Current Only-Based Fault Diagnosis Method for Industrial Robot Control Cables

Abstract

With the growth of factory automation, deep learning-based methods have become popular diagnostic tools because they can extract features automatically and diagnose faults under various fault conditions. Among these methods, a novelty detection approach is useful if the fault dataset is imbalanced and impossible reproduce perfectly in a laboratory. This study proposes a novelty detection-based soft fault-diagnosis method for control cables using only currents flowing through the cables. The proposed algorithm uses three-phase currents to calculate the sum and ratios of currents, which are used as inputs to the diagnosis network to detect novelties caused by soft faults. Autoencoder architecture is adopted to detect novelties and calculate anomaly scores for the inputs. Applying a moving average filter to anomaly scores, a threshold is defined, by which soft faults can be properly diagnosed under environmental disturbances. The proposed method is evaluated in 11 fault scenarios. The datasets for each scenario are collected when an industrial robot is working. To induce soft fault conditions, the conductor and its insulator in the cable are damaged gradually according to the scenarios. Experiments demonstrate that the proposed method accurately diagnoses soft faults under various operating conditions and degrees of fault severity.

Attention Recurrent Neural Network-Based Severity Estimation Method for Early-Stage Fault Diagnosis in Robot Harness Cable

“…The study by [9] overcame this problem by suggesting a method that is applicable to both online and offline diagnosis using a reference burst signal; however, it still requires a signal generator and a signal-coupling device. Several studies have utilized current signals when diagnosing cable faults [10,11], which are preferred over other methods because they do not require extra equipment. However, the study by [10] could not handle the varying conditions wherein the amplitude and phase angles varied according to the movements of automated machines.…”

“…In [10], a fast fault-detection method for a power transmission line was developed using the summation of the squared threephase currents. In [11], a novelty detection-based diagnosis method was introduced using an adversarial autoencoder and an anomaly scoring technique with three-phase current signals. The method is based on the concept that the data samples with high anomaly scores can be considered as faults [12][13][14][15][16][17][18].…”

Fault diagnosis of power cables based on wavelet transform and CNN-informer

“…However, due to the cables being buried underground in complex and damp environments, minor defects are prone to occur. If not repaired in a timely manner, these early minor defects may accumulate into permanent faults, leading to widespread power outages and economic losses [1][2][3][4] . To reduce the losses caused by cable faults, extensive research has been conducted on power cable fault diagnosis techniques both domestically and internationally.…”

System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes

“…Others, such as [6], diagnosed small, intermediate, and high gearbox backlash in a joint robot using DWT signal preprocessing and an ANN. Kim et al [7] proposed a method to diagnose the severity of control cable wire damage. Huh et al [8] diagnosed soft and hard pitting gears based on a critical information map (CIM).…”