Abstract
Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.
Highlights
A Combined Anomaly and TrendCurrently, industrial robots are the workhorses of highly automated production systems [1]
Through the remainder of this publication the term application refers to the condition monitoring of industrial robot gears
As no comparison of anomaly detection (AD) and trend detection (TD) models for univariate time series of HIs derived from acceleration sensors has been performed up to date, a method to select suitable AD and TD models for the application of industrial robot gear condition monitoring is formulated
Summary
Industrial robots are the workhorses of highly automated production systems [1]. Manual monitoring is not feasible and an automated system is required Such a system must be able to detect anomalies and trends in the health indicator data reliably. Anomalies in the data can be related to faults that occur abruptly (e.g., breaking of a gear tooth) and trends can be an indicator for increasing wear [3] The occurrence of such events should be presented to the maintenance crew while showing only few false alarms. A combined anomaly and trend detection system (CATS) for industrial robot gear CM and secondly a method for selecting suitable anomaly detection (AD) and trend detection (TD) models for this defined application are presented. Through the remainder of this publication the term application refers to the condition monitoring of industrial robot gears
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