Broken Bar Fault Detection Using Taylor-Fourier Filters and Statistical Analysis.

Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in electricity consumption, causing higher operative costs and losses related to the maintenance times or even the motor replacement if the damage has led to a complete failure. To prevent such situations, diverse signal processing algorithms have been applied to incipient fault detection, using different variables to analyze, such as vibrations, current, or flux. To counteract the broken rotor bar damage, this paper focuses on a motor current signal analysis for early broken bar detection and classification by using the digital Taylor-Fourier transform (DTFT), whose implementation allows fine filtering and amplitude estimation with the final purpose of achieving an incipient fault detection. The detection is based on an analysis of variance followed by a Tukey test of the estimated amplitude. The proposed methodology is implemented in Matlab using the O-splines of the DTFT to reduce the computational load compared with other methods. The analysis is focused on groups of 50-test of current signals corresponding to different damage levels for a motor operating at 50% and 75% of its full load.

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme.

BACKGROUND AND OBJECTIVE: The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. METHODS: The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. RESULTS: The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. CONCLUSIONS: The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.