Fault Detection and Prognosis of Time Series Data with Random Projection Filter Bank

Sepideh Pourazarm, Amir-massoud Farahmand, and Daniel Nikovski
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phmc_17_027.pdf704.38 KBAugust 17, 2017 - 4:19pm

We introduce Random Projection Filter Bank (RPFB) as a general framework for feature extraction from time series data. RPFB is a set of randomly generated stable auto-regressive filters that are convolved with the input time series. Filters in RPFB extract different aspects of the time series, and together they provide a reasonably good summary of the time series. These features can then be used by any conventional machine learning algorithm for solving tasks such as time series prediction, and fault detection and prognosis with time series data. RPFB is easy to implement, fast to compute, and parallelizable. Through a series of experiments we show that RPFB alongside conventional machine learning algorithms can be effective in solving data-driven fault detection and prognosis problems.

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Submission Keywords: 
time series
fault detection
Fault Prognosis
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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