Topic
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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TL;DR: A new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN), which shows the advantage of the proposed method in adaptive features selection and superior accuracy in Bearing condition recognition.
Abstract: Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.
97 citations
TL;DR: The modelling of condition monitoring information for three critical water pumps at a large soft-drinks manufacturing plant is described to predict the distribution of the residual lifetimes of the individual pumps.
Abstract: In this paper the modelling of condition monitoring information for three critical water pumps at a large soft-drinks manufacturing plant is described. The purpose of the model is to predict the distribution of the residual lifetimes of the individual pumps. This information is used to aid maintenance management decision-making, principally relating to overhaul. We describe a simple decision rule to determine whether maintenance action is necessary given monitoring information to date.
97 citations
21 Nov 2004
TL;DR: In this article, the ANN approach is adopted as a remedy for the drawback of ratio methods in the DGA for transformer fault diagnosis, where the ratio methods have an advantage that they are independent of volume of gases involved.
Abstract: Power transformer being a major apparatus in a power system, monitoring of its in-service behavior is necessary to avoid catastrophic failures, costly outages. Dissolved gas analysis (DGA) is an important tool for transformer fault diagnosis. The ratio methods used in the DGA have an advantage that they are independent of volume of gases involved. But the main draw back of the ratio methods is that they fail to cover all ranges of data. ANN approach is adopted as a remedy for the drawback of ratio methods in this paper.
97 citations
TL;DR: This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles that significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions.
Abstract: The efficient condition monitoring and accurate module defect detection in large-scale photovoltaic (PV) farms demand for novel inspection method and analysis tools. This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles. The convolutional neural network is used in the machine learning process to classify various forms of module defects. Such a supervised learning process can extract a range of deep features of operating PV modules. It significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions. The proposed algorithmic solution is extensively evaluated from different aspects, and the numerical result clearly demonstrates its effectiveness for efficient defect detection of PV modules.
97 citations
01 Sep 2001
TL;DR: This paper examines the performance of both types of classifier in one given scenario—a multiclass fault characterization example—and offers a strategy that improves the generalization performance significantly in cases where only limited training data are available.
Abstract: Artificial neural networks (ANNs) have been used to detect faults in rotating machinery for a number of years, using statistical estimates of the vibration signal as input features, and they have been shown to be highly successful in this type of application. Support vector machines (SVMs) are a more recent development, and little use has been made of them in the condition monitoring (CM) arena. The availability of a limited amount of training data creates some problems for the use of SVMs, and a strategy is offered that improves the generalization performance significantly in cases where only limited training data are available. This paper examines the performance of both types of classifier in one given scenario—a multiclass fault characterization example.
97 citations