slide1 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Anomaly Detection for Prognostic and Health Management System Development PowerPoint Presentation
Download Presentation
Anomaly Detection for Prognostic and Health Management System Development

Loading in 2 Seconds...

play fullscreen
1 / 34

Anomaly Detection for Prognostic and Health Management System Development - PowerPoint PPT Presentation


  • 339 Views
  • Uploaded on

Anomaly Detection for Prognostic and Health Management System Development. Tom Brotherton. New Stealth Technology. Outline. What is Anomaly Detection Different types of anomaly detectors Radial Basis Function Neural Net Anomaly Detector The basics

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Anomaly Detection for Prognostic and Health Management System Development' - lotus


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
outline
Outline
  • What is Anomaly Detection
    • Different types of anomaly detectors
  • Radial Basis Function Neural Net Anomaly Detector
    • The basics
    • Comparison with other neural net approaches
    • Feature ‘off-nominal’ distance measures
    • Training
  • Implementations
    • 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
what is anomaly detection
What 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
      • Statistical models
approaches

Accuracy & Cost

Approaches
  • Ex: Gas Turbine Engine Deck: Component level physics model

Physics

  • State Variable Models (derived from physics)
  • Hybrid Model: Combine Physics + Empirical

Parametric- Estimate of physics

  • JPL: BEAM (coherence = model of linear relationships)
  • Neural nets (non-linear relationships)

Empirical- Derived from collected data

  • Fused empirical: BEAM + NN
  • Academic: Support Vector
  • Simple statistics

Applicability

empirical modeling
Empirical Modeling

An anomaly

Idea: Theoretical boundary (multi-dimensional ‘tube’) that data should lie within: - Nominal data is inside the boundary - Anomaly data is outside

Problem: How to estimate / approximate the boundary?

Collected ‘Nominal’ Data

Problem: What measurement(s) caused the anomaly?

Problem: How far off-nominal is the anomaly / feature?

rbf neural net anomaly detection the idea

NN = Model for Nominal Data

= Sample of nominal data

= Sample of anomalous data

?

‘Distance’ from

Nominal Model

Yes

RBF Neural Net Anomaly Detection: The Idea

Radial Basis Function (RBF) Neural Net Model

  • Dynamic data = Lots of NN basis units to model
    • Piecewise stationary approximation
  • 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 features relative to basis unit = Prognosis
slide8

MLP NN

RBF NN

?

?

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
slide9

Support Vector Machine Model

RBF Model

Support Vector 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

slide10

Mahalanobis

Mahalanobis

Distance s2

Distance s1

Feature Distance Calculation

NN = Model for Nominal Data

?

  • Nearest Neighbor Distance
slide11

Closest Basis Unit

Truth

- Truth: Single Feature X = ‘Bad’

  • Report: Feature X = ‘OK’ & Feature Y = ‘Bad’

Alternative Distance Calculation

NN = Model for Nominal Data

  • Alternative Distance = Which Basis Unit gives the smallest number of individual off-nominal features -> Hamming Distance (from digital communications decoding)
rbf nn architectures

Weights

Input features

Is output for Nominal?

=1  Yes

> 1- Likely

< 1-  ?

< 1-  No

0<  <  <1

Basis Units

‘RBF’ NN Architectures

DetectorOutput

Gaussian elliptical basis function :

Rayleigh basis function :

Fuzzy membership basis function :

Good for magnitude spectral data

* Basis function is ‘matched’ to the data distribution

For those who like things fuzzy

= Gaussian Mixture Model

slide13

Training : Neural Net Architectures – How to select parameters

  • Small number of clusters Small number of basis units Low False Alarms

- Large number of clusters  Good ‘tracking’ of data dynamics Large number of basis units

 Very general Missed detections

Too General ?

 More sensitive to outliers More false alarms

Over Trained ?

Don’t know a-priori what are the ‘best’ settings

slide14

False alarms?

Only 2 points = false alarm

Small scale factor

4 points persist over time = detection

M of N Detection

Idea: M of N detection allows one sample high false alarm rate – Then integrate over time to remove

  • Trade off single point detection capability vs false alarm rate
  • Large Scale Factor / Small N
    • Short – high SNR anomalies
  • Small Scale Factor / Large N
    • Long – persistent – low SNR anomalies

Large scale factor

Detection?False alarm?

alternatives
Alternatives
  • This technique works well
    • Demonstrated by Pratt & Whitney for C-17 F117 applications
      • Transient engine operations
    • Long time to train – lots of different 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 gas turbine operations

Input Signal Vector

Scale Signal

RegimeRecognition

Neural NetSelect

Neural NetDetection

Neural NetDetection

Neural NetDetection

Median Filter

Trained NNs

Off-Nominal

SignalDistance

DetectionFlag

Example Gas Turbine Operations

Break the big problem in to a set of small problems

  • Regime recognition
    • Regimes:
      • Transient Throttle up
      • Transient Throttle down
      • Steady state – B14 open
      • Steady state – B14 closed
anomaly detection of stationary regime detected data
Anomaly Detection of Stationary Regime Detected Data
  • Web Server Implementation for Helicopter Vibration Data
    • 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 user’s like it
      • RBF neural net implementation as well
    • Multi-CIs for a component
      • RBF neural net implementation
slide18

FWDLAT

FWDVRT

FWDSP

CPITVRT

CPITLAT

FWDXMSNVRT

FWDXMSNLAT

HB2

HB3

HB4

HB5

HB6

HB7

AFTLAT

AFTVRT

AFTSP

ENG1COMP

ENG1NOSE

ENG1AXIAL

ENG1LAT

ENG2COMP

ENG2NOSE

ENG2AXIAL

ENG2LAT

CBOXOCFA

CBOXOCLAT

APU

AFTFANLAT

AFTXMSNVRT

AFTXMSNLAT

XSHAFT1

XSHAFT2

  • Configuration
  • 36 Vibration Sensors
  • 2 Speed Sensors
  • 1553 connection to HUD

Main

D/S

Main

Rotor

Cockpit Control Head

USB Memory Drive

Parameter Data

CVR-FDR

USB Download

IAC-1209

Modern Signal Processing Unit (MSPU)

Accelerometer

Ethernet

Tach

Sensor

+28VDC Power

Other Connections

On Board System

Tail Gearbox

Advanced Rotor Smoothing / Engine Diagnostics

Engines

Transmissions

Intermediate Gearbox

Cockpit VMU

Absorbers

Hanger Bearings

  • 18 Sensors Installed – Vibration
  • Automated Exceedance Monitoring using HUD data
  • Automated engine HIT, Max Power Check and exceedances
  • Complete aircraft vibration survey in under 30 seconds
slide19

Aircraft / Server Physical Connectivity

SCARNG

USB Memory Stick

Data Download

AIRCRAFT

OEMs

VMEP

PARTNER

Browser

PC-GBS Remote

PC-GBS Facility

AARNG

INTERNET

Wireless link

PC-GBS Remote

PC-GBS Facility

Deployed Unit

PC-GBS Remote

slide20

Browser

Aircraft / Server Logical Connectivity

Facility Systems

Support Team- e-mail notification- Fleet level reports- Automated s/w upgrades

Portable System

  • Army P-GBS

Aircraft Maintenance-Electronic help desk- Automated data archive- Automated s/w upgrades

- Army F-GBS

Web Client

MDS Server

Help Desk

Network

Security

Automated

Data Archive

Data ArchiveA/C config files

Help Training Base

Electronic ManualsFAQs

Diagnostics

Prognostics

Anomaly

Detection

Fleet Statistics

& Reports

Anomaly

Detection

slide21

Advanced Engineering on the Web

The role of anomaly detection on the website is to detect and bring to engineering’s attention the MOST INTERESTING data = Something that has NOT been encountered before

- More normal data not really of interest

single feature anomaly detection
Single Feature Anomaly Detection

Boxplots = Simple statistics - single feature anomaly detector. No Gaussian assumption, just counting points. They seem to work very well!

Default based on boxplot statistics

User set

anomaly analysis
Anomaly Analysis

Summary of all aircraft

slide26

Original

Transformed

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 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
slide29

Case Study: Apache Swashplate Bearing Spectral Server Data

  • Anomalous data identified with RBF NN 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
  • Suspected bad swashplate bearing

Main SP Spectra

Other

A/C

Tail 460

Tail 460

Other A/C

slide30

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 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
Follow Up
  • Specific algorithms to identify this fault now included with the on-board system
  • US Army now uses ‘on-condition’ information from the system to perform maintenance
    • True condition-based maintenance (CBM)
other applications
Other Applications

Water Quality Bio-Monitor

  • 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.
  • Sixteen 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.
  • 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 fish’s normal movement is amplified, filtered and passed on via the internet to IAC’s Bio-Monitoring Expert (BME) software system for automated analysis.
water quality bio monitor
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 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 intelligence) 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. BME’s modular design provides users with the ability to reconfigure the system for different biomonitoring applications and biosensors
questions

Questions?

Conference papers / case studies available at:

www.iac-online.com