Beyond Condition-Monitoring: Comparing Diagnostic Events with Word Sequence Kernel for Train Delay Prediction

Wan-Jui Lee, David M.J. Tax, and Robert P.W. Duin
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmec_16_013.pdf396.68 KBJune 8, 2016 - 11:06pm

In the modern trains operated by the Dutch Railways (Nederlandse Spoorwegen) in the Netherlands, there are on-board train management systems continuously onitoring the conditions of various train modules such as traction, climate, brake electronics and so forth. When an abnormal or particular situation occurs, the system will generate and store an event log on the local disk or on a remote disk using wireless data communications. These diagnostic events might give an indication of the train condition, and currently critical events are selected by business rules to give alarms on failure or malfunction to the control room. To give a better prediction on the train status based on the condition monitoring data, sequences of diagnostic events instead of individual critical events are analyzed in this work. Moreover, train delays instead of train failures are used as targets for providing more insight on the degeneration behavior of trains. We have adopted the word sequence kernel for learning the similarity between all sequence pairs, where each diagnostic event is considered as a word. To include multi-length word interpretations, we propose to combine the word sequence kernels of various lengths, where length=1 means one word is matched, length=2 means two words are matched, and so on. A kernel machine or similarity-based model can be leant directly on this combined word sequence kernel. The experimental results demonstrate that combining word sequence kernels of different lengths can bring a richer description to similarity measurements and gives better prediction performance.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
013
Page Count: 
6
Submission Keywords: 
Data-driven Methods
Preventive and predictive maintenance
Condition-based Monitoring
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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