Abstract
Complex data requires advanced analytics methods. In both science and industry, with the ever-increasing amounts of rich and large datasets available, advanced data analytics capabilities are required. Depending on the task at hand, several different techniques and methods can be used to analyze complex data (e.g., multivariate time series, log data, multimodal sensor data). In this chapter, we introduce approaches and methods for advanced analytics on complex industrial data. In particular, we focus on three exemplary methods for modeling and analyzing complex data, i.e., analytics for fault diagnosis, graph signal processing, and pattern mining on networks and graphs. We introduce the general approaches and methods and discuss their implementation in detail for an extended context. From a data perspective, we cover both sequential, i.e., time series, and relational, i.e., graph and network data, also bridging between both by analyzing signals on graphs. Besides setting the stage for the important theoretical background and concepts, we outline, in particular, the perspective on industrial applications and provide specific examples of the application of the presented methods in real-world industrial contexts, i.e., using complex industrial data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In J. B. Bocca, M. Jarke, & C. Zaniolo (Eds.), Proceedings of the 20th International Conference on Very Large Data Bases (VLDB) (pp. 487–499). Morgan Kaufmann.
Alsyouf, I. (2007). The role of maintenance in improving companies’ productivity and profitability. International Journal of Production Economics, 105(1), 70–78.
Angelo Medeiros Fonini, P. (2019). A didactic introduction to graph signal processing techniques and applications. Instituto Alberto Luiz Coimbra.
Arts, J. (2017). Maintenance modeling and optimization (Vol. 526). TU/e Eindhoven.
Atzmueller, M. (2015). Subgroup discovery. WIREs DMKD, 5(1), 35–49.
Atzmueller, M. (2016). Detecting community patterns capturing exceptional link trails. In Proceedings of the IEEE/ACM ASONAM. IEEE.
Atzmueller, M. (2018). Declarative aspects in explicative data mining for computational sensemaking. In Proceedings of the International Conference on Declarative Programming (DECLARE). Springer.
Atzmueller, M., Arnu, D., & Schmidt, A. (2017). Anomaly detection and structural analysis in industrial production environments. In Proceedings of the International Data Science Conference (IDSC 2017), Salzburg, Austria.
Atzmueller, M., Bloemheuvel, S., & Kloepper, B. (2019a). A framework for human-centered exploration of complex event log graphs. In Proceedings of the International Conference on Discovery Science (DS 2019). Springer.
Atzmueller, M., Doerfel, S., & Mitzlaff, F. (2016a). Description-oriented community detection using exhaustive subgroup discovery. Information Sciences, 329, 965–984.
Atzmueller, M., & Kloepper, B. (2018). Mining attributed interaction networks on industrial event logs. In Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning. Springer.
Atzmueller, M., Kloepper, B., Mawla, H. A., Jäschke, B., Hollender, M., Graube, M., Arnu, D., Schmidt, A., Heinze, S., Schorer, L., Kroll, A., Stumme, G., & Urbas, L. (2016b). Big data analytics for proactive industrial decision support. ATP Edition, 58(9).
Atzmueller, M., & Lemmerich, F. (2009). Fast subgroup discovery for continuous target concepts. In Proceedings of the 18th International Symposium on Methodologies for Intelligent Systems (ISMIS 2009) (LNCS) (Vol. 5722, pp. 1–15). Springer.
Atzmueller, M., & Lemmerich, F. (2012). VIKAMINE—Open-source subgroup discovery, pattern mining, and analytics. In Proceedings of the ECML/PKDD. Springer.
Atzmueller, M., Puppe, F., & Buscher, H. P. (2005). Profiling examiners using intelligent subgroup mining. In Proceedings of the IDAMAP, Aberdeen, Scotland (pp. 46–51).
Atzmueller, M., Soldano, H., Santini, G., & Bouthinon, D. (2019b). MinerLSD: Efficient mining of local patterns on attributed networks. Applied Network Science, 4(43).
Atzmueller, M., & Sternberg, E. (2017). Mixed-initiative feature engineering using knowledge graphs. In Proceedings of the 9th International Conference on Knowledge Capture (K-CAP). ACM Press.
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.
Bloemheuvel, S., Van den Hoogen, J., & Atzmueller, M. (2020). Graph signal processing on complex networks for structural health monitoring. In Proceedings of the Complex Networks. Springer.
Case School of Engineering. (n.d.). CWRU dataset; Case Western Reserve University bearing data center. Retrieved from https://csegroups.case.edu/bearingdatacenter/home
Chow, M., Mangum, P. M., & Yee, S. O. (1991). A neural network approach to real-time condition monitoring of induction motors. IEEE Transactions on Industrial Electronics, 38(6), 448–453.
Cococcioni, M., Lazzerini, B., & Volpi, S. L. (2013). Robust diagnosis of rolling element bearings based on classification techniques. IEEE Transactions on Industrial Informatics, 9(4), 2256–2263.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery: An overview. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining (pp. 1–34). AAAI Press.
Folmer, J., Kirchen, I., Trunzer, E., Vogel-Heuser, B., Pötter, T., Graube, M., Heinze, S., Urbas, L., Atzmueller, M., & Arnu, D. (2017). Big and smart data—Challenges in the process industries. ATP Edition (pp. 1–2).
Gebhardt, J., Froese, T., Krüger, A., Appel, J., Benner, R., Hammer, M., Altermann, A., Hochrein, T., Kugler, C., Jatzkowski, P., Gloy, Y. S., Saggiomo, M., Roth, R., Elixmann, I., Tapken, H., Weber, W., Atzmueller, M., Garcke, J., Pielmeier, J., Rosen, R., & Tercan, H. (2016). Status report: Chances with big data—Best practice. Tech. Rep. VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik.
Group, M. R. W., et al. (1985). Report of large motor reliability survey of industrial and commercial installations, part I. IEEE Transactions on Industry Applications, 1(4), 865–872.
Hajnayeb, A., Ghasemloonia, A., Khadem, S., & Moradi, M. (2011). Application and comparison of an ANN-based feature selection method and the genetic algorithm in gear-box fault diagnosis. Expert Systems with Applications, 38(8), 10205–10209.
Hoang, D. T., & Kang, H. J. (2019). Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognitive Systems Research, 53, 42–50.
Huang, J., Hu, X., & Yang, F. (2011). Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker. Measurement, 44(6), 1018–1027.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
Jin, N., Flach, P., Wilcox, T., Sellman, R., Thumim, J., & Knobbe, A. (2014). Subgroup discovery in smart electricity meter data. IEEE Transactions on Industrial Informatics, 10(2), 1327–1336.
Jin, X., Zhao, M., Chow, T. W., & Pecht, M. (2013). Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Transactions on Industrial Electronics, 61(5), 2441–2451.
Jing, L., Zhao, M., Li, P., & Xu, X. (2017). A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement, 111, 1–10.
Klösgen, W. (1996). Explora: A multipattern and multistrategy discovery assistant. In Advances in knowledge discovery and data mining (pp. 249–271). AAAI Press.
Knobbe, A. J., Cremilleux, B., Fürnkranz, J., & Scholz, M. (2008). From local patterns to global models: The LeGo approach to data mining. In From Local Patterns to Global Models: Proceedings of the ECML/PKDD-08 Workshop (LeGo-08) (pp. 1–16).
Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and support vector machines (SVMS). Applied Soft Computing, 11(6), 4203–4211.
Lavrac, N. (2005). Subgroup discovery techniques and applications. In T. B. Ho, D. W. Cheung, & H. Liu (Eds.), Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (LNAI) (Vol. 3518). Springer.
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 63(5), 3137–3147.
Lemmerich, F., Becker, M., & Atzmueller, M. (2012). Generic pattern trees for exhaustive exceptional model mining. In Proceedings of the ECML/PKDD. Springer.
Liu, H., Zhou, J., Zheng, Y., Jiang, W., & Zhang, Y. (2018a). Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Transactions, 77, 167–178.
Liu, R., Yang, B., Zio, E., & Chen, X. (2018b). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47.
Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10.
Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53(6), 1517–1525.
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148.
Meng, Z., Zhan, X., Li, J., & Pan, Z. (2018). An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement, 130, 448–454.
Miao, S., Veerman, R., Koenders, E., & Knobbe, A. (2013). Modal analysis of a concrete highway bridge: Structural calculations and vibration-based results. In Proceedings of the Conference on Structural Health Monitoring of Intelligent Infrastructure, Hongkong.
Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier.
Morik, K. (2002). Detecting interesting instances. In D. Hand, N. Adams, & R. Bolton (Eds.), Pattern detection and discovery (LNCS) (Vol. 2447, pp. 13–23). Springer.
Morik, K., Boulicaut, J., & Siebes, A. (Eds.). (2005). Local Pattern Detection, International Seminar, Dagstuhl Castle, Germany, April 12–16, 2004, Revised Selected Papers (LNCS) (Vol. 3539). Springer.
Natu, M., & Palshikar, G. (2014). Interesting subset discovery and its application on service processes. In K. Yada (Ed.), Data mining for service (Studies in big data) (Vol. 3, pp. 245–269). Springer.
Pandya, D., Upadhyay, S., & Harsha, S. P. (2013). Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Systems with Applications, 40(10), 4137–4145.
Santos, P., Villa, L. F., Reñones, A., Bustillo, A., & Maudes, J. (2015). An SVM-based solution for fault detection in wind turbines. Sensors, 15(3), 5627–5648.
Scarf, P. (2007). A framework for condition monitoring and condition based maintenance. Quality Technology & Quantitative Management, 4(2), 301–312.
Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5, 269–287.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Soldano, H., Santini, G., & Bouthinon, D. (2015). Local knowledge discovery in attributed graphs. In Proceedings of the ICTAI (pp. 250–257). IEEE.
Soldano, H., Santini, G., Bouthinon, D., & Lazega, E. (2017). Hub-authority cores and attributed directed network mining. In Proceedings of the ICTAI (pp. 1120–1127). IEEE.
Stankovic, L., Mandic, D., Dakovic, M., Brajovic, M., Scalzo, B., & Constantinides, T. (2019a). Graph signal processing—Part I: Graphs, graph spectra, and spectral clustering. arXiv preprint arXiv:1907.03467.
Stankovic, L., Mandic, D. P., Dakovic, M., Kisil, I., Sejdic, E., & Constantinides, A. G. (2019b). Understanding the basis of graph signal processing via an intuitive example-driven approach [lecture notes]. IEEE Signal Processing Magazine, 36(6), 133–145.
Stefan Bloemheuvel, B. K., & Atzmueller, M. (2019). Graph summarization for computational sensemaking on complex industrial event logs. In Proceedings of the Workshop on Methods for Interpretation of Industrial Event Logs, International Conference on Business Process Management, Vienna.
Sternberg, E., & Atzmueller, M. (2018). Knowledge-based mining of exceptional patterns in logistics data: Approaches and experiences in an industry 4.0 context. In Proceedings of the 24th International Symposium on Methodologies for Intelligent Systems (ISMIS) (LNCS). Springer.
Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., & Chen, X. (2016). A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement, 89, 171–178.
van den Hoogen, J., Bloemheuvel, S., & Atzmueller, M. (2020). An improved wide-kernel CNN for classifying multivariate signals in fault diagnosis. In ICDMW. IEEE.
Wang, Z., Zhang, Q., Xiong, J., Xiao, M., Sun, G., & He, J. (2017). Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sensors Journal, 17(17), 5581–5588.
Wortmann, F., & Flüchter, K. (2015). Internet of things. Business & Information Systems Engineering, 57(3), 221–224.
Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. In Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery (pp. 78–87). Springer.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2013). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962.
Yin, S., Li, X., Gao, H., & Kaynak, O. (2014). Data-based techniques focused on modern industry: An overview. IEEE Transactions on Industrial Electronics, 62(1), 657–667.
You, D., Gao, X., & Katayama, S. (2014). WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Transactions on Industrial Electronics, 62(1), 628–636.
Zhang, A., Li, S., Cui, Y., Yang, W., Dong, R., & Hu, J. (2019). Limited data rolling bearing fault diagnosis with few-shot learning. IEEE Access, 7, 110895–110904.
Zhang, W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017). A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 17(2), 425.
Zhang, X., Xu, R., Kwan, C., Liang, S. Y., Xie, Q., & Haynes, L. (2005). An integrated approach to bearing fault diagnostics and prognostics. In Proceedings of the 2005, American Control Conference (pp. 2750–2755). IEEE.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
Zhou, W., Habetler, T. G., & Harley, R. G. (2007). Bearing condition monitoring methods for electric machines: A general review. 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (pp. 3–6). IEEE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this chapter
Cite this chapter
van den Hoogen, J., Bloemheuvel, S., Atzmueller, M. (2023). Advanced Analytics on Complex Industrial Data. In: Liebregts, W., van den Heuvel, WJ., van den Born, A. (eds) Data Science for Entrepreneurship. Classroom Companion: Business. Springer, Cham. https://doi.org/10.1007/978-3-031-19554-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-19554-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19553-2
Online ISBN: 978-3-031-19554-9
eBook Packages: Business and ManagementBusiness and Management (R0)