Arturo Garcia-Perez

Bio: Arturo Garcia-Perez is an academic researcher from Universidad de Guanajuato. The author has contributed to research in topics: Induction motor & Fault detection and isolation. The author has an hindex of 23, co-authored 95 publications receiving 2286 citations.

Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks

Abstract: The detection and classification of power quality (PQ) disturbances have become a pressing concern due to the increasing number of disturbing loads connected to the power line and the susceptibility of certain loads to the presence of these disturbances; moreover, they can appear simultaneously since, in any real power system, there are multiple sources of different disturbances. In this paper, a new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting, on the one hand, of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices. With these indices, it is possible to detect and classify sags, swells, outages, and harmonics-interharmonics. On the other hand, a feedforward neural network for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform can classify spikes, notching, flicker, and oscillatory transients. The combination of the aforementioned neural networks allows the detection and classification of all the aforementioned disturbances even when they appear simultaneously. An experiment under real operating conditions is carried out in order to test the proposed methodology.

Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation

Abstract: Overall system performance on a production line is one of the major concerns in modern industry where induction motors are present and their condition monitoring is mandatory. Periodic offline monitoring of the motor condition is usually performed in the industry, consuming production time and increasing cost. Broken rotor bars are among the most common failures in induction motors. Reported research projects give a broken-rotor-bar-detection methodology based on personal-computer implementation that is performed offline and requires an expert technician interpretation which is not a cost-effective solution. The novelty of this paper is the development of an automatic online diagnosis algorithm for broken-rotor-bar detection, optimized for single low-cost field-programmable gate-array (FPGA) implementation, which guarantees the development of economical self-operated equipment. The proposed algorithm requires less computation load than the previously reported algorithms, and it is mainly based on the discrete-wavelet-transform application to the start-up current transient; a further single mean-square computation determines a weighting function that, according to its value, clearly points the motor condition as either healthy or damaged. In order to validate the proposed algorithm, several tests were performed, and an FPGA implementation was developed to show the algorithm feasibility for automatic online diagnosis.

The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors

Abstract: Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified; however, they generally deal with a single fault only. Instead, in real induction machines, the case of multiple faults is common. When multiple faults exist, vibration and current are excited by several fault-related frequencies combined with each other, linearly or nonlinearly. Different techniques have been proposed for the diagnosis of rotating machinery in literature, where most of them are focused on detecting single faults and few works deal with the diagnosis and identification of multiple combined faults. The contribution of this paper is the development of a condition-monitoring strategy that can make accurate and reliable assessments of the presence of specific fault conditions in induction motors with single or multiple combined faults present. The proposed method combines a finite impulse response filter bank with high-resolution spectral analysis based on multiple signal classification for an accurate identification of the frequency-related fault. Results show the methodology potentiality as a deterministic detection technique that is suited for detecting multiple features where the fault-related frequencies are very close to those analytically reported in literature.

Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review

Abstract: The relevance of power quality (PQ) issues has recently augmented because of the increased use of power electronic equipment, which results in a voltage deviation and current waveforms. The PQ monitoring is covered by two main subjects: the development of PQ indices to quantify the power supply quality and the electrical disturbances detection such as harmonics, sags, swells etc., which allows knowing the conditions of the electric power systems. In this study a review of techniques and methodologies developed for PQ analysis and power disturbance classification is presented in order to show their major characteristics.

Reconfigurable Monitoring System for Time-Frequency Analysis on Industrial Equipment Through STFT and DWT

Abstract: Nowadays industry pays much attention to prevent failures that may interrupt production with severe consequences in cost, product quality, and safety. The most-analyzed parameters for monitoring dynamic characteristics and ensuring correct functioning of systems are electric current, voltage, and vibrations. System-on-chip (SoC) design is an approach to increase performance and overcome costs during equipment monitoring. This work presents the design and implementation of a low-cost SoC design that utilizes reconfigurable hardware and a customized embedded processor for time-frequency analysis on industrial equipment through short-time Fourier transform and discrete wavelet transform. Three study cases (electric current supply to an induction motor during startup transient, voltage supply to an induction motor through a variable speed drive, and vibration signals from industrial-robot links) show the suitability of the proposed monitoring system for time-frequency analysis of different signals in distinct industrial applications, and early diagnosis and prognosis of abnormalities in monitored systems.

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Abstract: With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

[서평]「Computer Organization and Design, The Hardware/Software Interface」

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches

Abstract: This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on fault diagnosis techniques and their applications. Comments on the advantages and constraints of various diagnosis techniques, including model-based, signal-based, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of fault diagnosis is presented.