Big Operational Data Oriented Health Diagnosis Based on Weibull Proportional Hazards Model for Multi-State Manufacturing System

Abstract

Health diagnosis is the kernel of smart manufacturing technique, which could monitor system operational state and predict the impact of performance degradation on the produced product quality in manufacturing process systemically. However, previous studies on health diagnosis have often been depended on the static sensor data of individual production equipment immoderately while ignoring the abundant operational performance data of production task for multi-state manufacturing system. Therefore, a novel mission reliability modeling of manufacturing system based on the Weibull proportional hazards model (WPHM) is presented to diagnose the system health. First, in order to make full use of abundant operational data, the connotation of manufacturing system health is proposed. Second, the concept of big operational data and mission reliability modeling approach based on Quality state task network (QSTN) are put forward. Third, a health diagnosis approach is presented with the aid of mission reliability and WPHM to describe the holistic operational states. Finally, a case study of a cylinder head manufacturing system health diagnosis is performed to validate the proposed method.