Abstract
This work presents a new methodology for machine tools anomaly detection via operational data processing. The previous methodology has been field tested on a milling-boring machine in a real production environment. This paper also describes the data acquisition process, as well as the technical architecture needed for data processing. Subsequently, a technique for operational machine data segmentation based on dynamic time warping and hierarchical clustering is introduced. The formerly mentioned data segmentation and analysis technique allows for machine tools anomaly detection thanks to comparison between near real-time machine operational information, coming from strategically positioned sensors and outcomes collected from previous production cycles. Anomaly detection techniques shown in this article could achieve significant production improvements: “zero-defect manufacturing”, boosting factory efficiency, production plans scrap minimization, improvement of product quality, and the enhancement of overall equipment productivity.
Highlights
Industry 4.0 is about the significant transformation taking place in the way goods are produced [1]
Industry 4.0 is being considered the fourth generation of industrial revolution and it is based on the so-called cyber-physical system (CPS) [3], which enables manufacturing and service innovation
Smart production is based on process digitization and the competitive advantage given by the exploitation of the enormous sets of data obtained from a wide range of sources: from computer numeric control (CNC) machine tools, programmable logic controller (PLC), industrial control systems (ICS), supervisory control and data acquisition (SCADA), temperature sensors, and customer data, among others [4]
Summary
Industry 4.0 is about the significant transformation taking place in the way goods are produced [1]. Smart production is based on process digitization and the competitive advantage given by the exploitation of the enormous sets of data obtained from a wide range of sources: from computer numeric control (CNC) machine tools, programmable logic controller (PLC), industrial control systems (ICS), supervisory control and data acquisition (SCADA), temperature sensors, and customer data, among others [4]. According to sources of the industry a 15% drop in unplanned downtime is gathered and a 7% increase in product quality is achieved after smart manufacturing is implemented, and the later facts are showing that when sensor data is captured and analyzed, between 2.3 and three times improvement in quality and productivity are attained [6]. Significant gains await industries that strike out into digital transformation [7,8]
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