Essays about: "anomali detektion"

Showing result 1 - 5 of 8 essays containing the words anomali detektion.

1. 1. Finding Causal Relationships Among Metrics In A Cloud-Native Environment

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Suresh Rishi Nandan; [2023]
   Keywords : Causality; Causal Discovery; Bayesian Network; Conditional Independence; Partial Correlation; Ensemble Causal Discovery; Anomaly Detection; Causal Graphs; Causality; Causal Discovery; Bayesian Network; Conditional Indeberoende; partiell korrelation; Ensemble Causal Discovery; Anomali Detektion; kausala grafer;

   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

3. 2. Anomaly detection for prediction of failures in manufacturing environments : Machine learning based semi-supervised anomaly detection for multivariate time series to predict failures in a CNC-machine

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Felix Boltshauser; [2023]
   Keywords : Machine learning; Anomaly Detection; DeepAnT; ROCKET; OCSVM; manufacturing; predictive maintenance; Maskin inlärning; Anomali Detektion; DeepAnT; ROCKET; OCSVM; tillverkning; prediktivt underhåll;

   Abstract : For manufacturing enterprises, the potential of collecting large amounts of data from production processes has enabled the usage of machine learning for prediction-based monitoring and maintenance of machines. Yet common maintenance strategies still include reactive handling of machine failures or schedule-based maintenance conducted by experienced personnel. READ MORE

4. 3. Unsupervised Machine Learning Based Anomaly Detection in Stockholm Road Traffic

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Vilma Hellström; [2023]
   Keywords : Anomaly detection; DBSCAN; LSTM; Machine learning; Synthetic anomalies; Unsupervised learning; Anomalidetektering; DBSCAN; LSTM; maskininlärning; syntetiska anomalier; oövervakad inlärning;

   Abstract : This thesis is a study of anomaly detection in vehicle traffic data in central Stockholm. Anomaly detection is an important tool in the analysis of traffic data for improved urban planing. Two unsupervised machine learning models are used, the DBSCAN clustering model and the LSTM deep learning neural network. READ MORE

5. 4. Evaluating Unsupervised Methods for Out-of-Distribution Detection on Semantically Similar Image Data

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Magnus Pierrau; [2021]
   Keywords : Out-of-distribution detection; anomaly detection; semantic similarity; image data; comparative evaluation; synthetic image data; Out-of-distribution detektion; anomali detektion; semantisk likhet; bilddata; jämförande utvärdering; syntetisk bilddata;

   Abstract : Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. READ MORE

6. 5. Anomaly Detection using LSTM N. Networks and Naive Bayes Classifiers in Multi-Variate Time-Series Data from a Bolt Tightening Tool

   University essay from KTH/Skolan för industriell teknik och management (ITM)

   Author : Karl-Filip Selander; [2021]
   Keywords : LSTM; anomaly detection; time-series; multi-variable; sensor; deep learning; LSTM; anomalidetektion; tidsserie; multivariabel; sensor; djupinlärning;

   Abstract : In this thesis, an anomaly detection framework has been developed to aid in maintenance of tightening tools. The framework is built using LSTM networks and gaussian naive bayes  classifiers. The suitability of LSTM networks for multi-variate sensor data and time-series prediction as a basis for anomaly detection has been explored. READ MORE