Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization

Abstract

In mass customization, a manufacturing line is required to be kept in reliable operation to handle product demand volatility and potential machine degradations. Recent advances in data acquisition and processing allow for effective maintenance scheduling. This paper presents a systematic framework that integrates a sensor-driven prognostic method and an opportunistic maintenance policy. The prognostic method uses degradation signals of each individual machine to predict and update its time-to-failure (TTF) distributions in real time. Then, system-level opportunistic maintenance optimizations are dynamically made according to real-time TTF distributions and variable product orders. The online analytics framework is demonstrated through the case study based on the collected reliability information from a production line of engine crankshaft. The results can effectively prove that the real-time degradation updating and the opportunistic maintenance scheduling can efficiently reduce maintenance cost, avoid system breakdown, and ensure product quality. Furthermore, this framework can be applied not only in an automobile line but also for a broader range of manufacturing lines in mass customization.