Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification

1. 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 Case Study II – Aircraft Wing Structure Health Diagnostics Motivation and Objectives Deep Belief Network Based Health Diagnostic Procedure • 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 DBN Diagnostic Procedure DBN Architecture 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 DBN Classification Simulated Aircraft Wing Design Multi-State Classification DBN Characteristics and Benefits • Based on the operational performance of components, health state can be classified into three main conditions: • Safe Condition • Degrading Condition • Failure Condition Sensors Fault A Fault B Fault C Safe Region Degrading Region Failure Region • 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 DBN Classification Results DBN Validation Multi-Sensor State Classification: Placement of multiple sensors at different critical locations enables continuous health monitoring of aircraft components Sensors Conclusion RBM Learning Function • 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 Case Study I – Iris Flower Classification Existing Methods and its Challenges SOM Results • 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 handlescomplicated data structures • DBN has proved its applicability in image recognition and audio classification Iris Setosa Future Work Comparison Results • 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 Iris Versicolor Iris Virginica • 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.