Search results for "Isolation"
showing 10 items of 582 documents
Winding loss analysis and optimization of an AC inductor for a galvanically isolated PV inverter
2012
This paper describes simple treatments for fringing fields of air gaps in the core and bypass fluxes in the winding window area of an existing high frequency AC inductor used by a commercial developer of PV inverters. For this purpose, Maxwell (ANSOFT) electromagnetic software package is used for winding eddy current loss analysis. It is displayed that air gaps cause high flux strength and, therefore, induce significantly high eddy currents to the surrounded windings. Proximity effect also causes non uniform current density in the winding. Altogether, the inductor is affected by fringing fields, and proximity effect produces a very high AC resistance, consequently resulting in undesirable h…
New Alkenylresorcinols with Cytotoxic and Antimicrobial Activities from the Leaves of Embelia schimperi.
2020
AbstractA phytochemical study of the methanol extract of the leaves of Embelia schimperi resulted in the isolation of three new alkenylresorcinols, 1 – 3, together with the known analogs 4 – 7. Their structures were established by a combination of spectroscopic techniques. Compounds 1 – 7 exhibited moderate cytotoxic activity against human cervical cancer cells HeLa-S3 and more pronounced antimicrobial properties towards bacteria and filamentous fungi. The present study falls into an ongoing research project on the characterization of bioactive phenolic lipids from plants of the family Primulaceae.
Broken rotor bars detection via Park's vector approach based on ANFIS
2014
Many attempts have been made on fault diagnosis of induction motors based on frequency and time domain analysis of stator current. In this paper, first the Park's vector transformation and frequency analysis for fault detection of induction motors are introduced. Then a smart approach using Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed. This approach uses the time domain features derived from the Park's vector transformation of stator current. By the proposed method, a partial break including 5 mm crack on a bar, one broken bar and two broken bars using experimental data are investigated. It will be shown that features derived from Park's vector compared to features obtained fro…
Fault Detection of Networked Control Systems Based on Sliding Mode Observer
2013
Published version of an article in the journal: Mathematical Problems in Engineering. Also availeble from the publisher at: http://dx.doi.org/10.1155/2013/506217 Open Access This paper is concerned with the network-based fault detection problem for a class of nonlinear discrete-time networked control systems with multiple communication delays and bounded disturbances. First, a sliding mode based nonlinear discrete observer is proposed. Then the sufficient conditions of sliding motion asymptotical stability are derived by means of the linear matrix inequality (LMI) approach on a designed surface. Then a discrete-time sliding-mode fault observer is designed that is capable of guaranteeing the…
New Procedures of Pattern Classification for Vibration-Based Diagnostics via Neural Network
2014
In this paper, the new distance-based embedding procedures of pattern classification for vibration-based diagnostics of gas turbine engines via neural network are proposed. Diagnostics of gas turbine engines is important because of the high cost of engine failure and the possible loss of human life. Engine monitoring is performed using either ‘on-line’ systems, mounted within the aircraft, that perform analysis of engine data during flight, or ‘off-line’ ground-based systems, to which engine data is downloaded from the aircraft at the end of a flight. Typically, the health of a rotating system such as a gas turbine is manifested by its vibration level. Efficiency of gas turbine monitoring s…
Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm
2017
Online bearing fault detection is an important method for monitoring the health status of bearings in critical machines. This work proposes a classification algorithm, which can be extended towards an online bearing fault detection. The objective is to detect and classify the bearing faults in early stages. The overall design aspects of the online bearing fault detection and classification system are discussed. The proposed method is validated using experimental data, and a high accuracy of the fault classification was observed. Therefore, the proposed method can be applied for an online early fault detection and classification system.
Optimization and finite-frequency H ∞ control of active suspensions in in-wheel motor driven electric ground vehicles
2015
Abstract In this paper, the parameter optimization and H ∞ control problem of active suspensions equipped in in-wheel motor driven electric ground vehicles are investigated. In order to better isolate the force transmitted to motor bearing, dynamic vibration absorber (DVA) is installed in the active suspension. Parameters of the vibration isolation modules are also optimized in order to achieve better suspension performances. As the human body is much sensitive to vibrations between 4 and 8 Hz, a finite-frequency state-feedback H ∞ controller is designed to achieve the targeted disturbance attenuation in the concerned frequency range while other performances such as road holding capability …
Rotor-bar breakage mechanism and prognosis in an induction motor
2015
© 1982-2012 IEEE. This paper proposes a condition-based maintenance and prognostics and health management (CBM/PHM) procedure for a rotor bar in an induction motor. The methodology is based on the results of a fatigue test intended to reproduce in the most natural way a bar breakage in order to carry out a comparison between transient and stationary diagnosis methods for incipient fault detection. Newly developed techniques in stator-current transient analysis have allowed tracking the developing fault during the last part of the test, identifying the failure mechanism, and establishing a physical model of the process. This nonlinear failure model is integrated in a particle filtering algor…
Vibration analysis for bearing fault detection and classification using an intelligent filter
2014
Abstract This paper proposes an intelligent method based on artificial neural networks (ANNs) to detect bearing defects of induction motors. In this method, the vibration signal passes through removing non-bearing fault component (RNFC) filter, designed by neural networks, in order to remove its non-bearing fault components, and then enters the second neural network that uses pattern recognition techniques for fault classification. Four different categories include; healthy, inner race defect, outer race defect, and double holes in outer race are investigated. Compared to the regular fault detection methods that use frequency-domain features, the proposed method is based on analyzing time-d…
Application of latent nestling method using Coloured Petri Nets for the Fault Diagnosis in the wind turbine subsets
2008
This paper presents an application example using the lating nestling method for the fault diagnosis based in the use of coloured Petri nets, to a lubrication and cooling system in the wind turbinepsilas gearbox with a critical subsystem as far as failure probability. It demonstrate the synthesis capacity of the method for any model of diagnosis and isolation, giving as opposed to know the contributed advantages other methodologies, as those based in finite state machine.