Training data selection criteria for detecting failures in industrial robots

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. Inrecent 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.

Event Based Robot Prognostics Using Principal Component Analysis

Principal component analysis(PCA) has been widely used for monitoring multivariate process, but PCA can not monitor dynamic multivariate process .Dynamic principal component analysis(DPCA) is a method by extending static PCA to monitor dynamic multivariate process. The paper verifies the effectiveness of DPCA for monitoring dynamic multivariate process.

Research on Dynamic Principal ComponentAnalysis and Application

This paper presents principal component analysis (PCA), some improvement of PCA and the development of PCA. PCA does not depend on the accurate mathematical model, is able to implement the feature extraction of the complex process data, and establishes a principal component model of the corresponding process. It can achieve the extraction of the system information and eliminate the interference the system. So there is the existence of a good applications prospect in the complex process of fault diagnosis and prediction maintain.

Fault Prediction Based on Data-Driven Technique

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Fault Prediction Based on Data-Driven Technique