IEEE SMC 2014

[email protected] http://www.flll.jku.at/staff/franciscoFrancisco Serdio

IEEE SMC 2014 – San Diego, October 5-8, 2014

Reducing False Positives for Residual-Based On-line Fault Detection

by Means of Filters

Francisco Serdio Fernández

Department of Knowledge-Based Mathematical Systems

Johannes Kepler University Linz, Austria





IntroductionApproachResultsConclusions



Fault Detection

Operator Monitoring Tools

System



Operators

There are systems where the operators are only in charge of the monitoring



Operators

There are systems where the operators are also in charge of the operation of the system



Too many false positives Operator… Gets distracted Loses confidence on the system Could even decide to do not use the system anymore

False positives

Jacob Bortman. Invited speaker at PHM 2013, New Orleans, 2013



Fight the false positives !!






Filters

Use filters in the residual space



Filters

There are some from other domains Engineering, Image processing, Econometrics, etc

We used… Moving Average Modified Moving Average Moving Median Gaussian



Well known in image processing Used to smooth noisy pictures

Moving Average

C. Glasbey and R. Jones, Fast computation of moving average and related filters in octagonal windows, Pattern Recognition Letters, vol. 18, pp. 555–565, 1997.



Well known in econometrics It’s a particular exponential moving average

Modified Moving Average

D.S. Grebenkov and J.Serror, Following a trend with an exponential moving average: Analytical results for a Gaussian mode, Physica A: Statistical Mechanics and its Applications, vol. 394, pp. 288–303, 2014.



Well known in image processing Used to deal with impulse noise

Moving Median

A. M. Kizrak and F. Örzen, A new median filter based fingerprint recognition algorithm, Procedia Computer Science, vol. 3, pp. 859–865, 2011.



Applications in electronics and signal processing Applications in image processing

Gaussian

ISO 11562:1996. Geometrical Product Specifications (GPS) surface texture: profile method metrological characteristics of phase correct filters.



All filters together



Testing Environment

We tested a real scenario engine test bench We used artificial faults

100 faults 50 runs * 2 faults / run 5 fault intensities 5%, 10%, 20%, 50%, 100%






Results



Results

Linear Regression Moving Median Filter Window size = 8



Results



Results

PLS Regression Moving Median Filter Window size = 8



Results



Results

Fuzzy Systems SparseFIS Moving Median Filter Window size = 4






Conclusions

Filtering the Residual Space Reduces False Positives Increases True Positives

Median Filters Appear to be the more promising

Other domains… Could bring ideas to ours



Thanks a lot for your attention!

Presentations & Public Speaking

IEEE SMC 2014