This repository contains files of the anomaly detection model for the production environment. Anomaly Detection is a hot topic in recent years, where the goal is to identify abnormal data that are deviant from the general data distribution. Anomaly detection has been proven as an important area in diverge range of domains, such as credit card fraud analytics in the bank, malignant tumour detection, network intrusion detection, and mechanical unit defect detection etc. I am going to follow classification algorithmns e.g random forest, support vector machine, logistic regression, one- class support vector machine.
The goal is to share anomaly detection ideas in the production environment. Production environment can be any manufacturing industry where specific types of materials are produced.
The dataset used for this tutorial is from Stream Machine Learning (Streaml) Open Challenge 2017-2018. The data descriptions provided below are collected from their website.
"The data set comes from two types of machines: (1) injection molding machines and (2) assembly machines. Injection molding machines are equipped with sensors that measure various parameters of a production process: distance, pressure, time, frequency, volume, temperature, time, speed and force. All the measurements taken at a certain point in time result in a 120 dimensional vector consisting of values of different types (e.g., text or numerical values). Assembly machines are equipped with 3 energy meters. Each measurement for both types of machines is timestamped and described using the OWL ontology. The OWL ontology is provided as several modules that are available and documented on the HOBBIT CKAN site.
All data is provided as RDF triples. The data is provided as (1) metadata and (2) measurements. Metadata includes information about the machine type, the number of sensors per machine and the number of clusters that must be used in order to detect anomalies in the data. Measurements include the actual sensor data as measured by sensors within the machines. We refer participants to this document for additional information and excerpts of the input data".