Paper Info
Title
Event Based Robot Prognostics Using Principal Component Analysis
Abstract
As industrial systems are getting complicated, challenges in coming up with efficient maintenance strategies which include predicting failures in the system become important industry specific research topic. Traditionally, research focuses on developing failure prediction models based on physical understanding of the system. But, development of such models are often time consuming and labour intensive for complex systems. In recent past, due to advent of cheaper data collection mechanisms and efficient algorithms, data driven approaches for predicting failures are gaining significant interest in industrial research community. In this paper, we provide a Principal component Analysis (PCA) based approach of failure prediction in industrial robots using event log information. The event logs are collected through remote service set-up from a robot controller. The proposed method will reduce the dimensionality of the original data which consist of interrelated events while retaining the variation present in the data. Using PCA and multivariate statistics such as Hotelling T2, Q Residuals and Q contributions charts, we are able to detect abnormal behavior of event pattern within 30 days before failure.
Year
DOI
Venue
2014
10.1109/ISSREW.2014.52
Software Reliability Engineering Workshops
Keywords
Field
DocType
failure analysis,industrial control,industrial robots,maintenance engineering,principal component analysis,Hotelling T2,Q contribution charts,Q residuals,complex systems,data collection mechanisms,data dimensionality reduction,event based robot prognostics,event log information,failure prediction models,industrial robots,industrial systems,maintenance strategies,multivariate statistics,principal component analysis,Principal Component Analysis,Robot Fault Prognosis
Data mining,Prognostics,Computer science,Artificial intelligence,Predictive modelling,Data collection,Data-driven,Multivariate statistics,Hotelling's T-squared distribution,Robot,Machine learning,Reliability engineering,Principal component analysis
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Sathish, V.100.34
Sithu D. Sudarsan2207.39
srini ramaswamy333745.77