Abstract
The diffusion of smart sensor technology in production enables real-time monitoring of production conditions. Machine self-diagnosis shall serve the analysis of these conditions by differentiating expected data from anomalies. Several algorithms have been developed in practice and academia to detect anomalies in real-time and to support machine self-diagnosis, so that counteractions can be taken. However, due to the algorithms’ different functionalities, they yield different results when applied to the same data. Our research aims to leverage complementary potentials among these algorithms. To this end, we use a design science research approach to design and prototypically implement a real-time anomaly detection algorithm selector to support decision making regarding machine self-diagnosis. The selector decides in real-time for each sensor-emitted data point, which algorithm yields the most reliable result in terms of anomaly detection. We evaluate functionality and feasibility with two real-world case studies. The evaluation shows that the algorithm selector may outperform single algorithms and that it is applicable in practice.
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References
Buer, S.-V., Strandhagen, J.W., Semini, M., et al.: The digitalization of manufacturing: investigating the impact of production environment and company size. JMTM 32(3), 621–645 (2021)
Schütze, A., Helwig, N., Schneider, T. Sensors 4.0 – smart sensors and measurement technology enable Industry 4.0. J. Sens. Sens. Syst. 7(1), 359–371 (2018)
Cohen, Y., Singer, G.: A smart process controller framework for Industry 4.0 settings. J. Intell. Manuf. 32(7), 1975–1995 (2021). https://doi.org/10.1007/s10845-021-01748-5
Hsieh, R.-J., Chou, J., Ho, C.-H.: Unsupervised online anomaly detection on multivariate sensing time series data for smart manufacturing 2019, pp. 90–97 (2019)
Lavin, A., Ahmad, S.: Evaluating real-time anomaly detection algorithms - the Numenta anomaly benchmark, pp. 38–44 (2015)
Apostol, I., Preda, M., Nila, C., et al.: IoT botnet anomaly detection using unsupervised deep learning. Electronics 10(16), 1876 (2021)
Kotthoff, L.: Algorithm Selection Literature Summary. http://larskotthoff.github.io/assurvey/
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Sun, Q., Pfahringer, B.: Pairwise meta-rules for better meta-learning-based algorithm ranking. Mach. Learn. 93(1), 141–161 (2013). https://doi.org/10.1007/s10994-013-5387-y
Kerschke, P., Kotthoff, L., Bossek, J., et al.: Leveraging TSP solver complementarity through machine learning. Evol. Comput. 26(4), 597–620 (2018)
Peffers, K., Tuunanen, T., Rothenberger, M.A., et al.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24(3), 45–77 (2007)
Vom Brocke, J., Simons, A., Riemer, K., et al.: Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. CAIS 37 (2015)
Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26(2), xiii–xxiii (2002)
Ahmad, S., Lavin, A., Purdy, S., et al.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262, 134–147 (2017)
Numenta Anomaly Benchmark. Numenta Anomaly Benchmark. https://github.com/numenta/NAB
Stahmann, P., Rieger, B.: Towards design principles for a real-time anomaly detection algorithm benchmark suited to Industrie 4.0 streaming data. In: 55th HICSS 2022 (2022)
Siegel, B.: Industrial anomaly detection: a comparison of unsupervised neural network architectures. IEEE Sens. Lett. 4(8), 1–4 (2020)
Farquad, M., Bose, I.: Preprocessing unbalanced data using support vector machine. Decis. Support Syst. 53(1), 226–233 (2012)
Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a framework for evaluation in design science research. Eur. J. Inf. Syst. 25(1), 77–89 (2016)
“AnonymousPublisher1793” on GitHub. Data and additional information. https://github.com/anonymousPublisher1793/publication
Adams, E.P., MacKay, D.J.C.: Bayesian Online Changepoint Detection (2007)
Burnaev, E., Ishimtsev, V.: Conformalized density- and distance-based anomaly detection in time-series data (2016)
Schneider, M., Ertel, W., Ramos, F.: Expected similarity estimation for large-scale batch and streaming anomaly detection. Mach. Learn. 105(3), 305–333 (2016). https://doi.org/10.1007/s10994-016-5567-7
Dunning, T.: The t-digest: efficient estimates of distributions. Softw. Impacts 7, 100049 (2021)
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Stahmann, P., Oodes, J., Rieger, B. (2022). Improving Machine Self-Diagnosis with an Instance-Based Selector for Real-Time Anomaly Detection Algorithms. In: Cabral Seixas Costa, A.P., Papathanasiou, J., Jayawickrama, U., Kamissoko, D. (eds) Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs. ICDSST 2022. Lecture Notes in Business Information Processing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-031-06530-9_3
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