To achieve zero-defect production during computer numerical control (CNC) machining processes, it is imperative to develop effective diagnosis systems to detect anomalies efficiently. However, due to the dynamic conditions of the machine and tooling during machining processes, the relevant diagnosis systems currently adopted in industries are incompetent. To address this issue, this paper presents a novel data-driven diagnosis system for anomalies. In this system, power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis. To facilitate the analysis, preprocessing mechanisms have been designed to de-noise, normalize, and align the monitored data. Important features are extracted from the monitored data and thresholds are defined to identify anomalies. Considering the dynamic conditions of the machine and tooling during machining processes, the thresholds used to identify anomalies can vary. Based on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO) algorithm to achieve more accurate detection. Practical case studies were used to validate the system, thereby demonstrating the potential and effectiveness of the system for industrial applications.
Bibliographical notePublished by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- Anomaly detection
- Computer numerical control machining
- Data-driven method
- Fruit fly optimization algorithm
ASJC Scopus subject areas
- Computer Science(all)
- Environmental Engineering
- Chemical Engineering(all)
- Materials Science (miscellaneous)
- Energy Engineering and Power Technology