A study on rotate vector reducer performance degradation based on acoustic emission techniques

Recently, prognostic and health management (PHM) has become a prominent field in modern industry. The rotate vector (RV) reducer is one of the widely used mechanical components in industrial systems, specifically in robots. The RV reducer is known for its unique characteristics of small size, efficient speed transmission, and high torsion. The RV reducer is prone to several kinds of faults, due to its continuous operation in an industrial robot. To keep the operation smooth and steady, timely PHM of the RV reducer has become essential. Previously, the RV reducer fault was diagnosed via various techniques, such as ferrography analysis, vibration analysis, and acoustic emission analysis. However, these conventional techniques have various issues. To resolve those issues, we introduce a novel approach to use the embedded electrical current system for the fault detection of the RV reducer. However, this is quite complicated to investigate mechanical fault using an electrical current signature, since the RV reducer is not an integral part of the electric motor, and finding a fault pattern in faulty components needs thorough examination. We therefore focus on the application of machine learning (ML) for fault classifications. We present an approach for feature extraction, feature selection, and feature reduction using the information obtained from the motor current signature analysis to create an ML-based fault classification system with distinguishable prominent features. Finally, the authenticity of the presented approach is justified via the improved values of evaluating parameters, such as accuracy, specificity, and sensitivity, for ML classifiers.

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Mechanical fault detection based on machine learning for robotic RV reducer using electrical current signature analysis: a data-driven approach

Raouf

Lee

Kim

2022

Journal of Computational Design and Engineering

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Prognostic health management of the robotic strain wave gear reducer based on variable speed of operation: a data-driven via deep learning approach

Raouf

Lee

Noh

et al. 2022

Journal of Computational Design and Engineering

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The robotic reducer is prone to failure because of its unique characteristics. Data from vibration and acoustic emissions sensors have been used for the prognostics of the reducer. However, various issues are associated with such traditional techniques. Hence, our research group proposes a novel approach to utilize the embedded setup of the electrical current to detect the mechanical fault of the robotic reducer in the actual industrial robot. Previously, a comprehensive approach of feature engineering was proposed to classify the mechanical fault for the robotic reducer. However, handcrafted-based feature extraction is quite a tedious task, and computationally expensive. These features required a well-designed feature extractor, and the features need to be manually optimized before feeding into classifiers. In addition, the handcrafted features are problem-specific, and are complicated to generalize. To resolve these challenges, deep features are extracted to classify the fault and generalize for two different motion profiles under different working conditions. In the proposed research work, the fault characteristic is generalized for variable speed of operations considering various kinds of scenarios. In current research work, the generalization capability of the proposed approach is comprehensively evaluated. For that purpose, the data under different working conditions such as of lower speeds, higher speeds, and speed sequestration are used as unseen data to validate the model. The authenticity of the presented approach can be supported by the performance evaluation for fault classification of the different motion profiles and speed of operations.

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A study on rotate vector reducer performance degradation based on acoustic emission techniques

Cited by 2 publications

References 22 publications

Mechanical fault detection based on machine learning for robotic RV reducer using electrical current signature analysis: a data-driven approach

Mechanical fault detection based on machine learning for robotic RV reducer using electrical current signature analysis: a data-driven approach

Prognostic health management of the robotic strain wave gear reducer based on variable speed of operation: a data-driven via deep learning approach

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