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

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.