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

Abstract

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.

Robot Fault Detection Based on Big Data

“…Analysis of Figures 3 and 4 shows that the author's method is lower than the method based on neural network and the method based on expert system, whether it is the detection time of robot electrical fault or the diagnosis time, it shows that this method can realize the rapid detection and diagnosis of robot electrical faults [22][23][24][25][26][27].…”

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

“…For instance, Xu et al developed a detection method for the bolt loosening of industrial robot joints based on electromechanical modeling and motor current signature analysis (MCSA) [8]. Raouf et al in [9] proposed prognostic health management for the robotic rotate vector reducer using external sensors mounted on the robot and managed using an embedded system to electrical MCSA for mechanical fault detection. Nentwich et al proposed a new method to evaluate the health indicators for industrial robots and suggest a new health indicator (HI) based on vibration data measurements [10].…”

Inspection of Underwater Hull Surface Condition Using the Soft Voting Ensemble of the Transfer-Learned Models

“…Amosov et al [21] studied the defect inspection of rivet joints in aircrafts. Raouf et al [22] proposed a machinelearning-based fault classification system for the fault detection of rotating vector reducers.…”