Pierre Granjon

Bio: Pierre Granjon is an academic researcher from University of Grenoble. The author has contributed to research in topics: Condition monitoring & Fault (power engineering). The author has an hindex of 15, co-authored 58 publications receiving 1455 citations. Previous affiliations of Pierre Granjon include Centre national de la recherche scientifique & Grenoble Institute of Technology.

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Abstract: This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

Models for bearing damage detection in induction motors using stator current monitoring

Abstract: This paper describes new models for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on two effects of a bearing fault: the introduction of a particular radial rotor movement and load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

Fast and automatic processing of multi-level events in nanopore translocation experiments

Abstract: We have developed a method to analyze in detail, translocation events providing a novel and flexible tool for data analysis of nanopore experiments. Our program, called OpenNanopore, is based on the cumulative sums algorithm (CUSUM algorithm). This algorithm is an abrupt change detection algorithm that provides fitting of current blockages, allowing the user to easily identify the different levels in each event. Our method detects events using adaptive thresholds that adapt to low-frequency variations in the baseline. After event identification, our method uses the CUSUM algorithm to fit the levels inside every event and automatically extracts their time and amplitude information. This facilitates the statistical analysis of an event population with a given number of levels. The obtained information improves the interpretation of interactions between the molecule and nanopore. Since our program does not require any prior information about the analyzed molecules, novel molecule-nanopore interactions can be characterized. In addition our program is very fast and stable. With the progress in fabrication and control of the translocation speed, in the near future, our program could be useful in identification of the different bases of DNA.

Space Vector Method for Voltage Dips and Swells Analysis

Abstract: A new method for voltage dips and swells analysis is presented in this paper. This method is based on the space vector representation in the complex plane and the zero-sequence voltage. Indeed, in the case of nonfaulted system voltages, the space vector follows a circle in the complex plane with a radius equal to the nominal voltage. It follows the same shape for balanced dips, but with a smaller radius. For unbalanced dips, this shape becomes an ellipse with parameters depending on the phase(s) in drop, dip magnitude and phase angle shift. For swells the space vector shape is not modified, though the zero-sequence voltage presents significant changes in its phase and magnitude and can be used for swells analysis. The changes in the space vector and the zero-sequence voltage are used to determine the dip/swell time occurrence, to classify and finally characterize the measured power-quality disturbance. Algorithms are developed for each step of this automatic voltage dips and swells analysis (segmentation, classification, and characterization) and are validated on real measurement data.

Spectral kurtosis: from definition to application

Abstract: The objective of this paper is to present the Spectral Kurtosis (SK) as a spectral analysis tool complementary to the classical power spectrum density (PSD). Theoretical definition of the SK is firstly justified, followed by a study of its behavior with respect to the spectral components of signals. An unbiased estimator is thus proposed, with its exact variance, and its signal to noise ratio sensivity. Finally, results are illustrated by applying SK and PSD on synthetic and real signals.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Advances in Diagnostic Techniques for Induction Machines

Abstract: This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.