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Multi-Sensor Health Diagnosis Using Deep Belief Network Based State

ClassificationPrasanna Tamilselvan and Pingfeng Wang

Department of Industrial and Manufacturing Engineering, College of Engineering, Wichita State University

Motivation and Objectives Deep Belief Network Based Health Diagnostic Procedure

Step 1: Diagnostic definition and classification

Step 2: Data collection from different sensors

Step 3: Preprocessing of the data

Step 4: Development of DBN classifier model

Step 5: DBN training for different possible health states

Step 6: Misclassification determination of classifier

Step 7: DBN classification for Multi-sensor health diagnostics

Case Study I – Iris Flower Classification

Case Study II – Aircraft Wing Structure Health

Diagnostics

Conclusion

References

Existing Methods and its Challenges

Multi-State Classification

DBN Architecture

DBN Classification

DBN Validation

• Some of the existing methods to classify different health

states:

Artificial Neural Networks (ANN)

Self Organizing Maps (SOM)

Support Vector Machine (SVM)

Mahalanobis Distance (MD)

Genetic Algorithms (GAs)

• Most of the existing methods except SOM are supervised

learning

• Supervised learning is not suitable for detecting unknown

failures

• SOM is not suitable for complicated data structures

• DBN is an unsupervised learning process with deep

network structure and handles complicated data

structures

• DBN has proved its applicability in image recognition and

audio classification

RBM Methodology

DBN Diagnostic Procedure

DBN Characteristics and Benefits

Iris Setosa

Iris Versicolor Iris Virginica

RBM Learning Function

• DBN architecture looks similar to the stacked

structure of multiple Restricted Boltzmann

Machines (RBMs)

• DBN structure consists of one data input layer and

multiple hidden layers

• DBN learning function is based on RBM (sigmoid

function)

• DBN uses contrastive divergence algorithm as

fine tuning algorithm

• DBN learns complex data structure deeply

• DBN classifies unlabelled data and detects the

uncommon failure states

• DBN have fast inference, fast unsupervised

learning, and the ability to encode richer and higher

order network structures

Motivation• Kansas is the one of the headquarters of major aircraft

manufacturing industries

• Due to large human life risks involved in flight journey,

safety and operational reliability of aircraft is more critical

• Continuous health monitoring and failure diagnosis of aircraft

is more essential for Kansas aircraft industries, to

manufacture most reliable and failure preventive aircrafts to

the world

Objectives

• Health state diagnostics of aircraft using multi-sensors and a

novel artificial intelligence technique, Deep Belief Network

(DBN)

• Comparison of different existing methods with DBN for

multi-state classification based on sensor data

• Based on the

operational performance

of components, health

state can be classified

into three main

conditions:

Safe Condition

Degrading

Condition

Failure Condition

Multi-Sensor State

Classification:

Placement of multiple

sensors at different

critical locations enables

continuous health

monitoring of aircraft

components

SOM Results

MethodTraining

Data

Testing

Data

Training

Classification

Rate (%)

Testing

Classification

Rate (%)

Overall

Classification

Rate (%)

ANN 75 75 100 94.67 97.33

SOM 75 75 97.33 97.33 97.33

SVM 150 0 97.33 0 97.33

DBN 75 75 100 96 98

Sensors

Comparison Results

• Nair, V., and Hinton, G.E., (2009) “Implicit mixtures of restricted boltzmann machines,”

Advances in Neural Information Processing Systems, Vol. 21, pp. 215-231.

• Huang, R., Xi, L., Li, X., Liu, C. R., Qiu, H., and Lee, J., (2007), “Residual life

predictions for ball bearings based on self-organizing map and back propagation neural

network methods,” Mechanical Systems and Signal Processing, Vol. 21, pp. 193-207.

• Hinton, G. E., Osindero, S., and Teh, Y., (2006) “A fast learning algorithm for deep belief

nets,” Neural Computation, Vol. 18, pp. 1527-1554.

• Hsu, C., and Lin, C., (2002), “A comparison of methods for multiclass support vector

machines,” IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415-425.

Safe Region

Degrading Region

Failure Region

• Aircraft wing is designed

with five sensors

• Sensor data for variable

load is simulated for four

different health conditions

No Fault

Fault A

Fault B

Fault C

Aircraft Wing Structure

• DBN performs better than the existing methods based

on classification rate

• DBN classifies aircraft wing health state conditions

into four different classes at 97% classification rate

• Trained DBN classifier model can classify unknown

health states and sensor data

Simulated Aircraft Wing Design

Training Testing

Data 4000 4000

Classification Rate (%) 97.32 96.12

Overall Classification

Rate (%)96.72

DBN Classification Results

Future Work

Sensors

Fault A

Fault B

Fault C

• Apply DBN based health diagnostics for complex

structural systems

• Develop DBN based Prognostics and Health

Management (PHM) methodology for intelligent

structural degradation modeling and failure forecasting