Multi-Sensor Health Diagnosis Using Deep Belief Network Based State
Classification
Prasanna Tamilselvan and Pingfeng Wang
Department of Industrial and Manufacturing Engineering, College of Engineering, Wichita State University
Motivation and Objectives
Case Study II – Aircraft Wing Structure Health
Diagnostics
Deep Belief Network Based Health Diagnostic Procedure
Motivation
DBN Diagnostic Procedure
• 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
Step 1:
Step 2:
Step 3:
Step 4:
Step 5:
Step 6:
Step 7:
DBN Architecture
Diagnostic definition and classification
Data collection from different sensors
Preprocessing of the data
Development of DBN classifier model
DBN training for different possible health states
Misclassification determination of classifier
DBN classification for Multi-sensor health diagnostics
DBN Classification
Aircraft Wing Structure
• 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
RBM Methodology
Simulated Aircraft Wing Design
Multi-State Classification
Safe Region
Degrading Region
Failure Region
Sensors
• Based on the
operational performance
of components, health
state can be classified
into three main
conditions:
 Safe Condition
 Degrading
Condition
 Failure Condition
DBN Characteristics and Benefits
DBN Validation
Multi-Sensor State
Classification:
Placement of multiple
sensors at different
critical locations enables
continuous health
monitoring of aircraft
components
RBM Learning Function
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Case Study I – Iris Flower Classification
Existing Methods and its Challenges
• 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
• 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
Iris Setosa
SOM Results
Comparison Results
Training
Testing
Overall
Training Testing
Method
Classification Classification Classification
Data
Data
Rate (%)
Rate (%)
Rate (%)
Iris Versicolor
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
Iris Virginica
Sensors
Fault A
Fault B
Fault C
DBN Classification Results
Training
Testing
Data
4000
4000
Classification Rate (%)
97.32
96.12
Overall Classification
Rate (%)
96.72
Conclusion
• 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
Future Work
• 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
References
• 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.