1、Anomaly Detection for Prognostic and Health Management System Development,Tom Brotherton,New Stealth Technology,Outline,What is Anomaly Detection Different types of anomaly detectorsRadial Basis Function Neural Net Anomaly Detector The basics Comparison with other neural net approaches Feature off-n
2、ominal distance measures TrainingImplementations Continuous = Gas turbine engine monitoring Snap shot = Web server helicopter vibration condition indicators RBF NN & Boxplots Application to detection of helicopter bearing fault Application to monitoring fish behavior for water quality monitoring,Wha
3、t is Anomaly Detection?,Anomaly Detection = The Detection of Any Off-Nominal Event Data Known fault conditions Novel event = New - never seen before data New type of fault New variation of known nominal or fault data What is Nominal Sets of parameters that behave as expected Physics models Statistic
4、al models,Approaches,Applicability,Physics,Parametric - Estimate of physics,Empirical - Derived from collected data,State Variable Models (derived from physics),JPL: BEAM (coherence = model of linear relationships),Neural nets (non-linear relationships),Academic: Support Vector,Ex: Gas Turbine Engin
5、e Deck: Component level physics model,Simple statistics,Hybrid Model: Combine Physics + Empirical,Fused empirical: BEAM + NN,Empirical Modeling,Collected Nominal Data,Idea: Theoretical boundary (multi-dimensional tube) that data should lie within: - Nominal data is inside the boundary - Anomaly data
6、 is outside,Problem: How to estimate / approximate the boundary?,An anomaly,Problem: What measurement(s) caused the anomaly?,Problem: How far off-nominal is the anomaly / feature?,RBF Neural Net Anomaly Detection: The Idea,Dynamic data = Lots of NN basis units to model Piecewise stationary approxima
7、tion Distance measure = Function of the signal set Individual signal distances from nominal = distance from “closest” basis unit Detection can be for set of signals when no single signal is anomalous The model can be adaptively updated to include additional data / known fault classes Trajectories of
8、 features relative to basis unit = Prognosis,Radial Basis Function (RBF) Neural Net Model,Why Use Radial Basis Function Neural Nets?,Radial Basis Function Neural Net Nearest neighbor classifier Distance metric : Measure “nominal” Multi-layer perceptron (MLP) does not have these properties,Support Ve
9、ctor Machine,In some sense, much better model of truth . but Automated selection of number of basis units Lots! Trade off between fidelity vs smoothness Not practical for on-wing How to compute individual signal distances Loss of intuition,Training data,NN = Model for Nominal Data,Feature Distance C
10、alculation,?,Nearest Neighbor Distance,NN = Model for Nominal Data,Alternative Distance Calculation,Alternative Distance = Which Basis Unit gives the smallest number of individual off-nominal features - Hamming Distance (from digital communications decoding),RBF NN Architectures,Gaussian elliptical
11、basis function :,Fuzzy membership basis function :,Rayleigh basis function :,Detector Output,= Gaussian Mixture Model,Good for magnitude spectral data * Basis function is matched to the data distribution,For those who like things fuzzy,Small number of clusters Small number of basis units Low False A
12、larms, Very general Missed detectionsToo General ?,- Large number of clusters Good tracking of data dynamics Large number of basis units, More sensitive to outliers More false alarmsOver Trained ?,Dont know a-priori what are the best settings,Training : Neural Net Architectures How to select paramet
13、ers,RBF Training,Cluster the data to form Basis Units K-means clustering Assumes no a-priori knowledge of data relationships Optimization to determine centers and included points Alternative Clustering Take advantage of fact that data is continuous in time Clusters will be contiguous in time Determi
14、nistic so no optimization required 500xs faster the K-means cluster Weights are found via LMS estimate,M of N Detection,Detection? False alarm?,Large scale factor,Trade off single point detection capability vs false alarm rateLarge Scale Factor / Small N Short high SNR anomaliesSmall Scale Factor /
15、Large N Long persistent low SNR anomalies,Idea: M of N detection allows one sample high false alarm rate Then integrate over time to remove,Alternatives,This technique works well Demonstrated by Pratt & Whitney for C-17 F117 applications Transient engine operations Long time to train lots of differe
16、nt types of transients Model can become very complex Engine control system On-wing memory and timing constraints Alternative Combine equipment operating regime recognition with anomaly detector Ex: Identify steady operation and then take a snapshot of the data Simple statistics may suffice,Example G
17、as Turbine Operations,Regime recognition Regimes: Transient Throttle up Transient Throttle down Steady state B14 open Steady state B14 closed,Break the big problem in to a set of small problems,Anomaly Detection of Stationary Regime Detected Data,Web Server Implementation for Helicopter Vibration Da
18、ta Condition Indicators (CIs) = Features derived from on-board vibration measurements Two types of problems: Single CI for a component Simple statistics solution = Boxplot Intuitive = Army users like it RBF neural net implementation as well Multi-CIs for a component RBF neural net implementation,On
19、Board System,Absorbers,Hanger Bearings,Tail Gearbox,Intermediate Gearbox,Transmissions,Engines,Cockpit VMU,Advanced Rotor Smoothing / Engine Diagnostics,18 Sensors Installed VibrationAutomated Exceedance Monitoring using HUD dataAutomated engine HIT, Max Power Check and exceedancesComplete aircraft
20、vibration survey in under 30 seconds,Aircraft / Server Physical Connectivity,SCARNG,Deployed Unit,AARNG,INTERNET,AIRCRAFT OEMs,VMEP PARTNER,PC-GBS Facility,PC-GBS Facility,PC-GBS Remote,PC-GBS Remote,PC-GBS Remote,USB Memory Stick Data Download,Browser,Wireless link,Aircraft / Server Logical Connect
21、ivity,Army P-GBS,Support Team - e-mail notification - Fleet level reports - Automated s/w upgrades,Aircraft Maintenance -Electronic help desk - Automated data archive - Automated s/w upgrades,Fleet Statistics & Reports,Help Desk,Data Archive A/C config files,MDS Server,Help Training Base Electronic
22、Manuals FAQs,Prognostics,Diagnostics,Network Security,Automated Data Archive,Anomaly Detection,Portable System,- Army F-GBS,Web Client,Facility Systems,Anomaly Detection,Advanced Engineering on the Web,The role of anomaly detection on the website is to detect and bring to engineerings attention the
23、MOST INTERESTING data = Something that has NOT been encountered before- More normal data not really of interest,Default based on boxplot statistics,User set,Single Feature Anomaly Detection,Boxplots = Simple statistics - single feature anomaly detector. No Gaussian assumption, just counting points.
24、They seem to work very well!,Threshold Setting,Anomaly Analysis,Summary of all aircraft,The Raw Data,Gaussian Transformation Data,Problem: How to select a “matched” basis function Gaussian assumption? Usually violated! Statistical Model Fit Transform data to be Gaussian Transformation stored and is
25、part of the model Almost always only a single basis unit is required! Works on single feature data All processing “behind the scenes” done on transformed data,RBF Anomaly Detection,RBF Anomaly Detection,Case Study: Apache Swashplate Bearing Spectral Server Data,Anomalous data identified with RBF NN
26、AD running on the Server Aircraft was in Iraq Automatic email alert sent to users “Evidence” sent as well Data reviewed by AED-Aeromechanics and IAC via iMDS website Large peak in spectral data at 1250 Hz for tail #460 Sidebands spaced at intervals corresponding to bearing fault frequencies Suspecte
27、d bad swashplate bearing,Tail 460,Tail 460,Other A/C,Other A/C,Main SP Spectra,Case Study Apache Swashplate Bearing,AED-Aeromechanics acquired raw vibe data Apr 04 and received swashplate May 04 before aircraft was turned-in for D model conversion Swashplate disassembled by PIF per DMWR Aug 04 Minor
28、 spalling, corrosion and broken cage discovered Additional algorithms developed from raw data and implemented into VMEP for release Sep 04,Broken Cage,Spalling/Corrosion,Follow Up,Specific algorithms to identify this fault now included with the on-board system US Army now uses on-condition informati
29、on from the system to perform maintenance True condition-based maintenance (CBM),Other Applications,IAC 1090 is a mobile, web-enabled automated biomonitoring system that utilizing the ventilatory and body movement patterns of the bluegill fish as a bio-sensor, much like a canary in a coal mine. Sixt
30、een Bluegills are placed in individual flow-through Plexiglas chambers. Each chamber is equipped with an individual water input and drainage system. By utilizing sixteen different Bluegills, the IAC 1090 samples more biosensors than any other system on the market resulting in lower false alarm rates
31、.All fish generate a micro volt level electric field. Each individual fish is monitored by non-contact electrodes suspended above and below each fish in a Plexiglas chamber. The electrical signals generated by the fishs normal movement is amplified, filtered and passed on via the internet to IACs Bi
32、o-Monitoring Expert (BME) software system for automated analysis.,Water Quality Bio-Monitor,BME is a neural network based expert system that provides for rapid, real time assessment of water toxicity based on the ventilatory behavior of fish. BME has shown excellent detection capabilities for toxic
33、compounds with a low false alarm rate. False alarms, common in other similar systems, are typically generated by normal, non-toxic variations in the environment. Automated data collection and management tools, user interfaces, and real-time data interpretation employing advanced (artificial intellig
34、ence) models of fish ventilatory behavior make BME easy to use. Remote (Internet) access to IAC 1090 is provided through an easy-to-use graphical user interface. BMEs modular design provides users with the ability to reconfigure the system for different biomonitoring applications and biosensors,Water Quality Bio-Monitor,Questions?,Conference papers / case studies available at: www.iac-,
