Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the Linear Discriminant Analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a Fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology is evaluated by considering a complete dataset of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.Peer ReviewedPostprint (published version

Industrial data-driven monitoring based on incremental learning applied to the detection of novel faults

The detection of uncharacterized events during electromechanical systems operation represents one of the most critical data challenges dealing with condition-based monitoring under the Industry 4.0 framework. Thus, the detection of novelty conditions and the learning of new patterns are considered as mandatory competencies in modern industrial applications. In this regard, this article proposes a novel multifault detection and identification scheme, based on machine learning, information data-fusion, novelty-detection, and incremental learning. First, statistical time-domain features estimated from multiple physical magnitudes acquired from the electrical motor under inspection are fused under a feature-fusion level scheme. Second, a self-organizing map structure is proposed to construct a data-based model of the available conditions of operation. Third, the incremental learning of the condition-based monitoring scheme is performed adding self-organizing structures and optimizing their projections through a linear discriminant analysis. The performance of the proposed scheme is validated under a complete set of experimental scenarios from two different cases of study, and the results compared with a classical approach.Peer ReviewedPostprint (author's final draft

Thermography-based methodology for multifault diagnosis on kinematic chain

The procedures for condition monitoring of electromechanical systems are undergoing a reformulation, mainly, due to the current thermographic affordability of infrared cameras to be incorporated in industrial applications. However, high-performing multifault data-driven methodologies must be investigated in order to infer reliable condition information from the thermal distribution of not only electrical motors but also of shafts and couplings. To address this issue, a novel thermography-based methodology is proposed. First, the infrared capture is processed to obtain a thermographic residual image of the kinematic chain. Second, the thermal distribution of the image's regions of interest is characterized by means of statistical features. Finally, a distributed self-organizing map structure is used to model the nominal thermal distribution to subsequently perform a fault detection and identification. The method provides a reliability quantification of the resulting condition assessment in order to avoid misclassifications and identify the actual fault root-causes. The performance and the effectiveness of the proposed methodology is validated experimentally and compared with the classical maximum temperature gradient procedure.Peer ReviewedPostprint (published version

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

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

Identification of the electrical load by C-means from non-intrusive monitoring of electrical signals in non-residential buildings

Producción CientíficaLa acción combinada de diferentes equipos conectados a una instalación eléctrica es capaz de provocar cambios inesperados en el tipo de carga dentro de la instalación; estas variaciones de carga son responsables de algunas fallas eléctricas. En este artículo se presenta una metodología para clasificar e identificar los tipos de carga en entornos industriales. Las cantidades de energía (EPQ) y los valores actuales se utilizan para establecer índices con el fin de utilizarlos como características para un algoritmo C-means y realizar la clasificación de carga. La experimentación se realiza en un centro de salud recogiendo datos eléctricos en diferentes tableros de distribución eléctrica. Los resultados obtenidos del método de clasificación muestran variaciones en el comportamiento de la carga a lo largo del día. Además, algunas clases se pueden utilizar para reconocer equipos en la instalación eléctrica para su posterior inspección o detección de fallas

Power consumption analysis of electrical installations at healthcare facility

Producción CientíficaThis paper presents a methodology for power consumption estimation considering harmonic and interharmonic content and then it is compared to the power consumption estimation commonly done by commercial equipment based on the fundamental frequency, and how they can underestimate the power consumption considering power quality disturbances (PQD). For this purpose, data of electrical activity at the electrical distribution boards in a healthcare facility is acquired for a long time period with proprietary equipment. An analysis in the acquired current and voltage signals is done, in order to compare the power consumption centered in the fundamental frequency with the generalized definition of power consumption. The results obtained from the comparison in the power consumption estimation show differences between 4% and 10% of underestimated power consumption. Thus, it is demonstrated that the presence of harmonic and interharmonic content provokes a significant underestimation of power consumption using only the power consumption centered at the fundamental frequency.SEP-CONACYT, under grant 222453-2013FOMIX, under grant QRO-2014-C03-250269FOFIUAQ-FIN20161

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

Evaluation of Novelty Detection Methods for Condition Monitoring applied to an Electromechanical System

Dealing with industrial applications, the implementation of condition monitoring schemes must overcome a critical limitation, that is, the lack of a priori information about fault patterns of the system under analysis. Indeed, classical diagnosis schemes, in general, outdo the membership probability of a measure in regard to predefined operating scenarios. However, dealing with noncharacterized systems, the knowledge about faulty operating scenarios is limited and, consequently, the diagnosis performance is insufficient. In this context, the novelty detection framework plays an essential role for monitoring systems in which the information about different operating scenarios is initially unavailable or restricted. The novelty detection approach begins with the assumption that only data corresponding to the healthy operation of the system under analysis is available. Thus, the challenge is to detect and learn additional scenarios during the operation of the system in order to complement the information obtained by the diagnosis scheme. This work has two main objectives: first, the presentation of novelty detection as the current trend toward the new paradigm of industrial condition monitoring and, second, the introduction to its applicability by means of analyses of different novelty detection strategies over a real industrial system based on rotatory machinery