Carlos A. Perez-Ramirez

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.

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.

Compact kernel distribution-based approach for broken bars detection on induction motors

Induction motors, important elements into the industry, are susceptible to faults during its lifetime service; yet, they can keep working without affecting the process, but increasing the production costs as they consume more electrical current. Broken rotor bars (BRB) detection is an important topic due to the fact that this failure is silent and produces a power consumption increasing, vibration, or introduction of spurious frequencies in the electric line, among others. In this regard, a monitoring system that can efficiently diagnose the induction motor condition is highly required. In this work, a new methodology for one and two broken bars detection is presented. First, the compact kernel distribution (CKD) algorithm, a new high resolution time-frequency algorithm, is introduced for the detection of anomalies produced by the BRB failure in the startup current signal by considering that these signals describe changes on its dynamic characteristics due to the fault; then, the variance, a statistical feature, of the signal processed by CKD determines in automatic way the induction motor condition. The obtained results show a high overall efficiency for detecting broken rotor bars as well as healthy condition.

Tracking of voltage variations by means of an adaptive filter and fuzzy logic

Monitoring of voltage variations is a demanding issue for academic and industrial fields due mainly to their negative impact on equipment. In this work, a methodology based on adaptive filter using the least mean squares algorithm for tracking of voltage variations and a fuzzy logic system for automatic classification are proposed. The proposal consists of three stages: 1) denoising through a lowpass filter to remove non-fundamental frequency components, 2) envelope and type of voltage tracking, and 3) final classification according to the IEEE Std. 1159 using a rule-based decision process. In order to validate and test the proposal, a set of synthetic and real signals is used. The obtained results demonstrate the proposal effectiveness to detect and classify voltage variations, even when they are embedded in high level noise. Unlike other reported works, the proposed fuzzy logic system allows the tracking of the voltage variation such as sag, swell, or interruption over time, it means sample to sample.

Fractal dimension theory-based approach for bearing fault detection in induction motors

Induction motors, vital elements into the industry, are more likely to be influenced by different faults during their lifetime service. Even when they can keep working without affecting the line processes, in most cases, an increase in the production costs usually occurs. Bearing fault detection is an important topic due to the fact that this failure yields an increase in both vibration and temperature, among others, which can produce in other systems joined to the induction motor similar issues. In this regard, a monitoring system capable of detecting bearing fault in the induction motor condition is desirable in industry. In this work, a new methodology based on fractal dimension theory, a concept from the chaos theory, for outer race bearing defect (OBD) detection is presented. The fractal dimension (FD) theory is introduced for the detection of anomalies produced by OBD in the steady-state vibration signal of an induction motor, since this signal might have subtle changes on its dynamic characteristics due to the fault. The obtained results show that, as expected, the measured signal has the assumed changes, leading to have a methodology with a higher overall efficiency for distinguishing the fault and the heathy states.

The application of EMD methods to power quality signals

Over the past few years, power quality (PQ) monitoring has become an important topic because of the negative impact of different machines to the electrical network and to the susceptibility of critical equipment. There are different disturbances that affect the PQ; therefore, in order to apply a proper solution, these have to be correctly detected and classified. In general, signal processing techniques are applied for their detection. Recently, several approaches based on empirical mode decomposition (EMD) method have been reported; however, the selection of the best-suited method in terms of processing and performance for a particular case can be a complicated decision-making process. In this paper, a quantitative and qualitative comparative study using EMD methods such as conventional EMD, ensemble EMD, and complete ensemble EMD is presented. The study is applied to synthetic and real PQ signals, in which aspects of the computational cost and decomposition accuracy are discussed.

Dynamic Behavior Modeling of Civil Structures Using Wavenets and Neural Networks: A Comparative Study

Civil structures are known for having a non-linear and time-variant behavior, these features make a challenging task the use of linear methods for modeling the dynamical behavior since they only model time-invariant systems. To overcome this limitation, several approaches based on non-parametric methods have been proposed, however, the selection of the best-suited method for a particular case can be a complicated decision-making process. In this paper, a comparison between dynamic neural networks and wave nets for modeling the dynamic response of a five-bay space truss structure is presented, by using the structure response to a chirp signal, the models are created. Then, the root mean squared value (RMSE) is employed for determining the model that best approximates the dynamic behavior. An experimental study is carried out in order to validate the models efficiency and their accuracy.