POSTER TEMPLATE BY: 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 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 VersicolorIris 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 Method Training Data Testing Data Training Classification Rate (%) Testing Classification Rate (%) Overall Classification Rate (%) ANN SOM SVM DBN 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 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 Hinton, G. E., Osindero, S., and Teh, Y., (2006) “A fast learning algorithm for deep belief nets,” Neural Computation, Vol. 18, pp 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 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 TrainingTesting Data4000 Classification Rate (%) Overall Classification Rate (%) 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