The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
As shallow architecture is inefficient in terms of computational elements, some incipient fault features can be characterized through the composition of many nonlinearities, ie, with deep network. In this paper, a novel approach is developed for multivariate statistical process monitoring based on higher‐order correlations. First, the correlations among monitoring variables can be learned by a multilayer...
Most statistical analysis technologies use detection thresholds for fault diagnosis, which often cannot effectively characterize some specific faults in a statistical manner. However, the details and small changes in the faults can be exploited by deep learning‐based feature representation. In this paper, we present a weighted time series fault diagnosis method to learn the deep correlations of faults...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.