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 studied the correlation between emitted sound signals and the wear state of sheet metal stamping tools and found that the corrupting sources generated by the tooling of the stamping press and surrounding machinery have higher amplitudes compared to that of the sound emitted by the operation itself.
56 citations
TL;DR: A machine learning algorithm using vibration monitoring is proposed as a possible solution to the problem of monitoring the condition of hydraulic brakes by using the vibration characteristics.
56 citations
TL;DR: The effectiveness and superior performance of the proposed CBM policy for the serial multistage manufacturing systems is demonstrated through a case study of a serial four-stage machining system producing shaft sleeves.
56 citations
TL;DR: In this article, a method for removal of signal's frequential structure disturbances related with relative move of vehicles and stationary monitoring station was used. But the authors did not consider the effect of the Doppler effect.
56 citations
TL;DR: In this paper, a new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed, where Auto-Associative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature.
Abstract: Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA) data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective.
56 citations