Gearbox Fault Prediction Method Based on Deep Learning
Robots are extensively utilized in automotive gluing lines,where their sudden failures can signifi-cantly impact production pace and costs.Presently,gearbox failures in robot bodies are typically addressed post-occurrence,necessitating the urgent implementation of preventive maintenance measures.To tackle the challenge of difficult gearbox fault prediction,sensors are employed to collect the gearbox's state infor-mation,and a deep learning model for fault prediction is established.This model aims to identify abnormal patterns that could lead to faults,thereby enabling fault prediction.Initially,a multivariate time series a-nomaly detection framework based on a Generative Adversarial Network(GAN)is developed,which en-hances the generator's convergence through an improved loss function.Subsequently,a reconstruction er-ror calculation method based on Time-Warped Edit Distance(TWED)is introduced to accurately compute differences in time series signals.Furthermore,an anomaly evaluation method based on Localized Probabil-ity of Anomaly(LoOP)is adopted for scoring anomalies at each data point,thereby improving detection ac-curacy.Finally,the method's effectiveness is applied and validated in a body-in-white painting unit.
gearboxfault predictionmultivariate time seriesgenerative adversarial networksreconstruction error