Essays about: "anomaly detection"
Showing result 26 - 30 of 332 essays containing the words anomaly detection.
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26. Industrial Machine Monitoring: Real-Time Anomalous Sound Event Detection on Low-Powered Devices
University essay from Lunds universitet/Matematisk statistikAbstract : Traditionally fault detection in industrial machinery has been performed manually by experienced machine operators listening to the machines. However, it is desirable to automate this process to increase efficiency and improve the working environment of the operators. READ MORE
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27. Deep Learning-Based Anomaly Detection for Predictive Maintenance of Cold Isostatic Press
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : Predictive maintenance is an automated technique that analyses sensor data from industrial systems to enable downtime planning. Available for this study is unlabelled data from sensors placed in proximity to hydraulic system outlets of a cold isostatic press. READ MORE
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28. Industrial 3D Anomaly Detection and Localization Using Unsupervised Machine Learning
University essay from Linköpings universitet/DatorseendeAbstract : Detecting defects in industrially manufactured products is crucial to ensure their safety and quality. This process can be both expensive and error-prone if done manually, making automated solutions desirable. READ MORE
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29. Finding Causal Relationships Among Metrics In A Cloud-Native Environment
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. READ MORE
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30. Scalable Nonparametric L1 Density Estimation via Sparse Subtree Partitioning
University essay from Uppsala universitet/Statistik, AI och data scienceAbstract : We consider the construction of multivariate histogram estimators for any density f seeking to minimize its L1 distance to the true underlying density using arbitrarily large sample sizes. Theory for such estimators exist and the early stages of distributed implementations are available. READ MORE