Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain

© 2016 Juan Jose Saucedo-Dorantes et al. Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.Postprint (published version

Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain

Postprint (published version

Gradual Wear Diagnosis of Outer-race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

[EN] Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz¿s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies.This work was supported by the Spanish Ministerio de Ciencia Innovación y Universidades and FEDER program in the framework of the `Proyectos de I+D de Generación de Conocimiento del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i, Subprograma Estatal de Generación de Conocimiento¿ (ref: PGC2018-095747-B-I00), and Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship 652815.Zamudio-Ramirez, I.; Osornio-Rios, RA.; Antonino-Daviu, JA.; Cureño-Osornio, J.; Saucedo-Dorantes, J. (2021). Gradual Wear Diagnosis of Outer-race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals. Electronics. 10(12):1-22. https://doi.org/10.3390/electronics10121486122101

Desarrollo de la Plataforma Estratégica para la organización sin ánimo de lucro Humanismo Integral al servicio del Hombre – HISH

At present, organizations are immersed in a changing and globalized world, to which technological advances and the accelerated development of commercialization and industry processes have a strong influence on social paradigms. NGOs or non-governmental organizations are not alien to this reality, since, instead of offering products, they are regularly in charge of the provision of services, precisely for this reason it is necessary for managers to implement processes, techniques and tools that allow responding to the needs of the context in which they interact, developing strategic planning, since this gives the possibility of analyzing both internally and externally (Rendon & Russi, 2016). The objective of this research is to develop the Strategic Platform for the non-profit organization Humanismo Integral en servicio del Hombre - HISH, for which studies addressing strategic planning and the instruments that must be developed for the success of the goals set within the institution. To achieve the above objective, the information from the external and internal environment was analyzed, as well as a diagnosis of the organization, and based on this, the design of the strategic plan was carried out. Therefore, the present work is divided into five sections, described below. In the first instance, the documentary study is reflected and the methodological contributions of the project are structured. Subsequently, the characterization of the non-profit organization is carried out, to understand its evolution and behavior, identifying important aspects such as the current situation of the company, its trajectory, the portfolio of services and the processes it executes. Later, the analysis of the environment is carried out, through the main economic, demographic, social, cultural, environmental, political and legal characteristics, classifying them in opportunities and threats. Then, in chapter four, a description of the health sector was made, with tools such as competitive diamond and Porter's five forces to define the dynamics of the sector. Consecutively, an analysis of the internal situation in each of the organization's areas was carried out, identifying the main strengths and weaknesses, establishing strategies that allow reducing the negative effects and enhancing the positive factors of the entity. Finally, the action plan is described, specifying the objectives to be achieved, the activities to be carried out, time, resources, managers and indicators that allow the management to be measured. The conclusions and recommendations based on the findings and analyzes developed are presented, as well as the bibliography that contains all the material consulted for the development of this project.Rionegro, Antioqui

Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.Peer ReviewedPostprint (published version

Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine

Strategies of condition monitoring applied to electric motors play an important role in the competitiveness of multiple industrial sectors. However, the risk of faults coexistence in an electric motor and the overlapping of their effects in the considered physical magnitudes represent, currently, a critical limitation to provide reliable diagnosis outcomes. In this regard, additional investigation efforts are required towards high-dimensional data fusion schemes, particularly over the features calculation and features reduction, which represent two decisive stages in such data-driven approaches. In this study, a novel multiple-fault detection and identification methodology supported by a feature-level fusion strategy and a Self-Organizing Maps (SOM) hierarchical structure is proposed. The condition diagnosis as well as the corresponding estimated probability are obtained. Moreover, the proposed method allows the visualization of the results while preserving the underlying physical phenomenon of the system under monitoring. The proposed scheme is performed sequentially; first, a set of statistical-time based features is estimated from different physical magnitudes. Second, a hybrid feature reduction method is proposed, composed by an initial soft feature reduction, based on sequential floating forward selection to remove the less informative features, and followed by a hierarchical SOM structure which reveals directly the diagnosis and probability assessment. The effectiveness of the proposed detection and identification scheme is validated with a complete set of experimental data including healthy and five faulty conditions. The accuracy’s results are compared with classical condition monitoring approaches in order to validate the competency of the proposed method.Peer ReviewedPostprint (author's final draft

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

Determinación del peso de reductores mecánicos de un paso utilizando ejes hueco

Los reductores mecánicos son herramientas comúnmente utilizadas para la transmisión de potencia en la industria, constantemente están sometidos a grandes esfuerzos pero a la vez deben ser livianos para acomodarse a las necesidades del medio y uso eficiente de material. El objetivo de este artículo es identificar la incidencia que tendrá la utilización de ejes huecos en el peso de una serie de reductores de transmisión de un paso, para este fin se ha considerado la geometría y variación de peso de cada uno de los elementos constitutivos de los reductores afectados por el cambio geométrico en los árboles de salida, de esta forma se pretende generar un criterio acerca de la conveniencia de utilizar ejes huecos en reductores mecánicos de un paso y cual es su impacto

Determinación del peso de reductores mecánicos de un paso utilizando ejes hueco

Los reductores mecánicos son herramientas comúnmente utilizadas para la transmisión de potencia en la industria, constantemente están sometidos a grandes esfuerzos pero a la vez deben ser livianos para acomodarse a las necesidades del medio y uso eficiente de material. El objetivo de este artículo es identificar la incidencia que tendrá la utilización de ejes huecos en el peso de una serie de reductores de transmisión de un paso, para este fin se ha considerado la geometría y variación de peso de cada uno de los elementos constitutivos de los reductores afectados por el cambio geométrico en los árboles de salida, de esta forma se pretende generar un criterio acerca de la conveniencia de utilizar ejes huecos en reductores mecánicos de un paso y cual es su impacto