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: In this article, the authors reviewed the investigation into overhead line deterioration, briefly outlined the inspection methods available at present, and finally introduced a current project being undertaken in the Power and Energy Systems Research Group at the University of Bath in collaboration with two major utilities in the UK, into on-line condition monitoring of overhead lines.
116 citations
TL;DR: By exploiting the configuration of three-phase machines, it is demonstrated that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA).
Abstract: This paper deals with the diagnosis of three-phase electrical machines and focuses on failures that lead to stator-current modulation. To detect a failure, we propose a new method based on stator-current demodulation. By exploiting the configuration of three-phase machines, we demonstrate that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA). From a practical point of view, we also prove that PCA-based demodulation is more attractive than CT. After demodulation, we propose two statistical criteria aiming at measuring the failure severity from the demodulated signals. Simulations and experimental results highlight the good performance of the proposed approach for condition monitoring.
116 citations
01 Aug 2000
TL;DR: Experimental results show that the proposed system can reliably detect tool conditions in drilling operations in real time and is viable for industrial applications.
Abstract: Wavelet transforms and fuzzy techniques are used to monitor tool breakage and wear conditions in real time according to the measured spindle and feed motor currents, respectively. First, continuous and discrete wavelet transforms are used to decompose the spindle and feed ac servo motor current signals to extract signal features so as to detect the breakage of drills successfully. Next, the models of the relationships between the current signals and the cutting parameters are established under different tool wear states. Subsequently, fuzzy classification methods are used to detect tool wear states based on the above models. Finally, the two methods above are integrated to establish an intelligent tool condition monitoring system for drilling operations. The monitoring system can detect tool breakage and tool wear conditions using very simple current sensors. Experimental results show that the proposed system can reliably detect tool conditions in drilling operations in real time and is viable for industrial applications.
115 citations
TL;DR: An online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden using artificial neural network models, representing each main component of the combinedHeat and Power plant, connected to a graphical user interface.
115 citations
TL;DR: Computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals are explored.
115 citations