Roque Alfredo Osornio-Rios

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

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.

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

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.

FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors Through Information Entropy and Fuzzy Inference

The development of monitoring systems for rotating machines is the ability to accurately detect different faults in an incipient state. The most popular rotating machine in industry is the squirrel-cage induction motor, and the failure on such motors may have severe consequences in costs, product quality, and safety. Most of the condition-monitoring techniques for induction motors focus on a single specific fault. The identification of two or more combined faults has been rarely considered, in spite of being a very usual situation in real rotary machines. On the other hand, information entropy is a signal processing technique that has recently proved its suitability for fault detection on induction motors, and fuzzy logic analysis has extensively been used in combination with several processing techniques in improving the diagnosis of a single isolated fault. The contribution of this paper is a novel methodology that is suitable for hardware implementation, which merges information entropy analysis with fuzzy logic inference to identify faults like bearing defects, unbalance, broken rotor bars, and combinations of faults by analyzing one phase of the induction motor steady-state current signal. The proposed methodology shows satisfactory results that prove its suitability for online detection of single and multiple combined faults in an automatic way through its hardware implementation in a field programmable gate array device.

Novel Methodology for Online Half-Broken-Bar Detection on Induction Motors

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but is also how early these failures can be detected. Squirrel-cage induction motors are the most popular motors used in industry, consuming around 85% of the power in industrial plants. Broken rotor bars in induction motors are among the major failures that are desirable to detect at early stages because this failure significantly increases power consumption and is responsible for further damage to the machinery. Previously reported works base their analysis on current or vibration monitoring for broken-bar detection up to one broken bar under mechanically loaded motor conditions. The contribution of this paper presents a novel methodology for half-broken-bar detection, which combines current and vibration analysis by correlating the signal spectra to enhance detectability for mechanically loaded and unloaded operating conditions of the motor, which the other isolated techniques are unable to detect. The proposed methodology is implemented in a low-cost field-programmable gate array (FPGA), giving a special-purpose system-on-a-chip (SoC) solution for online operation, with the development of a complex postprocessing decision-making unit. Several cases of study are presented to demonstrate the performance of the implementation.

MUSIC-ANN Analysis for Locating Structural Damages in a Truss-Type Structure by Means of Vibrations

This article will present a methodology for damage detection, location, and quantification based on vibration signature analysis and a comprehensive experimental study to assess the utility of the proposed structural health monitoring applied to a five-bay truss-type structure. The MUltiple SIgnal Classification (MUSIC) algorithm introduced first by Jiang and Adeli for health monitoring of structures in 2007 is fused with artificial neural networks (ANN) for an automated result. The developed methodology is based on feeding the amplitude of the natural frequencies as input of an artificial neural network, being the novelty of the proposed methodology its ability to identify, locate, and quantify the severity of damages with precision such as: external and internal corrosion and cracks in an automated monitoring process. Results show the proposed methodology is effective for detecting a healthy structure, a structure with external and internal corrosion, and a structure with crack. Therefore, the proposed fusion of MUSIC-ANN algorithms can be regarded as a simple, effective, and automated tool without requiring sophisticated equipment. The algorithms are moving toward establishing a practical and reliable structural health monitoring methodology, which will help in evaluating the condition of the structure in order to detect damages early and to make the corresponding maintenance decisions in the structures.

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

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 Real-Time Smart Sensor for High-Resolution Frequency Estimation in Power Systems

Power quality monitoring is a theme in vogue and accurate frequency measurement of the power line is a major issue. This problem is particularly relevant for power generating systems since the generated signal must comply with restrictive standards. The novelty of this work is the development of a smart sensor for real-time high-resolution frequency measurement in accordance with international standards for power quality monitoring. The proposed smart sensor utilizes commercially available current clamp, hall-effect sensor or resistor as primary sensor. The signal processing is carried out through the chirp z-transform. Simulations and experimental results show the efficiency of the proposed smart sensor.

Open-architecture system based on a reconfigurable hardware-software multi-agent platform for CNC machines

New generation of manufacturing systems endows their intelligence and reconfigurability to the computerized numerical controller (CNC) machines. This paper presents an open-architecture platform based on multi-agent hardware-software units, by developing a novel Multi-Agent Distributed CONtroller (MADCON) system. This system intends to fulfill the requirements of reconfigurability for the next generation of intelligent machines. The design of intelligent drives for this system follows a hardware-software co-design approach using a simple and intuitive structure. The hardware units of the proposed system integrate control and monitoring functions providing an FPGA-based open architecture for reconfigurable applications. On the other hand, software components were developed utilizing the XML structure for system description files, gathering features like a flowchart descriptive language and a graphic user-interface. MADCON was applied to a retrofitted to CNC lathe for control and monitoring in order to validate the proposed architecture towards the development of new generation intelligent manufacturing systems.

FPGA-Based Fused Smart Sensor for Dynamic and Vibration Parameter Extraction in Industrial Robot Links

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).

FPGA-based fused smart-sensor for tool-wear area quantitative estimation in CNC machine inserts.

Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used.