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Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.


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Journal ArticleDOI
TL;DR: This paper deals with the integration of robust bondgraph-model-based fault diagnosis (FD) with structural recoverability analysis and fault-tolerant control (FTC) of an intelligent heavy-size and autonomous vehicle, used for loading and routing 20- and 40-ft containers inside port terminals.
Abstract: This paper deals with the integration of robust bondgraph-model-based fault diagnosis (FD) with structural recoverability analysis and fault-tolerant control (FTC) of an intelligent heavy-size and autonomous vehicle, used for loading and routing 20- and 40-ft containers inside port terminals. The overactuated vehicle is an omnidirectional mobile platform with redundant actuators such as four independent driven wheels, four independent braking wheels, and four-wheel steering systems. The supervision system is able to monitor the health condition of the vehicle and to study the fault recoverability possibilities to reconfigure the control input when a fault occurs. For FD, analytical redundancy relations (ARRs) are derived from the bond graph model. The latter is constraint relations describing the nominal system behavior and is written in terms of the measured system variables. To perform robust FD, adaptive thresholds are generated. They consider modeling and measurement uncertainties. Once a fault is detected, the structural recoverability algorithm analyzes the redundancy presented on the system, and an appropriate control strategy is applied. The designed procedure of FD and FTC is validated by considering a multiple-fault scenario on the vehicle and comparing its results with the nominal case.

61 citations

Journal ArticleDOI
TL;DR: A new contactless condition monitoring method based on kernel-based object tracking for identifying the interaction between pantograph-catenary systems that gives useful information about the problems of catenary-pantograph systems is proposed.
Abstract: A new contactless condition monitoring method is proposed for anomaly detection in the pantograph-catenary system.Kernel-based tracking is modified for a robust tracking of catenary wire.The foreground detection and object tracking are combined for simultaneously arcing detection.The detailed analysis of the trajectory of the contact wire gives useful information to evaluate the pantograph condition. Condition monitoring is very important in railway systems to reduce maintenance costs and to increase the safety. A high power is needed for the movement of the electric train and collection of the current is critical. Faults occurred in the current collection system cause serious damage in the line and disrupt the railway traffic. When a wear occurs on the contact strip, the asymmetries and distortion are generated in supply voltage and current waveforms because of pantograph arcing. Therefore, the monitoring of pantograph-catenary system has been a hot topic in recent years. This paper deals with a method based on kernel-based object tracking for identifying the interaction between pantograph-catenary systems that gives useful information about the problems of catenary-pantograph systems. The method consists of two key components. The first component is based on the kernel based tracking of the contact wire. The contact point between pantograph and catenary is tracked and the obtained positions are saved as a signal. In the other hand, the foreground of each frame is found by using Gaussian mixture models (GMMs). The occurred arcs are detected by combining tracking and foreground detection methods. The second component employs S-transform for analyzing the pantograph problems, which are used to detect the faults occurred on pantograph strip. The experimental results imply that the proposed method is useful to detect burst of arcing, and irregular positioning of the contact wire.

61 citations

Journal ArticleDOI
01 Sep 2012
TL;DR: The proposed algorithm provides information referred to the gear status and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected and results show that it can update the mesh dynamic properties of the gear on line.
Abstract: This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.

61 citations

Journal ArticleDOI
TL;DR: A methodology for wind turbine performance monitoring based on the use of high-frequency SCADA data is employed featuring state-of-the-art multivariate non-parametric methods for power curve modelling, based on operational data from four wind farms.

61 citations

Journal ArticleDOI
TL;DR: In this paper, a multi-wavelet denoising technique with the data-driven block threshold was proposed to detect the weak features of incipient faults in wind turbines, which is a useful tool for incipient fault detection and its effect mainly depends on the feature separation and the noise elimination.

61 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022413
2021798
2020927
2019936
2018906