anomaly-detection   146

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Detecting the Onset of Machine Failure using Anomaly Detection Techniques
Numerous factors can contribute to the quality of a product, and not every one of these factors is under manufacturers control. One of the most common sources of quality problems is faulty equipment…
machine-learning  predictive-maintenance  anomaly-detection 
8 weeks ago by hschilling
[1804.02998] Anomaly Detection for Industrial Big Data
As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by this http URL, and output of the NASA Prognostics Center of Excellence (PCoE).) However, as noted by Agrawal and Choudhary 'Our ability to collect "big data" has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery.' In order to fully utilize the potential of Industrial Big Data we need data-driven techniques that operate at scales that process models cannot. Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale. The method generalizes to application with almost any multivariate dataset based on independent ordinations of repeated (bootstrapped) partitions of the dataset and inspection of the joint distribution of ordinal distances.
anomaly-detection  machine-learning  dimension-reduction  statistics  to-write-about  algorithms 
may 2019 by Vaguery
[1811.06838] The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description
Support vector data description (SVDD) is a popular anomaly detection technique. The SVDD classifier partitions the whole data space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classifier requires a kernel function, for which the Gaussian kernel is a common choice. The Gaussian kernel has a bandwidth parameter, and it is important to set the value of this parameter correctly for good results. A small bandwidth leads to overfitting such that the resulting SVDD classifier overestimates the number of anomalies, whereas a large bandwidth leads to underfitting and an inability to detect many anomalies. In this paper, we present a new unsupervised method for selecting the Gaussian kernel bandwidth. Our method, which exploits the low-rank representation of the kernel matrix to suggest a kernel bandwidth value, is competitive with existing bandwidth selection methods.
machine-learning  anomaly-detection  outlier-detection  algorithms  parametrization  approximation  performance-measure  unsupervised-learning  to-understand  heuristics 
april 2019 by Vaguery
How to use machine learning for anomaly detection and condition monitoring
In this article, I will introduce a couple of different techniques and applications of machine learning and statistical analysis, and then show how to apply these approaches to solve a specific use…
anomaly-detection  machine-learning 
january 2019 by hschilling
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
Quote: "Existing approaches that leverage system log data for anomaly
detection can be broadly classi€ed into three groups: PCA based
approaches over log message counters [39], invariant mining based
methods to capture co-occurrence paŠerns between di‚erent log
keys [21], and workƒow based methods to identify execution anomalies in program logic ƒows [42]. Even though they are successful in
certain scenarios, none of them is e‚ective as a universal anomaly
detection method that is able to guard ...
machinelearning  logs  anomaly-detection 
january 2019 by ajohnson1200
yzhao062/pyod: A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly Detection. Since 2017, PyOD has been successfully used in various academic researches and commercial products. PyOD is featured for:

Unified APIs, detailed documentation, and interactive examples across various algorithms.
Advanced models, including Neural Networks/Deep Learning and Outlier Ensem...
anomaly  anomaly-detection  machinelearning  python  deeplearning  neuralnetwork  github 
january 2019 by newtonapple
MentatInnovations/ An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana - MentatInnovations/
anomaly-detection  anomaly  python  elasticsearch  kibana  stream  timeseries  github 
january 2019 by newtonapple
linkedin/luminol: Anomaly Detection and Correlation library
Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detection and correlation. It can be used to investigate possible causes of anomaly. You collect time series data and Luminol can:

Given a time series, detect if the data contains any anomaly and gives you back a time window where the anomaly happened in, a time stamp where the anomaly reaches its severity, and a score indicating how severe is the anomaly compare to ...
anomaly  anomaly-detection  linkedin  python  timeseries  library 
january 2019 by newtonapple
Introduction to Anomaly Detection
In this article, Data Scientist Pramit Choudhary provides an introduction to statistical and machine learning-based approaches to anomaly detection in Python.
Python  anomaly-detection  anomoly  machinelearning 
november 2018 by stuartcw

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