Data Mining and Gated Expert Neural Networks for Prognostic of Systems Health Monitoring

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Data Mining and Gated Expert Neural Networks for
Prognostic of Systems Health Monitoring

• Mo Jamshidi, Ph.D., DEgr., Dr. H.C.
• F-IEEE, F-ASME, F-AAAS, F-NYAS, F-HAE, F-TWAS
• Regents Professor, Electrical and Computer Engr.
  Department
• Director, Autonomous Control Engineering (ACE)
  Center
• University of New Mexico, Albuquerque, NM, USA
• Advisor, NASA JPL (1991-93), Headquarters
  (1996-2003)
• Sr. Research Advisor, US AF Research Lab.
  (1984-90,2001-present)
• Consultant, US DOE Oak Ridge NL (1988-92), Office
  of Renewable Energy (2001-2003)
• Vice President, IEEE Systems, Man and Cybernetics
  Society
• http//ace.unm.edu www.vlab.unm.edu
  moj_at_wacong.org.org
• Fairbanks, Alaska, USA May 24 2005

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OUTLINE

• Definition of Prognostics
• History of Prognostics
• Approaches of Prognostics
• Principle Component Analysis PCA
• PCA via Neural Network Architecture
• Prognostics via Neural Networks
• Gated Approach to Hardware Prognostics
• Applications Health and Industry
• Conclusion and Future Efforts

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Prognostics vs. Diagnostics vs. Health Monitoring
Are They the Same?

• Health Monitor v to keep track of current
  status systematically with a view to collect
  information.
• Diagnosis n identifying the nature or cause
  of some phenomenon.
• Prognosis n a prediction about how something
  (as the weather) will develop, forecasting.
• Conclusion they are not the same
• The Websters New World Dictionary.

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So How Are They Related?

• Health monitoring uses instrumentation to collect
  information about the subject system.
• Diagnostics uses the information in real time
• to detect abnormal operation or outright
  faults.
• Prognostics uses the information to predict the
  onset of abnormal conditions and faults prior the
  actual failure to allow the operators to
  gracefully plan for shutdown or, if required,
  operate the system in a degraded but safe-to-use
  mode until a shutdown and maintenance can be
  accomplished.

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A Brief History of Automated Diagnostics and
Prognostics

• Before the advent of inexpensive computing,
  diagnosis was ad-hoc, manual, and depended on
  human experts.
• With the advent of accessible digital computers,
  early expert systems attempt diesel locomotive
  engine diagnostics based on oil analysis. Humans
  still required for prognostics.
• 1970s saw the start of equipment health
  monitoring for high-value systems (i.e. nuclear
  power plants) and on-line diagnostics using
  minicomputers. Human interpretation was still
  required.
• 1980s saw the use of personal computers and
  digital analyzers to do equipment health
  monitoring. Some automatic shut-down on extreme
  exception was included, but human involvement was
  still required.

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A Brief History (Contd.)

• 1990s saw built-in test and real-time
  diagnostics added to military electronics and
  high-value civilian systems. Health
  monitoring/diagnostics at this point were
  evolving into decision support systems for the
  operator.
• NOW Diagnostics pervasive
• Automobiles (On Star , OBD II, heavy equipment,
  trucks, etc.)
• Electronics/electro-mechanical devices (copiers,
  complex manufacturing equipment, etc.)

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A Brief History (Contd.)

• Aviation (Boeing-777, Air Bus, etc.)
• Prognostics at the component/ subsystem level
  start to appear for the first time.
• Still no system-wide prognostics! By and large,
  prognostics are still done by the human operators
  deciding how much further they can go before
  stopping.

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Literature Survey

• Diagnostics are well developed.
• Prognostics are not!
• Logical next step Intelligent System Level
  Prognostics

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Approaches to Diagnostics and Prognostics

• Data Driven Methods
• Analytical Methods
• Knowledge based Methods

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Data Signatures

• Library of predictive algorithms based on a
  number of advanced pattern recognition techniques
  - such as multivariate statistics, neural
  networks, signal analysis
• Identify the partitions that separate the early
  signatures of functioning systems from those
  signatures of malfunctioning systems

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Predictive indicators of failures

• A viable prognostic system should be able to
  provide an accurate picture of faults, component
  degradation, and predictive indicators of
  failures
• Allowing our operators to take preventive
  maintenance actions to avoid costly damage on
  critical parts and to maintain availability/readin
  ess rates for the system.

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Data Driven Methods

• The huge amount of data has to be reduced
  intelligently for any careful fault diagnosis.
• Reduce the superficial dimensionality of data to
  intrinsic dimensionality (i.e., number of
  independent variables with significant
  contributions to nonrandom variations in the
  observations).

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Data Driven Methods

• Feature extraction
• Partial Least Square (PLS)
• Fisher Discriminant Analysis
• Canonical Variate Analysis
• Principal Component Analysis
• We will only focus on PCA and its non-linear
  relative (NLPCA).

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Principal Component Analysis

• What is PCA?
• It is a way of identifying patterns in data, and
  expressing the data in such a way as to highlight
  their similarities and differences. Since
  patterns in data can be hard to find in data of
  high dimension, where the luxury of graphical
  representation is not available.

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Principal Component Analysis

• PCA is a powerful tool for analyzing data.
• The other main advantage of PCA is that once you
  have found these patterns in the data, and you
  compress the data, i.e. by reducing the number of
  dimensions, you have not much loss of
  information.

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PCA

• The feature variables in PCA (also referred to as
  factors) are linear combinations of the original
  problem variables.

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Classical Statistics based PCA steps

1. Get Data
2. Subtract the mean
3. Calculate the covariance matrix
4. Calculate eigenvalues and eigenvectors of
   covariance matrix
5. Choose feature vector (data compression begins
   from here)
6. Derive the new data set (reduced)

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Principal Component Analysis (PCA)

• Assuming a data set of containing n
  observations and m variables (i.e., a n x m
  matrix), PCA divides into two matrices or
  the scores dimension (n x f) and which is the
  loading matrix dimension (m x f) plus a matrix
  of residuals of dimension (n x m).

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Principal Component Analysis (PCA)

• It is known that PCA optimizes the process by
  minimizing the Euclidean norm of the residual
  matrix .
• To satisfy this condition, it is known that
  columns of are the eigenvectors corresponding
  to the f largest eigenvalues of the covariance
  matrix of .

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Principal Component Analysis (PCA)

• In other words, PCA transforms our data from m to
  f dimension by providing a linear mapping
• where represents a row of the original data
  set and represents the corresponding row
  of .

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Non-Linear PCA (NLPCA)

• In Kramers NLPCA, the linear transformation in
  PCA is generalized to any nonlinear function such
  that
• where is a nonlinear vector function composed
  of f individual nonlinear functions analogous to
  the columns of .

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Non-Linear PCA (NLPCA)
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Analytical Methods

• The analytical methods generate features using
  detailed mathematical models.
• Based on the measured input and output ,
  it is common to generate residuals , parameter
  estimates , and state estimates .
• The residuals are the outcomes of consistency
  checks between the plant observations and a
  mathematical model.

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Integrated Method for Fault Diagnostics and
Prognostics (IFDP)

• Based on
• NLPCA for dimensionality reduction
• Society of experts (E-AANN, KSOM, RBFC)
• Gated Experts
• All developed in Matlab with Simulink for model
  simulations

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Extended Auto-Associative Neural Networks (E-AANN)
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Kohonen Self-Organizing Maps (KSOM)

• KSOM defines a mapping from the input data space
  ?n onto a regular two-dimensional array of nodes.
  
• In the System, a KSOM input is a vector combining
  both inputs and outputs of a certain the System
  component.
• Every node i is defined by a prototype vector mi
  ? ?n. Input vector x ? ?n is compared with every
  mi and the best match mb is selected.

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Kohonen Self-Organizing Maps (KSOM)

Three-dimensional input data in which each sample
vector x consists of the RGB (red-green-blue)
values of a color vector.
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Radial Basis Function based Clustering (RBFC)

• The RBF rulebase is identified by our clustering
  algorithm.
• We will consider a specific case of a rulebase
  with n inputs and a single output. The inputs to
  the rulebase are assumed to be normalized to fall
  within the range 0,1.

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Gated Experts for Combining Predictions of
Different Methods

• The Gated Experts (GE) architecture Weigened et
  al, 1995 was developed as a method for
  adaptively combining predictions of multiple
  experts operating in an environment with changing
  hidden regimes.
• The predictions are combined using a gate block,
  which dynamically assigns probabilities to the
  forecast of each expert being correct based on
  how close the current regime in the data fits the
  area of expertise for that expert.

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Gated Experts for Combining Predictions of
Different Methods

• The training process for the GE architecture uses
  the expectation-maximization (EM) algorithm,
  which combines both supervised and unsupervised
  learning.
• The supervised component in experts learns to
  predict the conditional mean for the next
  observed value, and the unsupervised component in
  the gate learns to discover hidden regimes and
  assign the probabilities to experts forecasts
  accordingly.

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Gated Experts for Combining Predictions of
Different Methods

• The unsupervised component is also present in
  experts in the form of a variance parameter,
  which each expert adjusts to match the variance
  of the data for which it was found most
  responsible by the gate.

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Prototype Hardware Implementations

• A Chiller at Texas AM University with (Langari
  and his team)
• A laser pointing system prototype at the
  University of New Mexico (Jamshidi and ACE team)
• A COIL laser at AFRL - USAF (Jamshidi Stone)
• A flash memory line at Intel Corp. (Jamshidi
  Stone)

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Chiller Model at Texas AM University

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Training Data and Test Data
Whole data with 1000 samples
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Training Data and Test Data
Normalized training data with 2 noise (sorted)
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Training Data and Test Data
Normalized test data with 2 noise (sorted)
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One Sensor with Drift Error
Test data with 2 noise, sensor 3 has drift error
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One Sensor with Drift Error
Drift error and sensor 3 data
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One Sensor with Shift Error
Test data with 2 noise, sensor 3 has shift error
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One Sensor with Shift Error
E-AANN output, the input data had 2 noise and
shift error
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One Sensor with Shift Error
Shift error and sensor 3 data
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One Sensor with Shift Error
The difference between E-AANN input and output,
the input data had 2 noise and shift error
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PCA Application to Cardiac Output

• Cardiac output is defined by two factors.
• Stroke volume
• Heart Rate
• Cardiac Output Heart rate X Stroke volume
• (ml/min) (beats/min)
  (ml/beat)
• CO for basal metabolic rate is about 5.5L/min
  

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The human heart
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Prognostics of CO using PCA Analysis

• PCA is used in identifying patterns in data, and
  expressing the data in such a way to highlight
  their similarities and differences.
• PCA assists us in making an accurate prognostic
  analysis of a patients Cardiac output performance
  and hence predict possible heart failures.

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Good data representation
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• By taking several measurements of CO, one is able
  to predict the possibilities of heart failure,
  and this allows for PCA to be very useful in the
  prognostics of Cardiac output.
• PCA takes these millions of output measurements
  and crunches them into a graph representation,
  from which we can easily visualize CO defects.

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Why prognostics ?

• In medicine, the cheapest way to cure disease is
  to prevent it. This is done with early
  diagnostics, medicines, vaccines, etc..
• However with an accurate prognostics approach,
  conditions like heart attack and heart failure
  can be greatly minimized.
• PCA enables us to arrive at prognostics.

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Parkinson's Disease Tremors

• a) No medication nor brain Stimulation
• b) Brain Stimulation no medication
• c) No brain stimulation and medication
• d) Bran stimulation and medication

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Test 1 Tests made on the differences and
similarities in patients that have both
medication and brain stimulation on vs.
medication off and brain stimulation on.
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Test 2 Tests made on the differences and
similarities in patients that have both
medication and brain stimulation on vs.
medication on and brain stimulation off.
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Test 3 Tests made on the differences and
similarities in patients that have both
medication and brain stimulation on vs.
medication off and brain stimulation off.
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Test 4 Tests made on the differences and
similarities in patients that have medication on
and brain stimulation off vs. medication off and
brain stimulation on.
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PCA Image Processing - ORIGINAL REDUCED 10
EIGENVECTORS
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PCA ORIGINAL REDUCED 20 EIGENVECTORS
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PCA ORIGINAL REDUCED 30 EIGENVECTORS
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PCA ORIGINAL REDUCED 40 EIGENVECTORS
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PCA ORIGINAL REDUCED 54 EIGENVECTORS
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USING ALL 325 EIGENVECTORS

• With all 325 eigenvectors we can see that this
  image looks the same as our image with only 54
  eigenvectors.

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PCA PERCENTAGES
Eigenvectors Of Eigenvectors Used
10 5.20
20 10.42
30 15.63
40 20.83
54 28.10
325 100
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Laser Pointing System at UNM
Lab View Controller Algorithm

ADC
DAC
DAC
X/Y motors
Mirror
Filter
Detector Quadrant
L A S E R
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Prognostics Possible test beds

• Chemical
• Laser
• System
• ATL
• Advanced
• Tactical
• Laser

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Prognostics Possible test beds

• Large Gimbal system
• hardware system -
• NOP (North Oscura
• Peak) System

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HARDWARE Prognostic System
Knowledge Base (NOP Senior Engineers)
Original Data
RBFC
Outputs
Relevant Data

Inputs
NOP Subsystem
Data Reduction Expert System
KSOM
GE-NN
PCA
Reduced Dominant Data
E-AANN
Inputs
NOP Diagnostic Prognostic System
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Architecture
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The Intel Flash Memory Assembly Line

• The Intel flash memory assembly line is a state
  of the art system that uses many sensors to
  monitor operating conditions.

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PCA

• Hundreds of sensors produce thousands of signal
  inputs per minute on the assembly line. Most of
  the incoming data is irrelevant. Principal
  component analysis finds the relevant information
  among the explosion of data and provides it to a
  computer for analysis.

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Feature Extraction

• PCA is used to reduce the dimensionality of
  the sensor data and extract features (or
  characteristic attributes). The features are fed
  to the computer for analysis.

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Alternate Method

• Alternately, data can be fuzzified and
  similarities can be found through this process.
  A neural network is then trained from the
  different data sets to determine a good data
  signature for which to judge all incoming
  streams of data.

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Decision Making

• Distilled signal information is handed to a
  computer for analysis. The computer can quickly
  recognize changing trends leading to a failure
  and alert an operator before the failure actually
  occurs.

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Conclusions

• Due to the huge number of sensors on many
  Systems, our approach for fault diagnostics and
  prognostics must be capable of intelligent data
  reduction (PCA) in such a way that no important
  data is lost and all the crucial data be used for
  smart prognosis with minimum false alarms.
• In its final configuration, it is expected that a
  library of these strong methods which is under
  development at benefit the the System program,
  ATL, Intel System, Bio-medical cases, etc.

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• THANK YOU!