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Event Forewarning from Time-Series Analysis Future Soldier Conference London (14 April 2005) Lee M. Hively (presenter) Vladimir A. Protopopescu Oak Ridge National Laboratory (ORNL) * * Managed by UT-Battelle, LLC, for the USDOE under Contract No. DE-AC05-00OR22725.

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slide1

Event Forewarning from Time-Series Analysis

Future Soldier Conference

London (14 April 2005)

Lee M. Hively (presenter)

Vladimir A. Protopopescu

Oak Ridge National Laboratory (ORNL)*

*Managed by UT-Battelle, LLC, for the USDOE under Contract No. DE-AC05-00OR22725

problem overview and aims
Problem: overview and aims
  • Key to reliability and survivability is …
  • Forewarning of adverse events …
  • Which arise from condition change …
  • That is hidden in noisy, complex data
  • Quantify change via practical technology:

Fast – (near) real time

Accurate – maximize total trues

Portable – Ambulatory device

solution quantify condition change
SOLUTION: quantify condition change

Acquire Process-Indicative Data

Remove Irrelevant Artifacts

Capture Signature of Base Case Dynamics

Capture Signature of Test Case

Dissimilarity Between the Two

Forewarning via Threshold Crossing

data quality check
Data Quality Check
  • Proper number of data points?
  • Significant period(s) with no change?
  • Is sampling rate too low?
  • Monotonic increases or decreases?
  • Excessive quasi-periodicities?
  • Excessive noise?
  • Data correctly scaled?
  • Consistent signal amplitude?
slide5

Artifact Removal

Time-serial data: xi

Raw (EEG) data (3 s)

Artifact (eyeblink) removal

Artifact-filtered data

slide6

Phase-Space Reconstruction

Construct phase space (PS):

y(i) = [xi, xi+] in 2D

y(i) = [xi, xi+, …, xi+(d-1)]

Distribution function (DF) of PS points to capture invariant properties of the dynamics for comparison to sequel DFs

multi channel reconstruction
Multi-Channel Reconstruction
  • Data from channel A: ai
  • Data from channel B: bi
  • Data from channel C: ci
  • y(i) = [ai, ai+, …, ai+(d-1),

bi, bi+, …, bi+(d-1),

ci, ci+, …, ci+(d-1)]

phase space dissimilarity measures
Phase-Space Dissimilarity Measures
  • 2 = k (Pk – Qk)2/(Pk + Qk)
  • L = k |Pk – Qk|

visitation frequency & location in PS

  • Dissimilarity measures:

subtract, then integrate … moresensitive

- Traditional measures:

integrate, then subtract … less sensitive

- Renormalization for consistency

example 1 eeg data
Example 1: EEG data
  • Biomedical Monitoring Systems Inc.
  • Sample rate = 250 Hz
  • 19 channels of scalp data
  • Band-pass filtered: 0.5 - 99 Hz
  • Datasets span 5,000 – 29,500 seconds
  • 60 datasets: 40 event, 20 non-event
  • Multiple datasets: 30 from 11 patients
  • 36 females and 24 males: 4years57
example 2 cardiac events
Example 2: cardiac events
  • 5 electrocardiogram datasets
    • Digital data from Holter recordings
    • Analysis of one channel (250 Hz)
    • Datasets spanned <1 hour
  • Dissimilarity analysis for forewarning
example 3 breathing difficulty
Example 3: breathing difficulty
  • Test at Walter Reed Medical Ctr.

- anesthetized pig

- 0 to 1400 ml of air into pleural space

- surface (chest) stethoscope

- sampling rate = 10 kHz

  • Basecase for normal breathing (0 ml)
  • Testcases for 100 ml increments
example 4 sepsis onset
Example 4: sepsis onset
  • 23 anesthetized rats at UTKMC

- 17 exposed to inhaled endotoxin

- 6 exposed to de-ionized water

  • 4 surface ECG electrodes (500 Hz)
  • Test protocol (1.5 to 3 hours total)

- 30 to 60 minutes for baseline

- 30 minutes of Salmonella endotoxin

- 30 to 90 minutes of recovery

example 5 fainting
Example 5: fainting
  • Experiments at University of Ky
  • ECG (250 Hz )
  • Flat (10m); 70° tilt (60m); flat (5m)
  • Two human subjects

RAY/PSB (event), PSA (no event)

RUI/PSB (event), PSA (no event)

example 5 fainting results 3
Example 5: fainting results (3)
  • RAY event

much larger slope (>12x)

larger values (4x)

  • RUI event

much larger slope (>1149x)

much larger values (>34x)

example 6 seeded motor fault
Example 6: seeded motor fault

Nominal = no fault

1st fault = rotor bar cut half way thru

 2nd fault = same rotor bar cut 100%

3rd fault = second rotor bar cut 100%

4th fault = 2 more rotor bars cut 100%

Motor power at 40kHz

examples of machine failure prognostication
Examples of Machine Failure Prognostication

Data ProviderEquipment and Type of Failure Diagnostic Data

1) EPRI (S) 800-HP electric motor: air-gap offset motor power

2) EPRI (S) 800-HP electric motor: broken rotor motor power

3) EPRI (S) 500-HP electric motor: turn-to-turn short motor power

4) Otero/Spain (S) ¼-HP electric motor: imbalance acceleration

5) PSU/ARL (A) 30-HP motor: overloaded gearbox load torque

6) PSU/ARL (A) 30-HP motor: overloaded gearbox vibration power

7) PSU/ARL (A) 30-HP motor: overloaded gearbox vibration power

8) PSU/ARL (S) crack in rotating blade motor power

9) PSU/ARL (A) motor-driven bearing vibration power

10) EPRI (S) 800-HP electric motor: air-gap offset vibration power

11) EPRI (S) 800-HP electric motor: broken rotor vibration power

12) EPRI (S) 500-HP electric motor: turn-to-turn short vibration power

13) PSU/ARL (A) 30-HP motor: overloaded gearbox vibration power

14) PSU/ARL (A) 30-HP motor: overloaded gearbox vibration power

15) PSU/ARL (A) 30-HP motor: overloaded gearbox vibration power

16) PSU/ARL (A) 30-HP motor: overloaded gearbox vibration power

17) PSU/ARL (S) crack in rotating blade vibration power

(S) = seeded fault

(A) = accelerated failure

technology status now at trl 5
Technology status: Now at TRL 5

 High-fidelity technology integration

  • Tests in simulated environment
  • Failure forewarning …
  • Via analysis of archival data …
  • On desktop computer …
  • 6 patents and two patents pending

(US government retains patent rights)

maturing from trl5 to trl7
Maturing from TRL5 to TRL7
  • Analysis now on iPAQ3790…
  • With graphical user interface …
  • For on-line analysis of …
  • Real-time data …
  • Robust choice of parameters …
  • Yielding accurate predictions…
  • That are event- and duration-specific…
  • And analyst-independent

TRL6 prototype

encouraging results more r d needed
Encouraging Results … More R&D Needed
  • Desktop data analysis is faster than real-time
  • Present technology is approaching TRL6
  • Good total-true forewarning rate
  • BUT …
  • Finding parameters is much slower than real-time

And parameter determination is analyst-intensive

  • Present parameters are not sufficiently robust
  • Method is not specific … does not distinguish different event types
conclusions commercial prognostic
Conclusions: commercial prognostic
  • Data-driven, non-intrusive, fast
  • Provides robust, timely forewarning
  • Examples congruent with soldier use:

- chest sounds

- sepsis/ECG

- epilepsy/EEG

- fainting/ECG

- motor failure

conclusions military prognostic
Conclusions: military prognostic
  • Data-driven, non-intrusive, fast
  • Provides robust, timely forewarning
  • Examples congruent with soldier use:

- chest sounds abdominal wound

- sepsis/ECG  bio-warfare agent

- epilepsy/EEG  neurotoxin

- fainting/ECG  heat exhaustion

- motor failure  equipment failure

questions
QUESTIONS

Office: 865-574-7188

Fax: 865-576-5943

  • http://computing.ornl.gov/cse_home/staff/hively.shtml
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