Search results for "Power engineering"
showing 10 items of 126 documents
CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features
2021
Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the…
A Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms Using Support Vector Machine
2019
Pitch systems are one of the components with the most frequent failure in wind turbines. This paper presents a two-stage fault detection and classification scheme for electric motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms with electric pitch systems driven by induction motors as well as permanent magnet synchronous motors. The adopted strategy utilizes three-phase motor current sensing at the pitch drives for fault detection and only when a fault condition is detected at this stage, features extracted from the current signals are transmitted to a support vector machine classifier located centrally to the wind farm. The …
Automatic detection of thermal anomalies in induction motors
2021
The paper proposes a methodology based on Artificial Intelligence techniques for the automatic detection of abnormal thermal distributions in electric motors, to rapidly identify pre-faults or fault conditions. The proposed approach, applied to induction motors of different sizes, installed in waterworks plants, is based on the execution of Thermographic Non-Destructive Tests, which allow identifying abnormal operating conditions without interrupting the ordinary working conditions of the system. Thermographic images of induction motors are acquired at the installation site and with perspectives visible to the operator, which are sometimes partially obstructed. These thermographic images ar…
Konferenz der Nationalen Komitees der Weltkraftkonferenz Lettlands, Estlands und Litauens: [Vorträge]
1939
Kopsavilkumi angļu valodā
Elektromašīnas, 1. daļa: Transformators [lekciju konspekts]
1941
Konspekts ar rokraksta tiesībām. Lasīts Latvijas Valsts Universitātes Mehānikas fakultātē.
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…
Fault Tolerant Ancillary Function of Power Converters in Distributed Generation Power System within a Microgrid Structure
2013
Distributed generation (DG) is deeply changing the existing distribution networks which become very sophisticated and complex incorporating both active and passive equipment. The simplification of their management can be obtained assuming a structure with small networks, namely, microgrids, reproducing, in a smaller scale, the structure of large networks including production, transmission, and distribution of the electrical energy. Power converters in distributed generation systems carry on some different ancillary functions as, for example, grid synchronization, islanding detection, fault ride through, and so on. In view of an optimal utilization of the generated electrical power, fault to…
Robust Redundant Input Reliable Tracking Control for Omnidirectional Rehabilitative Training Walker
2014
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/636934 The problem of robust reliable tracking control on the omnidirectional rehabilitative training walker is examined. The new nonlinear redundant input method is proposed when one wheel actuator fault occurs. The aim of the study is to design an asymptotically stable controller that can guarantee the safety of the user and ensure tracking on a training path planned by a physical therapist. The redundant degrees of freedom safety control and the asymptotically zero state detectable concept of the walker are presented, the model of redu…
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.