Topic
Condition monitoring
About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: A data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems and employs support vector machines for early detection and classification of anomalies is presented.
Abstract: Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods.
105 citations
11 Mar 2013
TL;DR: An overview of typical failure mechanisms and their causes is presented in this article, where recent advances and future perspectives are discussed as well as important and fundamental papers are reviewed and reported in a comprehensive list of references.
Abstract: This paper investigates diagnostic techniques for electrical machines with special reference to rotor faults in induction motors. An overview of typical failure mechanisms and their causes is presented. Also recent advances and future perspectives are discussed. To this aim the most recent related works along with important and fundamental papers are reviewed and reported in a comprehensive list of references.
105 citations
TL;DR: A novel automatic fault detection system using infrared imaging, focussing on bearings of rotating machinery, able to distinguish between all eight different conditions with an accuracy of 88.25%.
105 citations
TL;DR: The computational results prove the capability of the proposed monitoring approach in identifying impending blade breakages and validated by blade breakage cases collected from wind farms located in China.
Abstract: Monitoring wind turbine blade breakages based on supervisory control and data acquisition (SCADA) data is investigated in this research. A preliminary data analysis is performed to demonstrate that existing SCADA features are unable to present irregular patterns prior to occurrences of blade breakages. A deep autoencoder (DA) model is introduced to derive an indicator of impending blade breakages, the reconstruction error (RE), from SCADA data. The DA model is a neural network of multiple hidden layers organized symmetrically. In training DA models, the restricted Boltzmann machine is applied to initialize weights and biases. The back-propagation method is subsequently employed to further optimize the network structure. Through examining SCADA data, we observe that the trend of RE will shift by the blade breakage. To effectively detect RE shifts through online monitoring, the exponentially weighted moving average control chart is deployed. The effectiveness of the proposed monitoring approach is validated by blade breakage cases collected from wind farms located in China. The computational results prove the capability of the proposed monitoring approach in identifying impending blade breakages.
105 citations
TL;DR: Experiments show that the Teager domain features outperform those based on the temporal or AM signal and feature it with statistical and energy-based measures.
Abstract: Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager–Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal.
105 citations