Abstract
The popularity of 5G networks has made smart manufacturing not limited to high-tech industries such as semiconductors due to its high speed, ultra-high reliability, and low latency. With the advance of system on chip (SoC) design and manufacturing, 5G is also suitable for data transmission in harsh manufacturing environments such as high temperatures, dust, and extreme vibration. The defect of the screw head is caused by the wear and deformation of the die forming the head after mass production. Therefore, the screw quality inspection system based on the tactile network in this article monitors the production quality of the screw; the system will send a warning signal through the router to remind the technician to solve the production problem when the machine produces a defective product. Sensors are embedded into the traditional screw heading machine, and sensing data are transmitted through a gateway to the voluntary computing node for screw slot quality inspection. The anomaly detection data set collected by the screw heading machine has a ratio of anomaly to normal data of 0.006; thus, we propose a time-series deep AutoEncoder architecture for anomaly detection of screw slots. Our experimental results show that the proposed solution outperforms existing works in terms of efficiency and that the specificity and accuracy can reach 97% through the framework proposed in this article.
- J. Davis, T. Edgar, J. Porter, J. Bernaden, and Michael Sarli. 2012. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers and Chemical Engineering 47 (2012), 145–156. DOI:10.1016/j.compchemeng.2012.06.037Google Scholar
Cross Ref
- G. P. Fettweis. 2014. The tactile Internet applications and challenges. IEEE Vehicular Technology Magazine 9, 1 (2014), 64–70. DOI:10.1109/MVT.2013.2295069Google Scholar
Cross Ref
- H. Bourl and Y. Kamp. 1988. Auto-Association by multilayer perceptrons and singular value decomposition. Biological Cybernetics 59 (1988), 291–294. Google Scholar
Digital Library
- D. Ballard. 1987. Modular learning in neural networks. In AAAI Conference 1 (1987), 279. Google Scholar
Digital Library
- D. Rumelhart, G. Hinton, and R. Williams. 1986. Recurrent neural networks. 1986. Nature. 323 (1986), 533–536.Google Scholar
Cross Ref
- J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. http://arxiv.org/abs/1406.1078.Google Scholar
- M. Schuster and K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing 45, 11 (1997), 2673–2681. DOI:10.1109/78.650093 Google Scholar
Digital Library
- C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi. 2017. Inception-V4, Inception-Resnet and the impact of residual connections on learning. 2017. In AAAI Conference. Google Scholar
Digital Library
- V. Chandola, A. Banerjee, and V. Kumar. 2009. Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41, 3 (2009), 15. DOI:10.1145/1541880.1541882 Google Scholar
Digital Library
- R. Chalapathy and S. Chawla. 2019. Deep learning for anomaly detection: A survey. arXiv:1901.03407. DOI:10.1007/s10586-017-1117-8Google Scholar
Digital Library
- M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani. 2018. Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials 20, 4 (2018), 2923–2960. DOI:10.1109/COMST.2018.2844341Google Scholar
Digital Library
- D. Denning. 1987. An intrusion detection model. IEEE Transactions on Software Engineering 13, 2 (1987), 222–32. DOI:10.1109/SP.1986.10010 Google Scholar
Cross Ref
- N. Ye. 2000. A Markov chain model of temporal behavior for anomaly detection. In IEEE Workshop on Information Assurance and Security.Google Scholar
- A. Ray. 2003. Symbolic dynamic analysis of complex systems for anomaly detection. Signal Processing 84, 7 (2003), 1115–1130. DOI:10.1016/j.sigpro.2004.03.011 Google Scholar
Digital Library
- J. Hollmen and V. Tresp. 1997. Call-based fraud detection in mobile communication networks using a hierarchical regime-switching model. In NIPS Conference. Denver, CO, November 30–December 5, 1997. Google Scholar
Digital Library
- S. Guttormsson, R. Marks, M. El-Sharkawi, and I. Kerszenbaum. 1999. Elliptical novelty grouping for on-line short-turn detection of excited running rotors. IEEE Transactions on Energy Conversion 14, 1 (1999), 16–22. DOI:10.1109/60.749142Google Scholar
Cross Ref
- C. Yiakopoulos, K. Gryllias, M. Chioua, M. Hollender, and I. Antoniadis. 2016. An on-line SAX and HMM-based anomaly detection and visualization tool for early disturbance discovery in a dynamic industrial process. Journal of Process Control 44 (2016), 134–159. DOI:10.1016/j.jprocont.2016.05.007Google Scholar
Cross Ref
- K. Wang and S. Stolfo. 2003. Anomalous payload-based network intrusion detection. In Recent Advances in Intrusion Detection: 7th International Symposium, September 15-17, 2003, France, 1973–2020. DOI:10.1007/978-3-540-30143-1_11Google Scholar
- R. Puttini, Z. Marrakchi, and L. Mé. 2003. A Bayesian classification model for real-time intrusion detection. In AIP Conference Proceedings 659 (2003), 150. DOI:10.1063/1.1570541Google Scholar
Cross Ref
- M. Amer, M. Goldstein, and S. Abdennadher. 2013. Enhancing one-class support vector machines for unsupervised anomaly detection. In Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description Conference. DOI:10.1145/2500853.2500857 Google Scholar
Digital Library
- B. Scholkopf, R. Williamson, A. Smola, J. Shawe-Taylort, and J. Platt. 2000. Support vector method for novelty detection. In NIPS Conference, November 29–December 4, 2000, Denver, CO. Google Scholar
Digital Library
- J. Ma and S. Perkins. 2003. Time-series novelty detection using one-class support vector machines. In Proceedings of the International Joint Conference on Neural Networks 3 (2000). DOI:10.1109/IJCNN.2003.1223670Google Scholar
- S. Mahadevan and S. Shah. 2009. Fault detection and diagnosis in process data using one-class support vector machines. Journal of Process Control 19, 3 (2009), 1627–1639. DOI:10.1016/j.jprocont.2009.07.011Google Scholar
Cross Ref
- M. Markou and S. Singh. 2003. Novelty detection: A review—part 2: neural network based approaches. Signal Process 83, 12 (2003), 2499–2521. DOI:10.1016/j.sigpro.2003.07.018 Google Scholar
Digital Library
- C. Bishop. 1994. Novelty detection and neural network validation. IEEE Proceedings—Vision, Image and Signal Processing 141, 4 (1994), 217–222. DOI:10.1049/ip-vis:19941330Google Scholar
Cross Ref
- J. Ryan, M. Jang Lin, and R. Miikkulainen. 1997. Intrusion detection with neural networks. In Proceedings of the 1997 Conference on Advances in Neural Iinformation Processing Systems Denver, Colorado, USA. MIT Press, 943--949. Google Scholar
Digital Library
- P. Crook and G. Hayes. A robot implementation of a biologically inspired method for novelty detection. In Proceedings of Towards Intelligent Mobile Robots Conference. Report number UMCS-01-4-1. Manchester, Manchester University Press.Google Scholar
- S. Marsland, U. Nehmzow, and J. Shapiro. 2000. A real-time novelty detector for a mobile robot. In Proceedings of the EUREL European Advanced Robotics Systems Masterclass and Conference. https://arxiv.org/pdf/cs/0006006.pdf.Google Scholar
- K. Fu, D. Cheng, Y. Tu, and L. Zhang. 2016. Credit card fraud detection using convolutional neural networks. In International Conference on Neural Information Processing (ICONIP'16), 483--490. Kyoto, Japan, Springer. DOI:10.1007/978-3-319-46675-0_53Google Scholar
- D. Rumelhart, G. Hinton, and R. Williams. 1986. Learning representations by back-propagating errors. Nature 323 (1986), 533–536. DOI:10.1038/323533a0Google Scholar
Cross Ref
- M. Munoz-Organero. 2019. Outlier detection in wearable sensor data for human activity recognition (HAR) based on DRNNs. IEEE Access 7, 74422–74436. DOI:10.1109/ACCESS.2019.2921096Google Scholar
Cross Ref
- M. A. Khan, M. R. Karim, and Y. Kim. 2019. A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry 11, 4 (2019), 583, DOI:10.3390/sym11040583 9Google Scholar
- S. Hawkins, H. He, G. Williams, and R. Baxter. 2002. Outlier detection using replicator neural networks. In DaWaK, Springer (2002). 170–180. DOI:10.1007/3-540-46145-0_17 Google Scholar
Cross Ref
- J. An and S. Cho. 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE. DOI:10.6633/IJNS.201511.17(6).03Google Scholar
- B. Zong, Q. Song, M. Renqiang Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen. 2018. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In ICLR. February 16, 2018.Google Scholar
- K. Reddy, S. Sarkar, V. Venug Opalan, and M. Giering. 2016. Anomaly detection and fault disambiguation in large flight data: A multi-modal deep auto-encoder approach. In Annual Conference of the Prognostics and Health Management Society. PHM Society, Denver, Colorado. https://doi.org/10.36001/phmconf.2016.v8i1.2549.Google Scholar
- M. Sakurada and T. Yairi. 2014. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis Gold Coast, Australia. Association for Computing Machinery Press, 4--11. DOI:https://doi.org/10.1145/2689746.2689747 Google Scholar
Digital Library
- Z. Li1, J. Li1, Y. Wang, and K. Wang. 2019. A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment. The International Journal of Advanced Manufacturing Technology 103 (2019), 499–510. DOI:10.1007/s00170-019-03557-wGoogle Scholar
Cross Ref
- S. Ma, M. Chen, J. Wu, Y. Wang, B. Jia, and Y. Jiang. 2018. High-voltage circuit breaker fault diagnosis using a hybrid feature transformation approach based on random forest and stacked autoencoder. IEEE Transactions on Industrial Electronics 66, 12 (2018), 9777–9788. DOI:10.1109/TIE.2018.2879308Google Scholar
Cross Ref
- M. Breunig, H. Peter Kriegel, R. Ng, and J. Sander. 2000. LOF: Identifying density-based local outliers. In ACM SIGMOD International Conference on Management of Data, (2000), Dallas, Texas, USA. Association for Computing Machinery Press, 93--104. DOI:10.1145/342009.335388 Google Scholar
Digital Library
- J. Tang, Z. Chen, A. Waichee Fu, and D. Cheung. 2005. Capabilities of outlier detection schemes in large datasets, framework and methodologies. Knowledge and Information Systems 11, 1 (2005), 45–84. DOI:10.1007/s10115-005-0233-6Google Scholar
Cross Ref
- F. Tony Liu, K. Ming Ting, and F. Tony Liu, K. Ming Ting. 2008. Isolation Forest. In Eighth IEEE International Conference on Data Mining (ICDM'08), Vol. 1. IEEE Computer Society Press, 413--422. DOI:10.1109/ICDM.2008.17Google Scholar
Digital Library
- L. Pugginia and S. McLooneb. 2018. An enhanced variable selection and isolation forest based methodology for anomaly detection with OES. Engineering Applications of Artificial Intelligence. 67 (2018), 126–135. DOI:10.1016/j.engappai.2017.09.021 Google Scholar
Digital Library
- J. Ma, L. Sun, H. Wang, Y. Zhang, and U. Aickelin. 2016. Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Transactions on Internet Technology 16, 1 (2016), 4. DOI:10.1145/2806890 Google Scholar
Digital Library
- M. T. Luong, P. Hieu, and M. D. Christopher. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP'15). Association for Computational Linguistics Press, 1412--1421. DOI:10.18653/v1/D15-1166Google Scholar
Index Terms
Screw Slot Quality Inspection System Based on Tactile Network
Recommendations
Mechatronic Modeling and Analyzing for Feed Servo Control System Based on Torsion Dynamics of Lead-Screw
ICMTMA '10: Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 02Dividing the rotor-screw system of machine tool into a multi-degree-of-freedom torsion system, this paper educes the transfer function model of the mechanism including torsion vibration modes of the first two orders. Consequently, a mechatronic model of ...
A Novel Parallel Manipulator for Rotary Humanoid Wrist Based on Screw Theory
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data MiningA class of parallel manipulators known as Rotary Humanoid Wrist (RHW), which is actuated by six circular motors on the circular base and dose not involve any prismatic joint, is analyzed based on screw theory. Model of RHW is build by using simulink ...
Towards an immunity-based anomaly detection system for network traffic
This paper proposes an immunity-based anomaly detection system for network traffic. The system is inspired by the specificity and diversity of the immune system; the system has a user-specific agent for every user, and diverse agents make a decision ...






Comments