2019, Volume 5, Issue 4
Engineering >> 2019, Volume 5, Issue 4 doi: 10.1016/j.eng.2019.03.012
Data-Driven Anomaly Diagnosis for Machining Processes
a Faculty of Engineering, Environment and Computing, Coventry University, CV1 5FB, UK
b School of Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China
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Abstract
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 denoise, 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.
Keywords
Computer numerical control machining ; Anomaly detection ; Fruit fly optimization algorithm ; Data-driven method
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References
[ 1 ] Bayar N, Darmoul S, Hajri-Gabouj S, Pierreval H. Fault detection, diagnosis and recovery using artificial immune systems: a review. Eng Appl Artif Intell 2015;46:43–57. link1
[ 2 ] Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN. A review of process fault detection and diagnosis: part I: quantitative model-based methods. Comput Chem Eng 2003;27(3):293–311. link1
[ 3 ] Aydin I, Karakose M, Akin E. Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection. Expert Syst Appl 2010;37 (7):5285–94. link1
[ 4 ] Yang H, Li T, Hu X, Wang F, Zou Y. A survey of artificial immune system based intrusion detection. Sci World J 2014;2014:156790. link1
[ 5 ] Wang S, Liang YC, Li WD, Cai XT. Big data enabled intelligent immune system for energy efficient manufacturing management. J Clean Prod 2018;195:507–20. link1
[ 6 ] Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, et al. Cloud-enabled prognosis for manufacturing. CIRP Ann 2015;64(2):749–72. link1
[ 7 ] Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D. Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mech Syst Signal Process 2014;42(1–2):314–34. link1
[ 8 ] Hu G, Li H, Xia Y, Luo L. A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis. Comput Ind 2018;100:287–96. link1
[ 9 ] Tian Y, Fu M, Wu F. Steel plates fault diagnosis on the basis of support vector machines. Neurocomputing 2015;151:296–303. link1
[10] Zheng J, Pan H, Cheng J. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech Syst Signal Process 2017;85:746–59. link1
[11] Wu H, Zhao J. Deep convolutional neural network model based chemical process fault diagnosis. Comput Chem Eng 2018;115:185–97. link1
[12] Madhusudana C, Kumar H, Narendranath S. Fault diagnosis of face milling tool using decision tree and sound signal. Materials Today Proc 2018;5 (5):12035–44. link1
[13] Lu F, Jiang J, Huang J, Qiu X. Dual reduced kernel extreme learning machine for aero-engine fault diagnosis. Aerosp Sci Technol 2017;71:742–50. link1
[14] Wen L, Li X, Gao L, Zhang Y. A new convolutional neural network-based datadriven fault diagnosis method. IEEE Trans Ind Electron 2018;65(7):5990–8. link1
[15] Wen L, Gao L, Li X. A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans Syst Man Cybern Syst 2019;49(1):136–44. link1
[16] Wen L, Li X, Gao L. A new two-level hierarchical diagnosis network based on convolutional neural network. IEEE Trans Instrum Meas. Forthcoming 2019. link1
[17] García S, Luengo J, Herrera F. Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowl Base Syst 2016;98:1–29. link1
[18] Pan S, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2010;22(10):1345–59. link1
[19] Liu Z, Guo Y, Sealy M, Liu Z. Energy consumption and process sustainability of hard milling with tool wear progression. J Mater Process Technol 2016;229:305–12. link1
[20] Sealy M, Liu Z, Zhang D, Guo Y, Liu Z. Energy consumption and modeling in precision hard milling. J Clean Prod 2016;135:1591–601. link1
[21] Stoney R, Donohoe B, Geraghty D, O’Donnell G. The development of surface acoustic wave sensors (SAWs) for process monitoring. Procedia CIRP 2012;1:569–74. link1
[22] García Plaza E, Núñez López PJ. Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mech Syst Signal Process 2018;98:902–19. link1
[23] Feng Z, Zuo M, Chu F. Application of regularization dimension to gear damage assessment. Mech Syst Signal Process 2010;24(4):1081–98. link1
[24] Rimpault X, Bitar-Nehme E, Balazinski M, Mayer J. Online monitoring and failure detection of capacitive displacement sensor in a Capball device using fractal analysis. Measurement 2018;118:23–8. link1
[25] Xia M, Li T, Xu L, Liu L, De Silva C. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans Mechatron 2018;23(1):101–10. link1
[26] Zheng X, Wang L, Wang S. A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowl Base Syst 2014;57:95–103. link1
[27] Liang Y, Lu X, Li W, Wang S. Cyber physical system and big data enabled energy efficient machining optimisation. J Clean Prod 2018;187:46–62. link1
[28] Powers D. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J Mach Learn Technol 2011;2 (1):37–63. link1
[29] Du T, Ke X, Liao J, Shen Y. DSLC-FOA: improved fruit fly optimization algorithm for application to structural engineering design optimization problems. Appl Math Model 2018;55:314–39. link1
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