Transcript Visualizing Time Series State Changes with Prototype Based

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
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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
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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
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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