Online Fault-monitoring in Machine Tools Based on Energy Consumption Analysis and Non-invasive Data Acquisition for Improved Resource-efficiency

Abstract

Improving the overall equipment effectiveness of machine tools will improve resource-efficiency and productivity in manufacturing. First step to achieve more effectiveness would require sensors for monitoring of machine availability and quality of machining processes. Abnormal machine conditions are characterized by fault-pattern, which can indicate failure and quality losses. Further, machine failure can shorten the remaining useful life of the components and affect the products. Therefore, it is essential to determine a valuable data source which will enable the extraction of fault-pattern and the allocation of these pattern to machining processes. However, this can be challenging due to lack of open source control architecture, different machine types and automation degree, changing operating loads, and dynamic failure rates in a real environment. Retrofit for online analysis of electrical power intake of machine tools seems to satisfy this challenge. A fault-monitoring framework for manufacturing equipment has been proposed in this paper, based on data stream mining techniques for online pattern matching in electrical power data streams. Complex event processing is applied to ensure scalable data processing for large data volumes and automate the reporting in order to assign the fault-patterns to machining processes and products. This concept is introduced as energy-based maintenance and validated for a powertrain machining line in milling and drilling machines.