Data-driven Models for Fault Classification and Prediction of Industrial Robots

Nentwich, Corbinian; Junker, Sebastian; Reinhart, Günther

doi:10.1016/j.procir.2020.04.126

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…Multiple supervised models such as a support vector machine, neural networks, gaussian processes and random forests were combined with different dimensionality reduction methods based on data from acceleration sensors attached to the gear caps for gear fault classification. The SVM and GP showed the best performance with accuracy values over 91 percent [14].…”

Section: Industrial Robot Condition Monitoringmentioning

confidence: 99%

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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Section: Industrial Robot Condition Monitoringmentioning

confidence: 99%

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

Nentwich

Reinhart

2021

Applied Sciences

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“…1. Mathematical methods (fast and short-time Fourier transform, continuous wavelet transforms) [7,8]; 2. Modeling methods (fuzzy logic, machine learning, and other artificial intelligence methods) [8,9]; 3.…”

Section: Cartesian Robot Descriptionmentioning

confidence: 99%

“…Therefore, for diagnostics, each of the methods presented has its own scope of application and is used to achieve various goals [1,6,7]. The mathematical methods are a powerful tool for evaluating and analyzing data, thereby identifying anomalies in the behavior of the mechanism.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

Autsou,

Kudelina,

Vaimann

et al. 2024

Applied Sciences

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The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.

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“…The changes of the RMS-HI and characteristic frequencies for functional and broken strain gears of industrial robots were investigated in [10]. The classification and regression performance of different data-driven models based on frequency-domain data and principal component analysis for dimensionality reduction was evaluated in [11].…”

Section: Vibration-based Robot Condition Monitoringmentioning

confidence: 99%

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Nentwich

Reinhart

2021

Robotics

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Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.

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Data-driven Models for Fault Classification and Prediction of Industrial Robots

Cited by 11 publications

References 15 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

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

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