Condition monitoring of bearing faults using the stator current and shrinkage methods

Producción CientíficaCondition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.CAPES (process BEX552269/2011-5

Analysis of the use of the Hanning Window for the measurement of interharmonic distortion caused by close tones in IEC standard framework

Producción CientíficaThe widespread use of devices based on power electronics and other nonlinear loads has led to an increase in harmonic distortion that affects the quality of power systems. Therefore, the correct measurement of harmonic and interharmonic content is necessary. The International Electrotechnical Commission (IEC) standards define the concepts of spectral and time grouping required for such measurements. This paper demonstrates that the procedures defined in the IEC standards are not sufficiently accurate when several close interharmonic tones interact due to the lack of stability of the values that the Discrete Fourier Transform obtains in each sampling window, and to the inaccuracy in the measurement of interharmonic groups and rates when using the Hanning window. This paper proposes novel solutions based on time aggregation and the use of other groupings and alternative windows. The results obtained are compared with the results produced by applying the rectangular window indicated in the standards, using sensitivity analysis varying one of the tones and using experimental results measuring the output signals of frequency inverters driving induction motors. The proposed method achieves greater accuracy and stability in the measurement of spectral groupings and their related distortion rates in signals with abundant and dispersed interharmonic content

A study of the effects of time aggregation and overlapping within the framework of IEC standards for the measurement of harmonics and interharmonics

Producción CientíficaThe increasing incorporation of power electronics and other non-linear loads, in addition to their energy advantages, also implies a poor power quality, especially as regards harmonic pollution. Different solutions have been proposed to measure harmonic content, taking the International Electrotechnical Commission (IEC) standards as a reference. However, there are still some issues related to the measurement of the harmonic, and especially, interharmonic content. Some of those questions are addressed in this work, such as the problem derived from the instability of the values obtained by applying the discrete Fourier transform to each sampling window, or the appearance of local peaks when there are tones separated by multiples of the resolution. Solutions were proposed based on time aggregation and the overlapping of windows. The results demonstrate that aggregation time, window type, and overlapping can improve the accuracy in harmonic measurement using Fourier transform-based methods, as defined in the standards. The paper shows the need to consider spectral and time groupings together, improving results by using an appropriate percentage of overlap and an adaptation of the aggregation time to the harmonic content

Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors

Producción CientíficaThree-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.Consejo Nacional de Desarrollo Científico y Tecnológico - (processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3

Estimation of bearing fault severity in line-connected and inverter-fed three-phase induction motors

Producción CientíficaThis paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.CAPES (process BEX552269/2011-5)National Council for Scientific and Technological Development (grant #474290/2008-3, #473576/2011-2, #552269/2011-5, #307220/2016-8

Time-frequency analysis based on minimum-norm spectral estimation to detect induction motor faults

Producción CientíficaIn this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.Consejo Nacional de Ciencia y Tecnología (Proyecto 487058)Universidad de Guanajuato (Proyecto 248495/2019

Fundamental frequency suppression for the detection of broken bar in induction motors at low slip and frequency

Producción CientíficaBroken rotor bar (BRB) is one of the most common failures in induction motors (IMs) these days; however, its identification is complicated since the frequencies associated with the fault condition appear near the fundamental frequency component (FFC). This situation gets worse when the IM slip or the operation frequency is low. In these circumstances, the common techniques for condition monitoring may experience troubles in the identification of a faulty condition. By suppressing the FFC, the fault detection is enhanced, allowing the identification of BRB even at low slip conditions. The main contribution of this work consists of the development of a preprocessing technique that estimates the FFC from an optimization point of view. This way, it is possible to remove a single frequency component instead of removing a complete frequency band from the current signals of an IM. Experimentation is performed on an IM operating at two different frequencies and at three different load levels. The proposed methodology is compared with two different approaches and the results show that the use of the proposed methodology allows to enhance the performance delivered by the common methodologies for the detection of BRB in steady state.CONACyT scholarship (415315)Project FOFI-UAQ 2018 FIN201812PRODEP UAQ-PTC-385 gran

Robust detection of incipient faults in VSI-fed induction motors using quality control charts.

A considerable amount of papers have been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper a new method is proposed which is based on robust statistical techniques applied in Quality Control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked, broken bar, a faulty bearing and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults

Genetic algorithm methodology for the estimation of generated power and harmonic content in photovoltaic generation

Producción CientíficaRenewable generation sources like photovoltaic plants are weather dependent and it is hard to predict their behavior. This work proposes a methodology for obtaining a parameterized model that estimates the generated power in a photovoltaic generation system. The proposed methodology uses a genetic algorithm to obtain the mathematical model that best fits the behavior of the generated power through the day. Additionally, using the same methodology, a mathematical model is developed for harmonic distortion estimation that allows one to predict the produced power and its quality. Experimentation is performed using real signals from a photovoltaic system. Eight days from different seasons of the year are selected considering different irradiance conditions to assess the performance of the methodology under different environmental and electrical conditions. The proposed methodology is compared with an artificial neural network, with the results showing an improved performance when using the genetic algorithm methodology.CONACYT (scholarship 415315)FOFI –UAQ 2018 (project FIN201812)PRODEP (project UAQ-PTC-385

DETECCIÓN DE FALLA DE RODAMIENTO EN UNA CADENA CINEMÁTICA VÍA EMISIÓN ACÚSTICA

ResumenLas cadenas cinemáticas son componentes esenciales en la mayoría industrias, compuestas principalmente por motores de inducción, cajas de engranes, etc.., las fallas de estás provocan grandes pérdidas monetarias. Para evitarlos se utilizan sistemas automatizados de monitorización. Existen diferentes técnicas de monitoreo con diferentes metodologías, la emisión acústica (EA) es uno de los métodos de monitoreo no invasivo para la detección de fallas en estos sistemas. En este trabajo se presenta el desarrollo de un sistema de adquisición de señales de EA y una metodología basada en el análisis de estas señales para la detección de falla de rodamiento en un banco de pruebas de una cadena cinemática, la identificación de los componentes relacionados con la falla para el análisis es respaldado por su modelo teórico. Los resultados obtenidos muestran la detección de falla en rodamiento en altas frecuencias y la metodología para el análisis de la EA.Palabras Claves: Detección de fallas, emisión acústica, FFT, rodamientos. DETECTION OF BEARING FAILURE IN A CINEMATIC CHAIN VIA ACOUSTIC EMISSIONAbstractKinematics Chains are essential components in most industries, composed mainly of induction motors, gearboxes, etc.., failures within them cause great monetary losses. To avoid this, automated monitoring systems are used. There are different monitoring techniques with different methodologies, the acoustic emission (AE) is one of the methods of noninvasive monitoring for the detection of failures in these systems. This work presents the development of an AE signal acquisition system and a methodology based on the analysis of these signals for the detection of bearing failure in a test bench of a kinematic chain. The identification of the components related to the fault for the analysis is supported by its theoretical model. The obtained results show the detection of failure in rolling in high frequencies and the methodology for the analysis of the AE. Keywords: Acoustic emission, bearings, faults detection, FFT