NDLI: Multi-Machine Gaussian Topic Modeling for Predictive Maintenance

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Alexander Karlsson Ebru Turanoglu Bekar Anders Skoogh
Abstract	In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behaviors. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems.
e-ISSN	21693536
DOI	10.1109/ACCESS.2021.3096387
Journal	IEEE Access
Volume Number	9
Language	English
Publisher	IEEE
Publisher Date	2021-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Exploratory Data Analysis Cluster Analysis Gaussian Topic Modeling Hierarchical Modeling Multi-machine Analysis Multiple Data Streams
Content Type	Text
Resource Type	Article

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