Search

Industrial robots are widely used in modern factories. Robot faults lead to the inevitable suspension of production lines. The prediction of robot failure can improve production capacity. However, it is challenging due to the variations of robots in dynamic working regimes. This paper presents a methodology of fault prognosis of industrial robots, including (1) a modeling approach of remaining useful life prediction using domain-generalization-adversarial long short-term memory to reduce the robot-to-robot variations, (2) an approach of two-stage health assessment based on principal component analysis-squared prediction error and p-chart that can reduce the disturbance of outliers in normal operations, and (3) a workflow containing feature extraction using wavelet packet decomposition, feature smoothing using exponential smoothing, feature normalization using z-score and feature selection using Pearson correlation coefficient. The case study of liquid crystal display transfer robots shows the methodology can effectively reduce the variations and improve the prediction of RUL.

The prediction of the average Material Removal Rate (MRR) for Chemical Mechanical Planarization (CMP) process has been recognized to be a critical factor of Virtual Metrology (VM) modeling as well as wafer-to-wafer process control. In this paper, a novel method is proposed to dynamically predict MRR in CMP process by using a Just-in-time (JIT) model-based strategy. First, the proposed method applies a reference search procedure to query similar data samples from the historical dataset. Second, a Support Vector Regression (SVR) model is used to fuse the similar data samples with the past MRR. Finally, Particle Filter (PF) is employed to estimate and update the fusion result. The application of PF ensures that the method can track the CMP process change and continuously learn from the current dataset. Compared with static models, the proposed method, named JIT-PF model is an online dynamic method with enhanced accuracy. Compared with pure JIT model, JIT-PF model keeps the historical knowledge in the process. A public dataset is used to validate JIT-PF model with the benchmarks from the recent literature. The model outperforms other peer dynamic models and some static models from the literature.