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Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification
Prasanna Tamilselvan and Pingfeng WangDepartment 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 classificationStep 2: Data collection from different sensorsStep 3: Preprocessing of the dataStep 4: Development of DBN classifier modelStep 5: DBN training for different possible health statesStep 6: Misclassification determination of classifierStep 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
P (h j=1|v )=sigm ¿
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
DataTesting
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 RegionDegrading RegionFailure 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
SensorsFault AFault BFault 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