transient detection of close components through the chirplet transform: Rotor faults in inverter-fed induction motors

Up to now, detection of rotor faults in inverter-fed induction motors has received very little attention. This fault is difficult to be detected, since the fault-related components are too close to the fundamental (the inverter usually operates at low slip). Moreover, classic techniques cannot be applied since steady states are not common. The causes of this type of fault are analyzed in the paper, showing its importance. Particularly, cases of real faults in electric traction are exposed. Then, the paper explores the use of linear time-frequency transforms to detect the time-frequency evolution of the fault-related components. It is shown how the most common linear transforms, such as the Short Time Fourier Transform, do not enable the fault detection. The Chirplet Transform (which has never been used for diagnosing purposes), is proposed to obtain the components evolutions, even if they are too close in the time-frequency plane. The technique is validated through startup tests, in which the presence of the fault is quantified when analyzing the stator current.

Broken bar condition monitoring of an induction motor under different supplies using a linear discriminant analysis

This paper presents a procedure for broken rotor bar diagnosis in induction motors based in data extracted from stator current, which is calculated in the time and frequency domains. Data comes from a tested motor fed by different types of supply: direct line and two different Voltage Source Inverters. Diagnosis is always difficult in Voltage Source Inverter fed motors due to inherent noise level and the presence of additional non-related fault harmonics in the stator current spectrum. Moreover, the motor was tested under different load conditions, from no-load to full-load. Diagnosis is also more difficult at lower load levels. Previous to fault classification, a variable reduction was carried out using Principal Component Analysis. Fault classification was performed using Linear Discriminant Analysis. The motor was tested with different fault severities, what allowed us to perform an analysis oriented to different maintenance approaches, considering the criticality of the motor.

Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling

Intelligent fault detection in induction motors (IMs) is a widely studied research topic. Various artificial-intelligence-based approaches have been proposed to deal with a large amount of data obtained from destructive laboratory testing. However, in real applications, such volume of data is not always available due to the effort required in obtaining the predictors for classifying the faults. Therefore, in realistic scenarios, it is necessary to cope with the small-data problem, as it is known in the literature. Fault-related instances along with healthy state observations obtained from the IM compose datasets that are usually imbalanced, where the number of instances classified as the faulty class (minority) is much lower than those classified under the healthy class (majority). This paper presents a novel supervised classification approach for IM faults based on the adaptive boosting algorithm with an optimized sampling technique that deals with the imbalanced experimental dataset. The stator current signal is used to compose a dataset with features both from the time domain and from the frequency domain. The experimental results demonstrate that the proposed approach achieves higher performance metrics than others classifiers used in this field for the incipient detection and classification of faults in IM.

Assessment of the lubrication condition of a bearing using spectral analysis of the stator current

Bearing lubrication is very important to ensure a satisfactory and long operation of bearings. An excess of lubrication can be as damaging as a lack of lubrication. An excess of oil or grease has damaging effects in the short term such as difficult heat evacuation, sliding balls and greater current consumption. As MCSA is widely recognized as a useful and reliable tool for condition monitoring of induction motors, we have used it to identify signs in the stator current spectrum of an excess of lubrication.

Fault detection in inverter-fed induction motors in transient regime: State of the art

Fault detection in induction motors operating in non-stationary regimes has become a need in todays industry. Moreover, the number of fed induction motors has significantly increased in recent years Therefore, several fault detection techniques have been lately proposed based, mainly, in an adequate input signal processing to obtain some fault signatures in the time and/or frequency domains. In this paper, a review of time-frequency techniques applied to fault detection in inverter-fed induction motors in a transient state is presented. The strengths and weaknesses of these techniques are discussed with the goal of providing guidance for future developments in this field.