A Hybrid Method for On-line Performance Assessment and Life Prediction in Drilling Operations

Tool wear condition monitoring and reaming life prediction are critical for near-zero downtime machining. Recent manufacturing outsourcing business environment necessitates more focus on machine performance degradation to optimize the tool management for improved six-sigma productivity and manufacturing performance. The unmet needs for drilling monitoring is how to effectively predict its remaining life and manage the tool change to minimize downtime and costs. This paper presents a hybrid method for on-line assessment and performance prediction of remaining tool life in drilling operations based on the vibration signals. Logistic regression (LR) analysis combined with maximum likelihood technique is employed to evaluate tool wear condition based on features extracted from vibration signals using wavelet packet decomposition (WPD) technique. Auto-regressive moving average (ARMA) model is then applied to predict remaining useful life based on tool wear assessment result. In addition, failure risk distribution is discussed. The developed prognostic method is validated in drilling operations, which can be also implemented to other manufacturing processes.