Root cause analysis of an out-of-control process using a logical analysis of data regression model and exponential weighted moving average

Control charts are widely used as a tool in process quality monitoring to detect anomalies and to improve the quality of a process and product. Nevertheless, their limitations have increased in the face of increasingly complex manufacturing processes. They do not have capability of handling large streams of non-normal and autocorrelated multivariate data, which is in most real applications. This may lead to an increase in false alarm signals and/or missed detection of anomalies. They are not designed to automatically identify the root causes of an anomaly when the process is out-of-control. Several machine-learning techniques were integrated with control charts to improve the sensitivity and specificity of anomaly detection. Nevertheless, some existing techniques still produce a high false alarm rate and/or missed detection. The root cause analysis is seldom performed. In this paper, we propose a new integration that combines the logical analysis of data regression technique ( LADR ) and the exponential weighted moving average ( EWMA ) as a new model-based control chart. LADR is based on the traditional LAD methodology, which is a supervised data mining technique for pattern generation. LADR transforms the original independent variables into pattern variables by using cbmLAD software to develop a regression model. The LADR–EWMA increases the sensitivity of anomaly detection in the process and uses the patterns to perform root cause analysis of that anomaly. We applied LADR–EWMA to a real application: a concrete manufacturing process. We compared its performance with Linear regression, Support vector regression, Partial Least Square regression, and Multivariate adaptive regression Spline. The results demonstrate that the LADR–EWMA , which is based on pattern recognition, performs better compared to the other techniques in terms of a reduction of false alarms and missed detection. In addition, LADR–EWMA facilitates interpretation and identification of the root cause of the detected anomaly.