Unsupervised detection of state changes during operation of machine elements

• Autorzy: Hillenbrand Jonas , Fleischer Jürgen

Identyfikatory

DOI

10.36897/jme/136311

• Języki publikacji: EN

Abstrakty

EN

Interpretation of sensor data from machine elements is challenging, if no prior knowledge of the system is available. Evaluation methods must adapt surrounding conditions and operation modes. As supervised learning models can be time-consuming to set up, unsupervised learning poses as alternative solution. This paper introduces a new clustering scheme that incorporates iterative cluster retrieval in order to track the clustering results over time. The approach is used to identify changing machine element states such as operating conditions and undesired changes, like incipient damage or wear. We show that knowledge about the evolving clusters can be used to identify operation and failure events. The approach is validated for machine elements with slide and roll contacts, such as ball screws and bearings. The data used has been captured using vibration and acoustic emission sensors. The results show a general applicability to the unsupervised monitoring of machine elements using the proposed approach.

Słowa kluczowe

EN

condition monitoring; clustering tracking; unsupervised learning

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2021
• Tom: Vol. 21, No. 2
• Strony: 35--46
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Hillenbrand Jonas

• wbk Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

autor

Fleischer Jürgen

• wbk Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

Bibliografia

• [1] MÄRZ M., 2017, Maintenance 4.0 Bestimmt Profitabilität der Fabrik von Morgen: White Paper zu Predictive Maintenance, Mobile Instandhaltung und Asset Innovation, VDI-Z Integrierte Produktion, 159, 52–53.
• [2] UHLMANN E., HOHWIELER E., GEISERT C., 2017, Intelligent Production Systems in the Era of Industry 4.0: Changing Mindsets and Business Models, Journal of Machine Engineering, 17/2, 5–24.
• [3] CELEBI M.E., AYDIN K., 2016, Unsupervised Learning Algorithms, Springer.
• [4] SAARI J., ODELIUS J., 2018, Detecting Operation Regimes Using Unsupervised Clustering with Infected Group Labelling to Improve Machine Diagnostics and Prognostics, Operations Research Perspectives, 5, https://doi.org/10.1016/j.orp.2018.08.002, 232–244.
• [5] HENZINGER P., RAGHAVAN S., RAJAGOPALAN M.R., 1998, Computing on Data Streams, SRC Technical Note, 011, https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=A341F8AD36C78BF4B067C0A3456EA10F?, doi=10.1.1.19.9554&rep=rep1&type=pdf.
• [6] ZHANG T., RAMAKRISHNAN R., LIVNY M., 1996, BIRCH: An Efficient Data Clustering Method for Very Large Databases, SIGMOD Rec., 25, https://doi.org/10.1145/235968.233324, 103–114.
• [7] SILVA J.A., FARIA E.R., BARROS R.C., HRUSCHKA E.R., DE CARVALHO A.C.P.L.F. GAMA J., 2013, Data Stream Clustering, ACM Comput. Surv., 46, https://doi.org/10.1145/2522968.2522981, 1–31.
• [8] MathWorks, 2021, Condition Monitoring and Prognostics Using Vibration Signals, https://www.mathworks.com/help/predmaint/ug/condition-monitoring-and-prognostics-using-vibration-signals.html.
• [9] AGGARWAL C.C., YU P.S., HAN J., WANG J., 2003, A Framework for Clustering Evolving Data Streams, Proceedings of the Twenty-Ninth International Conference on Very Large Databases, Berlin, Germany, 9–12 Morgan Kaufmann Publishers/Elsevier Science, St Louis, MO, 81–92.
• [10] KRANEN P., ASSENT I., BALDAUF C., SEIDL T., 2009, Self-Adaptive Anytime Stream Clustering, 2009, Ninth IEEE International Conference on Data Mining, Miami Beach, FL, USA, IEEE, 249–258.
• [11] CHEN Y., TU L., 2007, Density-Based Clustering for Real-Time Stream Data, Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA.
• [12] NAMITHA K., SANTHOSH KUMAR G., 2020, Learning in the Presence of Concept Recurrence in Data Stream Clustering, J. Big Data, 7/1, 1–28, https://doi.org/10.1186/s40537-020-00354-1.
• [13] SERIR L., RAMASSO E., ZERHOUNI N., 2012, Evidential Evolving Gustafson–Kessel Algorithm for Online Data Streams Partitioning Using Belief Function Theory, International Journal of Approximate Reasoning, 53/5, 747–768, https://doi.org/10.1016/j.ijar.2012.01.009.
• [14] SPILIOPOULOU M., NTOUTSI I., THEODORIDIS Y., SCHULT R., 2006, MONIC: Modeling and Monitoring Cluster Transitions, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD-06, Philadelphia, PA, USA, ACM Press, New York, USA, 706–711.
• [15] PUTRG G.H., READ M.N., KOPRINSKA I., SINGH D., RÖHM U., ASHHURST T.M., KING N.J., 2019, ChronoClust: Density-Based Clustering and Cluster Tracking in High-Dimensional Time-Series Data, Knowledge- Based Systems, 174, 9–26, https://doi.org/10.1016/j.knosys.2019.02.018.
• [16] ESTER M., KRIEGEL H.-P., SANDER J., XU X., 1996, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, AAAI (Hg.) KDD-96 Proceedings, 226–231.
• [17] SCHUBERT E., SANDER J., ESTER M., KRIEGEL H.P., XU X., 2017, DBSCAN Revisited, ACM Trans. Database Syst., 42, 1–21, https://doi.org/10.1145/3068335.
• [18] SVEN, 2021, Inpolyhedron – are points inside a triangulated volume? MATLAB Central File Exchange, https://www.mathworks.com/matlabcentral/fileexchange/37856-inpolyhedron-are-points-inside-a-triangulated-vo lume.
• [19] MathWorks Inpolygon, 2006, Points Located Inside or on Edge of Polygonal Region, https://de.mathworks.com/help/matlab/ref/inpolygon.html.
• [20] HILLENBRAND J., 2020, Ball Screw Failure – Dataset: v1, https://git.scc.kit.edu/ml-wzmm_public/ballscrew loadfailure_v1.
• [21] HILLENBRAND J., SPOHRER A., FLEISCHER J., 2018, Zustandsüberwachung bei Kugelgewindetrieben: Integration von DMS-Sensorik in Kugelgewindetriebemuttern, wt Werkstattstechnik online, 8, 493.
• [22] HILLENBRAND J., 2020, Axial Ball Bearing Speeds v1, ML-WZMM_Public., https://git.scc.kit.edu/mlwzmmpublic/Axial_Ball_Bearing_Speeds_v1.
• [23] ANKERST M., BREUNIG M.M., KRIEGEL H.-P., SANDER J., 1999, OPTICS: Ordering Points to Identify the Clustering Structure, SIGMOD Rec., 28,49–60, https://doi.org/10.1145/304181.304187.
• [24] ESTER M., KRIEGEL H.-P., SANDER J., WIMMER M., XU X., 1998, Incremental Clustering for Mining in a Data Warehousing Environment, Proceedings of the 24th VLDB Conference New York, USA.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).