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1. Field-Programmable Gate Array Architecture for the Discrete Orthonormal Stockwell Transform (DOST) Hardware Implementation.

Author

Valtierra-Rodriguez, Martin, Contreras-Hernandez, Jose-Luis, Granados-Lieberman, David, Rivera-Guillen, Jesus Rooney, Amezquita-Sanchez, Juan Pablo, and Camarena-Martinez, David

Subjects
PATTERN recognition systems, STANDARD deviations, GATE array circuits, SIGNAL processing, ARCHITECTURAL design
Abstract

Time–frequency analysis is critical in studying linear and non-linear signals that exhibit variations across both time and frequency domains. Such analysis not only facilitates the identification of transient events and extraction of key features but also aids in displaying signal properties and pattern recognition. Recently, the Discrete Orthonormal Stockwell Transform (DOST) has become increasingly utilized in many specialized signal processing tasks. Given its growing importance, this work proposes a reconfigurable field-programmable gate array (FPGA) architecture designed to efficiently implement the DOST algorithm on cost-effective FPGA chips. An accompanying MATLAB app enables the automatic configuration of the DOST method for varying sizes (64, 128, 256, 512, and 1024 points). For the implementation, a Cyclone V series FPGA device from Intel Altera, featuring the 5CSEMA5F31C6N chip, is used. To provide a complete hardware solution, the proposed DOST core has been integrated into a hybrid ARM-HPS (Advanced RISC Machine–Hard Processor System) control unit, which allows the control of different peripherals, such as communication protocols and VGA-based displays. Results show that less than 5% of the chip's resources are occupied, indicating a low-cost architecture that can be easily integrated into other FPGA structures or hardware systems for diverse applications. Moreover, the accuracy of the proposed FPGA-based implementation is underscored by a root mean squared error of 6.0155 × 10−3 when compared with results from floating-point processors, highlighting its accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Incipient Inter-Turn Short Circuit Detection in Induction Motors Using Cumulative Distribution Function and the EfficientNetv2 Model.

Author

Morales-Perez, Carlos Javier, Perez-Enriquez, Laritza, Amezquita-Sanchez, Juan Pablo, de Jesus Rangel-Magdaleno, Jose, Valtierra-Rodriguez, Martin, and Granados-Lieberman, David

Subjects
PATTERN recognition systems, SHORT circuits, INDUCTION motors, MECHANICAL loads, CONVOLUTIONAL neural networks, MECHANICAL failures, CUMULATIVE distribution function
Abstract

Induction motors are one of the most used machines because they provide the necessary traction force for many industrial applications. Their easy operation, installation, maintenance, and reliability make them preferred over other electrical motors. Mechanical and electrical failures, as with other machines, can appear at any stage of their service life, making the stator intern-turn short-circuit fault (ITSC) stand out. Hence, its detection is necessary in order to extend and save useful life, avoiding a breakdown and unprogrammed maintenance processes as well as, in the worst circumstances, a total loss of the machine. Nonetheless, the challenge lies in detecting this type of fault, which has made the analysis and diagnosis processes easier. Such is the case with convolutional neural networks (CNNs), which facilitate the development of methodologies for pattern recognition in several areas of knowledge. Unfortunately, these techniques require a large amount of data for an adequate training process, which is not always available. In this sense, this paper presents a new methodology for the detection of incipient ITSC faults employing a modified cumulative distribution function (CDF) of the current stator signal. Then, these are converted to images and fed into a fast and compact CNN model, trained with a small data set, reaching up to 99.16% accuracy for seven conditions (0, 5, 10, 15, 20, 30, and 40 short-circuited turns) and four mechanical load conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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9. APLICACIÓN DEL ESPECTROGRAMA MODIFICADO PARA LA IDENTIFICACIÓN DE MÚLTIPLES FALLOS COMBINADOS EN MOTORES DE INDUCCIÓN ALIMENTADOS POR INVERSORES (THE APPLICATION OF MODIFIED SPECTROGRAM FOR IDENTIFYING MULTIPLE COMBINED FAULTS IN INVERTER-FED INDUCTION MOTORS)

Author

Garcia Perez, Arturo, Valtierra Rodriguez, Martin, Camarena Martinez, David, Granados Lieberman, David, Garcia Perez, Arturo, Valtierra Rodriguez, Martin, Camarena Martinez, David, and Granados Lieberman, David

Abstract

Resumen Actualmente, las industrias utilizan motores de inducción alimentados con variadores de velocidad, los cuales generan componentes armónicos en la corriente del estator. Por lo tanto, es importante la detección y el diagnóstico temprano de fallas en el motor de inducción para su uso en el mantenimiento basado en condiciones. Sin embargo, la mayoría de los métodos se ocupan de un único fallo. La contribución de esta investigación es la aplicación de una estrategia de monitoreo de condición que puede realizar evaluaciones precisas y confiables de la presencia de condiciones de falla única o combinada en motores de inducción. El artículo presenta una descripción del estado del arte en el monitoreo de fallas y establece los métodos usados para la identificación de estas fallas, usando el método del espectrograma reasignado. Se analizan tres tipos de fallas y en los resultados pueden verse la adecuada identificación de estas usando espectros de tiempo-frecuencia. Los resultados muestran que el método del espectrograma reasignado podría utilizarse como técnica de detección determinista; donde las frecuencias de los fallos son muy cercanas a las reportadas analíticamente en la literatura. Palabras Clave: Monitoreo de la condición, diagnóstico de fallas, motores de inducción, espectrograma reasignado, análisis espectral. Abstract Currently, industries use induction motors fed with variable speed drives, which generate harmonic components in the stator current. Therefore, it is important early failure detection and diagnosis in induction motor for use in condition-based maintenance. However, most of the methods deal with a single fault, only. In electrical equipment with multiple faulty conditions present; it is critical to differentiate between the single or combined faulty conditions; so, it is important to differentiate between these. The contribution of this research is the application of a condition monitoring strategy that can make accurate and reliable assessme

Published
2022

32. Shannon Entropy and K-Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals.

Author

Camarena-Martinez, David, Valtierra-Rodriguez, Martin, Amezquita-Sanchez, Juan P., Granados-Lieberman, David, Romero-Troncoso, Rene J., and Garcia-Perez, Arturo

Subjects
SHANNON & Weaver's model (Communication), VIBRATION (Mechanics), K-means clustering, INDUCTION motors, ROTORS
Abstract

For industry, the induction motors are essential elements in production chains. Despite the robustness of induction motors, they are susceptible to failures. The broken rotor bar (BRB) fault in induction motors has received special attention since one of its characteristics is that the motor can continue operating with apparent normality; however, at certain point the fault may cause severe damage to the motor. In this work, a methodology to detect BRBs using vibration signals is proposed. The methodology uses the Shannon entropy to quantify the amount of information provided by the vibration signals, which changes due to the presence of new frequency components associated with the fault. For automatic diagnosis, the K-means cluster algorithm and a decision-making unit that looks for the nearest cluster through the Euclidian distance are applied. Unlike other reported works, the proposal can diagnose the BRB condition during startup transient and steady state regimes of operation. Additionally, the proposal is also implemented into a field programmable gate array in order to offer a low-cost and low-complex online monitoring system. The obtained results demonstrate the proposal effectiveness to diagnose half, one, and two BRBs. [ABSTRACT FROM AUTHOR]

Published
2016
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