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

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Published Oct 2, 2017
Sepideh Pourazarm Amir-massoud Farahmand Daniel Nikovski

Abstract

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 autoregressive 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.

How to Cite

Pourazarm, S., Farahmand, A.- massoud, & Nikovski, D. (2017). Fault Detection and Prognosis of Time Series Data with Random Projection Filter Bank. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2426
Abstract 417 | PDF Downloads 235

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Keywords

fault detection, time series, Fault Prognosis

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Section
Technical Research Papers