Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Nentwich, Corbinian; Reinhart, Gunther

doi:10.20944/preprints202104.0797.v1

2021

DOI: 10.20944/preprints202104.0797.v1

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Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Corbinian Nentwich¹,

Gunther Reinhart²

Abstract: Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. We suggest a new health indicator based on vibration data measurements and compare its performance with state-of-the-art health indicators regarding different criteria. This evaluation is based on different data sets from robot test rigs. We find that the proposed health indicator can detect different faults, has low temperature sensitivity and works in instationary velocity regimes. A disc… Show more

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A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Nentwich

Reinhart

2021

Applied Sciences

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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.

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A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Nentwich

Reinhart

2021

Applied Sciences

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A Hybrid Finite Element Method–Analytical Model for Classifying the Effects of Cracks on Gear Train Systems Using Artificial Neural Networks

et al. 2022

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Rotating machinery is fundamental in industry, gearboxes especially. However, failures may occur in their transmission components due to regular usage over long periods of time, even when operations are not intense. To avoid such failures, Structural Health Monitoring (SHM) techniques for damage prediction and in-advance detection can be applied. In this regard, correlations between measured signal variations and damage can be inspected using Artificial Intelligence (AI), which demands large numbers of data for training. Since obtaining signal samples of damaged components experimentally is currently unviable for complex systems due to destructive test costs, model-based numerical approaches are to be explored to solve this problem. To address this issue, this work applied an innovative hybrid Finite Element Method (FEM)–analytical approach, reducing computational effort and increasing performance with respect to traditional FEM. With this methodology, a system can be simulated with accuracy and without geometrical simplifications for healthy and damaged cases. Indeed, considering different positions and dimensions of damages (e.g., cracks) on the tooth roots of gears can offer new ways of damage investigation. As a reference to validate healthy systems and damage cases in terms of eigenfrequencies, a back-to-back test rig was used. Numerical simulations were performed for different cases, resulting in vibrational spectra for systems with no damage, with damage, and with damage of different intensities. The vibration spectra were used as data to train an Artificial Neural Network (ANN) to predict the machine state by Condition Monitoring (CM) and Fault Diagnosis (FD). For predicting the health and the intensity of damage to a system, classification and multi-class classification methods were implemented, respectively. Both sets of classification results presented good prediction agreement.

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ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

2022

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The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions.

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Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Cited by 4 publications

References 0 publications

A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

A Hybrid Finite Element Method–Analytical Model for Classifying the Effects of Cracks on Gear Train Systems Using Artificial Neural Networks

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

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