Abstract
Manufacturing processes can be monitored for anomalies and failures just like machines, in condition monitoring and prognostic and health management. This research takes inspiration from condition monitoring and prognostic and health management techniques to develop a method for part production process monitoring. The contribution brought by this paper is an automated technique for process monitoring that works with low sampling rates of 1/3Hz, a limitation that comes from using data provided by an industrial partner and acquired from industrial manufacturing processes. The technique uses kernel density estimation functions on machine tools spindle load historical time signals for distribution estimation. It then uses this estimation to monitor the manufacturing processes for anomalies in real time. A modified version was tested by our industrial partner on a titanium part manufacturing line.
Highlights
Condition-based monitoring (CBM) is defined as the act of monitoring the condition of a machine or a process [1], while prognostics and health management (PHM) is defined as an algorithmic way of detecting, predicting, monitoring and assessing operation problems and health changes of systems [2] as well as taking decisions on them. Both are used to monitor machinery, such as pumps [3], bearings and gears [4,5], electronics [6], plane parts [6] and machine tools (Computer Numerical Control (CNC)) [2,7]. This field of research is backed by a strong interest of the industry towards Smart Manufacturing, called Industry 4.0, which aims at the autonomous management of production through virtualization of the production chain [8]
This paper proposes a data-driven automated system for normal behavior signal determination applied to the manufacturing process of complex aerospace parts with a low sampling rate, limited to 1/3 Hz by our industrial partner who could not change this setting
We present an explanation of the processing needed for normal behavior determination by using kernel density estimation functions and the results
Summary
Condition-based monitoring (CBM) is defined as the act of monitoring the condition of a machine or a process [1], while prognostics and health management (PHM) is defined as an algorithmic way of detecting, predicting, monitoring and assessing operation problems and health changes of systems [2] as well as taking decisions on them Both are used to monitor machinery, such as pumps [3], bearings and gears [4,5], electronics [6], plane parts [6] and machine tools (Computer Numerical Control (CNC)) [2,7]. These methods are of great interest since they can be updated without expert knowledge of the machine and can be trained to work on more than one system, as long as good quality contextual data are available
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