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University of Jyväskylä Department of Mathematical Information Technology Mining Time Series State Changes with Prototype Based Clustering Markus Pylvänen Sami Äyrämö Tommi Kärkkäinen University of Jyväskylä ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology The Problem • Industrial processes produce a huge amount of multivariate time series data • Manual surveillance requires too much resources • Malfunction should be detected before the occurrence – The malfunction state and the preceeding states, or even sequence of the states, must be recognized, characterized and detected for proactive surveillance ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology Äyrämö, S., Knowledge Mining using Robust Clustering, PhD Thesis, University of Jyväskylä, 2006 ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology About the Domain • Monitoring of wind turbine gears and mechanical drives manufactured for the process industries • By detecting faults before they occur it is possible to plan service breaks in advance and maximize the running time of gear units • No a priori information are available on the operational states • The visualization tool – detecting and visualizing the state changes in gear units – a simple and understandable view to the process data for the use of industrial process experts ICANNGA 2009 Department of Mathematical Information Technology University of Jyväskylä Gear unit • Measured gear unit is 750 kW industrial planetary gear ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology The data • Condition of the gear units are monitored by Moventas Condition Management System (CMaS) which uses several sensors for detecting – – – – – count of oil particles vibration rotation speed oil temperature oil pressure • Size of test data 2029 × 215 • One hour resolution • One malfunction was detected by the domain specialist in the test data collected from the gear unit ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology The method • Time-series data can be analyzed with many data mining techniques – E.g., clustering and dimension reduction provide information about process states or correlations between measurements • Using sequence mining also the order of state changes can be recognized • Combining these with visualization can get an overall view to the different states in the process and the order they occurred ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology Mining the Time Series State Changes ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology Occurrence of Clusters in Timeline • • • • • Colors represent clusters Each cluster correspond to a particular state Any clustering method can be applied Information about within- and between-cluster similarities is lost Recurrent sequences are still difficult to recognize ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology Implementation • The MATLAB K-means algorithm was used in the clustering step – The prototype-based methods provide natural representatives for clusters prototypes – Easy to modify for incomplete data sets – Based on classical statistics, not robust against gross errors – The other methods should be tried later when more data will be available • Dimension reduction was realized using MATLAB PCA-method • Graphical user interface was programmed with Java using JFreeChart library • All the written code are open source and licensed with GPLv3 ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology Transition network Malfunction state ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology Window for Comparing Clusters • • Clusters are compared with one of the vibration variables Malfunction in cluster 5 can be easily seen ICANNGA 2009 University of Jyväskylä Department of Mathematical Information Technology Conclusions • The prototype software was found to be a promising monitoring tool for gear unit monitoring • More data from normal behavior and malfunctions are required • More efficient clustering techniques (including missing data treatment) must be evaluated • Design of the visual outlook must be enhanced ICANNGA 2009
