Resumen
Enormous Data investigation has pulled in exceptional premium as of late for its endeavour to remove data, learning and insight from Big Data. In industry, with the advancement of sensor innovation and Information and Communication Technologies (ICT), reams of high-dimensional, spilling, and nonlinear information are being gathered and curated to help basic leadership. The recognition of deficiencies in these information is a critical application in eMaintenance arrangements, as it can encourage upkeep basic leadership. Early disclosure of framework deficiencies may guarantee the unwavering quality and security of mechanical frameworks and lessen the danger of spontaneous breakdowns. Complexities in the information, including high dimensionality, quick streaming information streams, and high nonlinearity, force stringent difficulties on blame identification applications. From the information demonstrating point of view, high dimensionality may cause the infamous "revile of dimensionality" and prompt decay in the exactness of blame discovery calculations. Quick streaming information streams expect calculations to give constant or close ongoing reactions upon the landing of new examples. High nonlinearity requires blame identification ways to deal with have adequately expressive power and to abstain from overfitting or underfitting issues. Most existing flaw recognition approaches work in moderately low-dimensional spaces. Hypothetical examinations on high-dimensional blame recognition essentially concentrate on recognizing inconsistencies on subspace projections. In any case, these models are either subjective in choosing subspaces or computationally concentrated. To meet the prerequisites of quick streaming information streams, a few techniques have been proposed to adjust existing models to an online mode to make them pertinent in stream information mining. Be that as it may, few investigations have all the while handled the difficulties related with high dimensionality and information streams. Existing nonlinear blame discovery approaches can't give palatable execution as far as smoothness, viability, heartiness and interpretability. New methodologies are expected to address this issue. Enormous Data investigation has pulled in exceptional premium as of late for its endeavour to remove data, learning and insight from Big Data. In industry, with the advancement of sensor innovation and Information and Communication Technologies (ICT), reams of high-dimensional, spilling, and nonlinear information are being gathered and curated to help basic leadership. The recognition of deficiencies in these information is a critical application in eMaintenance arrangements, as it can encourage upkeep basic leadership. Early disclosure of framework deficiencies may guarantee the unwavering quality and security of mechanical frameworks and lessen the danger of spontaneous breakdowns.Complexities in the information, including high dimensionality, quick streaming information streams, and high nonlinearity, force stringent difficulties on blame identification applications. From the information demonstrating point of view, high dimensionality may cause the infamous "revile of dimensionality" and prompt decay in the exactness of blame discovery calculations. Quick streaming information streams expect calculations to give constant or close ongoing reactions upon the landing of new examples. High nonlinearity requires blame identification ways to deal with have adequately expressive power and to abstain from overfitting or underfitting issues. Most existing flaw recognition approaches work in moderately low-dimensional spaces. Hypothetical examinations on high-dimensional blame recognition essentially concentrate on recognizing inconsistencies on subspace projections. In any case, these models are either subjective in choosing subspaces or computationally concentrated. To meet the prerequisites of quick streaming information streams, a few techniques have been proposed to adjust existing models to an online mode to make them pertinent in stream information mining. Be that as it may, few investigations have all the while handled the difficulties related with high dimensionality and information streams. Existing nonlinear blame discovery approaches can't give palatable execution as far as smoothness, viability, heartiness and interpretability. New methodologies are expected to address this issue.