Pablo Garcia Sandia JSF PM Sandia National Laboratories (SNL) Albuquerque, NM 87185 Phone: (505)844-5799 Fax: (505)84 - PowerPoint PPT Presentation

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Pablo Garcia

Sandia JSF PM

Sandia National Laboratories (SNL)

Albuquerque, NM 87185

Phone: (505)844-5799 Fax: (505)844-3321

Email: pgarcia@sandia.gov

Web Site: reliability.sandia.gov

Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company,for the United States Department of Energy under contract DE-AC04-94AL85000.

Prognostics & Health Management (PHM)



Conditioned-Based Maintenance (CBM)

Total Productive Maintenance

System Availability

Reliability Centered Maintenance

Computerized Maintenance Management Systems (CMMS)

Preventive Maintenance

Inspection

Run to failure

1930

1950

2000

1990

Evolving Maintenance Strategies

“The function of maintenance in a world-class operating environment is not to simply maintain, but to provide reliable systems and to extend the life of systems at optimum costs.” Stanley Lasday, June 1997, Industrial Heating

Why PHM?

The High Cost of Maintenance

Estimated that U.S. industry needlessly squanders in excess of $200 billion each year on inadequate or unnecessary maintenance procedures.
One major company has stated that maintenance was its “single largest controllable cost opportunity representing $100-300 million per year corporate wide.”
DoD spends in excess of $85B/yr on maintenance & support activities
Universally, Industry & government have indicated need for better, more robust “predictive maintenance” capabilities.

System Health Condition

Functioning

Degrading

Failing

Time

Associated Cost with Time of Replacement

$$$

Prescriptive replacement of functioning “good” item

$$$$$$$

Lose of life and/or system

due to catastrophic failure

$ Optimum

Replace item with maximum usage before failure

Prognostics & Health Management

Definition: The capability to estimate the likelihood of a system failure over some future time interval so that appropriate actions can be taken. A Prognostics & Health Management (PHM) system consists of:

Raw Data

Sensor data
Historical maintenance/failure data

Diagnostics

Data fusion
Data interpretation

Prognostics

Predictions
System “health”

Health Management

What should be done
When should it be done

PHM Systems

PROBLEM: Prognostics and Health Management (PHM) systems must be highly customized to the specific equipment being monitored.
OUR GOAL is to develop a robust, general prognostics capability that can be applied to many situations.
OUR APPROACH is to develop a toolkit of reusable software objects and components and an architecture for their use.

Categorization of Failure Modes

Possible Failure Modes of System

Some – Can predict well with sensors (~10%-20%)
Some – Candidates for sensors, but technology not ready
Some – May or may not be candidates for sensors, but have good time-to-failure data
Some – Not candidates for sensors and have poor life data
Some – Random

MEMS Sensor Array

Pressure

Flow

Temperature

Tissue PH

Oxygen Tension

Orientation

MEMS Technology

Memory

CPU

Power

Generator

Communication Coil

Valves

PDP = Positive displacement pump

A3

A4

A5

MEMS Actuators

Wireless, Self-Powered MEMS Sensor & Actuator

Void Growth Analysis

Corrosion Analysis

Materials Aging

SNL Technologies Support PHM

Technologies for SNL’s NW’s Enhanced Surveillance & Life Extension Programs

Modeling & Simulation

Predictive analyses
Sensitivity/Uncertainty analyses
Optimization analyses

Microsensor Development

Fiber-optic chemical sensors
Integrated/Micromachine Sensors
Micromachine Ion Mobility Spectrometer
Wireless, self-powered sensors

Life Prediction Algorithms

Fuzzy Logic
Neural networks, Bayesian networks
Genetic Algorithms
Bayesian updating

Electronics Quality/Reliability Center

Failure analysis
Burn-in elimination & testing
Life prediction

Materials Aging

Life extension
Reliability predictions

Power Plant PHM

Manufacturing Facility PHM

Machine Tool PHM

F-16 ADG PHM

AH-64 PHM

Automotive PHM

ABL PHM

SNL PHM Activities/Programs

Nuclear Power Plant “Smart” Equipment

DOE Nuclear Energy Research Initiative (NERI)
Introduce PHM to selected power plant equipment

Manufacturing Facility PHM

DOE funded program
Implement PHM in manufacturing facility

Machine Tool PHM

DOE funded program
Implement PHM on SNL machine tools

F-16 Accessory Drive Gearbox (ADG)

Joint Shared Vision program with LM Aero
Extend replacement intervals

AH-64 Apache Attack Helicopter

Program with Apache PO
Utilize data from Flight Data Recorders

Automotive Industry On-Board Diagnostics for Emissions

Robust algorithm development for emissions control
Pattern recognition and decision algorithms

Airborne Laser PHM

Evidence Engine

Consequence Engine

System Health

Maintenance

Scenarios

Component Health

Consequence

Analysis

Raw Sensor Data

Optimal

Maintenance

Recommendations

Failure Mode Health

Sensor Feature Interpretation

(SPC, EWMA, NN)

Sensor Feature Extraction

SNL PHM System Architecture

Updated TTF Distributions

Estimates of Remaining Useful Life

Environmental Conditions
Maintenance History
Aging and Time-to-Failure

System Model

Data Fusion:

Bayesian

Belief

Networks

x

predicted service life

Consequence Engine Concept

PREDICTED SERVICE LIFE

What did they do ? (predicted loads)

and

How long did they do it ? (exposure)

predicted

loads

predicted

loads

Loads under normal operating conditions

Flight loads

predicted

loads

predicted

loads

profileB

profile C

profile A

profile D

predicted

exposure

predicted

exposure

predicted

exposure

predicted

exposure

x’

normal service life

Service Life (in hours)

Confidential and Proprietary

AlliedSignal

Flexible Manufacturing System (FMS)

PHM

Clients

Real Time Sensor Data

Data Server

Machine Repair Data

Monitoring multiple machines at facility
Data Server filters & stores real-time sensor data
Interfaces with CMMS
CMMS stores all historical maintenance data
Networked for remote access

CMMS

Factory Floor PHM

PHM Future Research Plans

Evidence Engine

Add more signal processing algorithms
Add a case-based reasoning capability
Determine how sensor data (such as accelerometer data tracking vibration) can be correlated with flight recorder data to detect fault conditions vs. harsh operating environments
Develop algorithms to incorporate inspection and maintenance data to update component Time-To-Failure

Consequence Engine

Add capability to allow redundancy in the underlying model
Enhance capability to update aging factors based on mission profile (referred to as the “variable loading” problem)
- Add capability to generate scenarios automatically and optimize on them (currently the consequence engine analyzes the scenario that the user inputs)

PHM Future Research Plans

A Systems Driven, Physics-Based Approach for PHM of Electronic Systems – Proposed Research

Problem:

Electronic failures appear random & unpredictable
Predicting such failures requires an understanding of underlying failure processes that does not exist
Research into failure processes is usually reactive, and proactive research into failure processes often not focused

Solution:

Utilize SNL’s strengths in materials aging modeling to gain an understanding of process behind “random” failures
Use a systems approach (a la system reliability optimization) to direct model development towards the most promising areas
Obtain physics-based underpinning on which a PHM capability for electronic systems can be built

Backup slides

Sensor Feature Extraction/Fusion

Sensor feature extraction: extract key information from high and low-bandwidth sensor data

Time synchronous averaging
Kurtosis and other moments
Level shifts
Peaks ...

Sensor fusion: Combine sensor features to draw conclusions

Data fusion algorithms
Bayesian Belief Networks, Neural Networks
Case-based and model-based reasoning

Evidence Engine

Integrates information from a variety of sources in order to take into account all the evidence that impacts a prognosis for system health:

Maintenance history
Time to Failure distributions
Aging data and algorithms
Environmental conditions.

Uses a Bayesian Belief Network (BBN) for information fusion
Additional prognostic algorithms are used to detect trends, abstract and identify sensor features, estimate remaining useful life, etc.

Consequence Engine

Purpose: Most prognostic systems focus solely on predicting remaining life. The Consequence Engine takes the predictions and simulates how various maintenance actions affect system level decisions in terms of cost, availability, mission effectiveness, etc.
Consequence Engine includes the following:

Database of scheduled activities
Discrete-event simulation capability

simulate failures, maintenance actions, downtime, etc.
calculate system performance measures such as Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR), Time to Failure (TTF), etc.

Capability to allow for different operational states (e.g., partially mission capable vs. up or down).

Virtual System

System Model

Simulated Failure and Repair Events

Simulated Sensor Data

Virtual System

Simulated Mission Profile

Performance Metrics:

Mission Completion Prob.

Maintenance Cost

Downtime

Availability

Parts Requirements

Scenario Generation

Planned Use Schedule

Virtual System Simulator

A software tool that simulates the behavior of a system including failures, maintenance, and sensor signals

Motivation
Real system experiences failures and repairs too infrequently to support PHM
Require capability for high-speed, realistic, reliability simulation of system to be monitored

- Support PHM design & testing
- Provide platform for realistic demos
- Analyze consequences of maintenance decisions

f(t)

Updated

TTF Distribution

Original

TTF Distribution

Current Time

2000

Time to Failure (hours)

PHM Goal: Update TTF Distribution

Note: The updated TTF Distribution may be based on a more severe mission profile, sensor indications, inspection results, etc. or a combination of these.