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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05 Dec 2005-Materials Science and Engineering A-structural Materials Properties Microstructure and Processing
TL;DR: In this paper, wavelet packet transform is used as a tool, to characterise the acoustic emission signals released from glass/phenolic polymeric composite during drilling, and the results show that the selected monitoring indices from the wavelet coefficients are capable of detecting the drill condition effectively.
Abstract: Monitoring of tool condition on the basis of sensor signals requires a selection of suitable signal processing technique and monitoring index, to assess the tool condition. In this paper, wavelet packet transform is used as a tool, to characterise the acoustic emission signals released from glass/phenolic polymeric composite during drilling. The results show that the selected monitoring indices from the wavelet packet coefficients are capable of detecting the drill condition effectively.
79 citations
TL;DR: This paper will cover the generic capabilities of artificial neural networks, in both estimation and classification mode, for condition monitoring applications, using examples based around work that the authors have carried out with respect to the monitoring of a power transformer.
79 citations
TL;DR: Motivated by spectral kurtosis (SK) and extreme learning machine (ELM), a novel intelligent diagnosis method for fault classification of rotating machines is proposed and the significance of SK as a feature set is examined and improved ELM in comparison with traditional methods.
Abstract: The condition monitoring of rotating machinery systems based on effective and intelligent fault diagnosis has been widely accepted. Traditional signal processing (SP) methods are less effective due to noises and interferences from different sources and incipient faults which remain active for a short time with a particular frequency. In recent times, SP techniques along with artificial intelligence methods are being used for fault classification. Various complex approaches in SP domain have used for feature extraction of the vibration data to design a feature set. A challenging task is to select dominant features from the available feature set for improving the accuracy of fault classification. Thus, motivated by spectral kurtosis (SK) and extreme learning machine (ELM), we propose a novel intelligent diagnosis method for fault classification of rotating machines. In this paper, SK is used as an input feature set to avoid the task of finding the dominant feature set. The extracted features are fed to ELM for fault identification. However, ELM performance primarily depends upon the hidden node parameters and the number of hidden nodes. The selection of optimum ELM parameters for good performance is an open issue. Therefore, modified bidirectional search with local search method is proposed to determine the optimum set of ELM parameters. The developed method is tested on two vibration data sets of rolling element bearings. We examined the significance of SK as a feature set and improved ELM in comparison with traditional methods. The experimental results demonstrate that the proposed method efficiently optimizes the ELM parameters to provide a compact ELM architecture and also enhances the fault classification accuracy.
79 citations
Journal Article•
TL;DR: In this article, the magnetostrictive sensor technology is used for long range global testing and condition monitoring of structures such as piping, plates and steel cables, including the probe, instrument and data analysis software.
Abstract: A technical background on and applications of a guided wave technology called the magnetostrictive sensor are described. The magnetostrictive sensor technology is for long range global testing and condition monitoring of structures such as piping, plates and steel cables. For long range testing of piping in processing plants, such as refineries and chemical plants, the magnetostrictive sensor system, including the probe, instrument and data analysis software, is matured for use in commercial testing services. Capabilities of the present magnetostrictive sensor system for pipe testing are presented together with an example of testing data and their analysis.
79 citations
TL;DR: Continuous HMM (CHMM) has been tuned to be used in mechanical signal analysis and applied to diagnose of various mechanical signals including rotor fault signals, showing HMM's big potential as an intelligent condition monitoring tool based on its accuracy, robustness, and forecasting ability.
79 citations