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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

### OUTLINE

MoJamshidi, 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 [email protected]

Fairbanks, Alaska, USA May 24 2005

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

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 Webster’s New World Dictionary.

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.

- Prognosticsuses 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.

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.
- 1970’s 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.
- 1980’s 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.

A Brief History (Contd.)

- 1990’s 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.)

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.

Literature Survey …

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

Approaches to Diagnostics and Prognostics

- Data Driven Methods
- Analytical Methods
- Knowledge based Methods

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

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/readiness rates for the system.

Data Driven Methods

- The huge amount of data has to be reducedintelligently 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).

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).

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.

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.

PCA …

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

Classical Statistics based PCA steps…

- Get Data
- Subtract the mean
- Calculate the covariance matrix
- Calculate eigenvalues and eigenvectors of covariance matrix
- Choose feature vector (data compression begins from here)
- Derive the new data set (reduced)

Principal Component Analysis (PCA)

- Assuming a data set of containingn observations andmvariables (i.e., a n x mmatrix), 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 residualsof dimension (n x m).

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 .

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 .

Non-Linear PCA (NLPCA)

- In Kramer’s 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 .

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.

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

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 min. Input vector xn is compared with every mi and the best match mb is selected.

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.

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].

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.

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.

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.

Prototype Hardware Implementations

- A Chiller at Texas A&M 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)

Whole data with 1000 samples

Normalized training data with 2% noise (sorted)

Normalized test data with 2% noise (sorted)

Test data with 2% noise, sensor 3 has drift error

Drift error and sensor 3 data

Test data with 2% noise, sensor 3 has shift error

E-AANN output, the input data had 2% noise and shift error

Shift error and sensor 3 data

The difference between E-AANN input and output, the input data had 2% noise and shift error

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

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.

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.

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.

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

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.

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.

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.

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.

USING ALL 325 EIGENVECTORS

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

Laser Pointing System at UNM

Lab View

Controller Algorithm

ADC

DAC

DAC

X/Y motors

Mirror

Filter

Detector Quadrant

L

A

S

E

R

HARDWAREPrognosticSystem

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

Architecture

FAB

Template

Data

iUSC

SECS Link

Data/Templates

Data Repository

(Unix)

Office NTWorkstation

SECS/GEM

(Domain\'s PDE)

Process Tool

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.

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.

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.

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.

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.

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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