Juan Manuel Ramirez-Cortes

FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology

Broken bars detection on induction motors has been a topic of interest in recent years. Its detection is important due to the fact that the failure is silent and the consequences it produces as power consumption increasing, vibration, introduction of spurious frequencies in the electric line, among others, can be catastrophic. In this paper, the use of motor current signature analysis and mathematical morphology to detect broken bars on induction motors under different mechanical load condition is analyzed. The proposed algorithm first identifies the motor load and then the motor condition. The statistical analysis of several tests under different motor loads (100%, 75%, 50%, and 25%) and motor condition (healthy, one broken bar, and two broken bars) is presented. The proposed method has been implemented in a field programmable gate array, to be used in real-time online applications. The algorithm obtained in average a 95% accuracy of failure detection.

Quantitative Estimation of Temperature Variations in Plantar Angiosomes: A Study Case for Diabetic Foot

Thermography is a useful tool since it provides information that may help in the diagnostic of several diseases in a noninvasive and fast way. Particularly, thermography has been applied in the study of the diabetic foot. However, most of these studies report only qualitative information making it difficult to measure significant parameters such as temperature variations. These variations are important in the analysis of the diabetic foot since they could bring knowledge, for instance, regarding ulceration risks. The early detection of ulceration risks is considered an important research topic in the medicine field, as its objective is to avoid major complications that might lead to a limb amputation. The absence of symptoms in the early phase of the ulceration is conceived as the main disadvantage to provide an opportune diagnostic in subjects with neuropathy. Since the relation between temperature and ulceration risks is well established in the literature, a methodology that obtains quantitative temperature differences in the plantar area of the diabetic foot to detect ulceration risks is proposed in this work. Such methodology is based on the angiosome concept and image processing.

On Signal P-300 Detection for BCI Applications Based on Wavelet Analysis and ICA Preprocessing

This paper describes an experiment on the detection of a P-300 rhythm from electroencephalographic signals for brain computer interfaces applications. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected through a time-scale analysis based on the discrete wavelet transform (DWT). Comparison using the Short Time Fourier Transform (STFT), and Wigner–Ville Distribution (WVD) indicates that the DWT outperforms the others as an analyzing tool for P300 rhythm detection.

Wavelet-based smoke detection in outdoor video sequences

In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application.

A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series

The accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster when the model is built using data generated by highly dynamic or chaotic systems. This paper presents a topology and training scheme for a novel artificial neural network, named “Hybrid-connected Complex Neural Network” (HCNN), which is able to capture the dynamics embedded in chaotic time series and to predict long horizons of such series. HCNN is composed of small recurrent neural networks, inserted in a structure made of feed-forward and recurrent connections and trained in several stages using the algorithm back-propagation through time (BPTT). In experiments using a Mackey-Glass time series and an electrocardiogram (ECG) as training signals, HCNN was able to output stable chaotic signals, oscillating for periods as long as four times the size of the training signals. The largest local Lyapunov Exponent (LE) of predicted signals was positive (an evidence of chaos), and similar to the LE calculated over the training signals. The magnitudes of peaks in the ECG signal were not accurately predicted, but the predicted signal was similar to the ECG in the rest of its structure.

Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but also how early these failures can be detected. The purpose of this paper is to present an experimental study of partially damaged rotor bar in induction motor under different load conditions based on discrete wavelet transform analysis. The approach is based on the extraction of features from vibration signals at different level of damage and three mechanical load conditions. The proposed analysis is reliable for tracking the damage in rotor bar. The paper presents an analysis and extraction of vibration features for partially damaged rotor bar in induction motors. The experimental analysis shows the change in behavior of vibration due to load condition and progressive damage.

Shape-based hand recognition approach using the morphological pattern spectrum

We propose the use of the morphological pattern spec- trum, or pecstrum, as the base of a biometric shape-based hand recognition system. The system receives an image of the right hand of a subject in an unconstrained pose, which is captured with a commercial flatbed scanner. According to pecstrum property of in- variance to translation and rotation, the system does not require the use of pegs for a fixed hand position, which simplifies the image acquisition process. This novel feature-extraction method is tested using a Euclidean distance classifier for identification and verifica- tion cases, obtaining 97% correct identification, and an equal error rate (EER) of 0.0285 (2.85%) for the verification mode. The obtained results indicate that the pattern spectrum represents a good feature- extraction alternative for low- and medium-level hand-shape-based biometric applications.

Broken bars detection on induction motor using MCSA and mathematical morphology: An experimental study

Currently, industry demands early failure detection on his processes, machines, production lines, etc. One of the most widely used motors in industry is the induction motor. A common induction motor failure is the broken bars. It is well know that broken bars produce spurious frequencies around the supply frequency. Moreover, the amplitude of the spurious frequencies in the sideband of the main frequency is sensitive to the number of broken bars. In this paper a real-time pre-processing methodology to enhance detectability for broken bar detection using motor current signature analysis and mathematical morphology is presented. The proposed methodology is implemented into a low cost FPGA. A statistical analysis is presented in order to demonstrate the detection improvement.

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the users blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

Anfis-Based P300 Rhythm Detection Using Wavelet Feature Extraction on Blind Source Separated Eeg Signals

An experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented.