Juan P. Amezquita-Sanchez

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.

Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures

A new methodology is presented for (a) detecting, (b) locating, and (c) quantifying the damage severity in a smart highrise building structure. The methodology consists of three steps: In step 1, the synchrosqueezed wavelet transform is used to eliminate the noise in the signals. In step 2, a nonlinear dynamics measure based on the chaos theory, fractality dimension (FD), is employed to detect features to be used for damage detection. In step 3, a new structural damage index, based on the estimated FD values, is proposed as a measure of the condition of the structure. Further, the damage location is obtained using the changes of the estimated FD values. Three different FD algorithms for computing the fractality of time series signals are investigated. They are Katzs FD, Higuchis FD, and box dimension. The usefulness and effectiveness of the proposed methodology are validated using the sensed data obtained experimentally for the 1:20 scaled model of a 38-storey concrete building structure.

New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet transform

Abstract Many applications related to modeling, control and condition assessment of smart structures require an accurate identification of natural frequencies and damping ratios. This identification is generally carried out through artificial and natural vibration sources. The latter is often preferred in many situations; yet their analysis represents a challenge since the measured data are non-stationary with a high noise level. In this paper, a new methodology is proposed based on the synchrosqueezed wavelet transform (SWT). First, the random decrement technique (RDT) is applied to estimate the free vibration response from measured ambient vibration signals. Then, the SWT algorithm is used to decompose the vibration response into individual mode components. Finally, the Hilbert transform (HT) and the Kalman filter (KF) are used to estimate the natural frequencies and damping ratios of each mode and to filter and smoothen the results. The effectiveness of the proposed approach is first validated through numerical simulation of damped free vibration response of a 3-degree of freedom (DOF) system with two closely-spaced frequencies. Then, numerical and experimental data of a benchmark 4-story 2×2 bay 3D steel frame structure subjected to ambient vibrations is analyzed. Finally, the natural frequencies and damping ratios of a real-life bridge located in Queretaro, Mexico are obtained. For comparison purposes, two recent and advanced signal processing techniques, the complete ensemble empirical mode decomposition (CEEMD) technique and the short-time multiple signal classification (ST-MUSIC) are also tested. Numerical and experimental results show accurate identification of the natural frequencies and damping ratios even when the signal is embedded in high-level noise demonstrating that the proposed methodology provides a powerful approach to estimate the modal parameters of a civil structure using ambient vibration excitations.

A new music-empirical wavelet transform methodology for time-frequency analysis of noisy nonlinear and non-stationary signals

The goal of signal processing is to estimate the contained frequencies and extract subtle changes in the signals. In this paper, a new adaptive multiple signal classification-empirical wavelet transform (MUSIC-EWT) methodology is presented for accurate time-frequency representation of noisy non-stationary and nonlinear signals. It uses the MUSIC algorithm to estimate the contained frequencies in the signal and build the appropriate boundaries to create the wavelet filter bank. Then, the EWT decomposes the time-series signal into a set of frequency bands according to the estimated boundaries. Finally, the Hilbert transform is applied to observe the evolution of calculated frequency bands over time. The usefulness and effectiveness of the proposed methodology are validated using two simulated signals and an ECG signal obtained experimentally. The results demonstrate clearly that the proposed methodology is immune to noise and capable of estimating the optimal boundaries to isolate the frequencies from noise and estimate the main frequencies with high accuracy, especially the closely-spaced frequencies.

Vibration Control on Smart Civil Structures: A Review

Smart civil structures are capable of partially compensating the undesirable effects due to external perturbations; they sense and react to the environment in a predictable and desirable form through the integration of several elements, such as sensors, actuators, signal processors, and power sources, working with control strategies. This article will focus on reviewing the main control techniques applied to suppress vibrations in civil structures using smart materials, remarking on the advantages and disadvantages of smart actuators and control strategies tendencies in smart civil structures.

Novel Downsampling Empirical Mode Decomposition Approach for Power Quality Analysis

The development and application of techniques and methodologies for the analysis of power quality (PQ) signals that offer a more efficient and reliable analysis in terms of processing and performance are still issues for industrial and academic fields, mainly considering the quick growing of the PQ data in modern power systems. In this regard, an iterative downsampling stage fused to the empirical mode decomposition (EMD) method is proposed. It is validated and tested using synthetic and real signals. In general, the aim of the proposed method is to extract the fundamental component as the first intrinsic mode function (IMF) to simplify the remaining decomposition. The proposed method is compared to the classical EMD and the ensemble EMD (EEMD). Advantages of the proposed method include a more adequate IMF extraction than the EMD technique, and a noticeable reduction of computational burden compared to the EEMD. The results obtained from the synthetic and real signals demonstrate the reliability and efficiency of the proposed method.

A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG).

Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, greater than expected by age. A new methodology is presented to identify MCI patients during a working memory task using MEG signals. The methodology consists of four steps: In step 1, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose the MEG signal into a set of adaptive sub-bands according to its contained frequency information. In step 2, a nonlinear dynamics measure based on permutation entropy (PE) analysis is employed to analyze the sub-bands and detect features to be used for MCI detection. In step 3, an analysis of variation (ANOVA) is used for feature selection. In step 4, the enhanced probabilistic neural network (EPNN) classifier is applied to the selected features to distinguish between MCI and healthy patients. The usefulness and effectiveness of the proposed methodology are validated using the sensed MEG data obtained experimentally from 18 MCI and 19 control patients.

High-resolution spectral-analysis for identifying the natural modes of a truss-type structure by means of vibrations

Nowadays, the system identification methods applied to civil structures are rising in order to get a better understanding of structural behavior and improve traditional analytical analysis. Accurate identification of the modal parameters of a structure is essential because it allows building a proper analytical model, and it discloses the difficulties that may not have been considered in analytical studies, as well as finding out the existence of structural damages or deterioration, and sometimes estimating the remaining life of the structure. A clear disadvantage of most experimental methodologies is to require of a long sampling time window that stresses the structure under test. This paper shows the effectiveness of a novel methodology based on the multiple signal classification (MUSIC) algorithm and its high-resolution properties, applied for identifying most of the natural modes and analyzing vibration signals in a truss-type structure by using a reduced sample data set and short sampling time window. It has the advantage of submitting the structure to a reduced fatigue and stress during testing as a difference from other works, where the analysis involves putting the structures under severe fatigue and stress. Identifying most of the natural modes in the truss-type structure is realized at first by locating the fundamental mode in a frequency region, and the other natural modes are identified in higher frequencies, where each of these natural modes is located in different frequency regions. Thus, the MUSIC algorithm can identify most of the natural modes in different frequency regions of a vibration signal successfully.

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

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 -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.

Fractal dimension-based approach for detection of multiple combined faults on induction motors

Induction motors, key elements for industry, are susceptible to one or more faults at the same time; yet, they can keep working without affecting the process, but increasing the production costs. For this reason, a monitoring system that can efficiently diagnose the induction motor condition, even under multiple combined faults, is a demanding task. In this work, a methodology and its implementation into a field programmable gate array for an online and real-time monitoring system of multiple combined faults are presented. First, the fractal dimension approach, using the Katz algorithm, is introduced as a measure of variation of 3-axis startup vibration signals for the induction motor condition, considering that these signals describe changes on its dynamic characteristics due to the different faults. Then, an artificial neural network determines in an automatic way the induction motor condition according to the fractal dimension values. The obtained results show a higher overall efficiency than previous works for detecting broken rotor bars, outer-race bearing defects, unbalance, and their combinations, as well as a healthy condition.