No abstract

Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.

No abstract

No abstract

We have found that nanobacteria, recently discovered Gram-negative atypical bacteria, can cause local calciphylaxis on the mitral valve in a setting of high-calcium X phosphorous product in the blood. We present the case of a 33-year-old man with diabetic renal failure on continuous ambulatory peritoneal dialysis who died as a result of multiple brain infarcts due to embolizations from mitral valve vegetations. Systemic calciphylaxis was not present. Spectrometric analysis of the mitral valve vegetations showed that they were composed of calcium phosphate, carbonate apatite form, and fibrin. The electron microscopy of the thrombotic vegetation demonstrated nanobacterium as a nidus for carbonate apatite formation. Investigation for the presence of nanobacteria in the multiple organs involved in systemic calciphylaxis may be of help in elucidating the pathogenesis of this frequently fatal disorder.