Editing Mixture model (section)

===Predictive Maintenance===
The mixture model-based clustering is also predominantly used in identifying the state of the machine in [[predictive maintenance]]. Density plots are used to analyze the density of high dimensional features. If multi-model densities are observed, then it is assumed that a finite set of densities are formed by a finite set of normal mixtures. A multivariate Gaussian mixture model is used to cluster the feature data into k number of groups where k represents each state of the machine. The machine state can be a normal state, power off state, or faulty state.<ref>{{Cite book|url=https://www.researchgate.net/publication/322900854|title=Fault Class Prediction in Unsupervised Learning using Model-Based Clustering Approach|last1=Amruthnath|first1=Nagdev|last2=Gupta|first2=Tarun|date=2018-02-02|doi=10.13140/rg.2.2.22085.14563|publisher=Unpublished}}</ref> Each formed cluster can be diagnosed using techniques such as spectral analysis. In the recent years, this has also been widely used in other areas such as early fault detection.<ref>{{Cite book|url=https://www.researchgate.net/publication/322869981|title=A Research Study on Unsupervised Machine Learning Algorithms for Fault Detection in Predictive Maintenance|last1=Amruthnath|first1=Nagdev|last2=Gupta|first2=Tarun|date=2018-02-01|doi=10.13140/rg.2.2.28822.24648|publisher=Unpublished}}</ref>