Fault detection for turbine engine disk using adaptive Gaussian mixture model

Rotating turbine engine disk is one of the most critical components of the aircraft engine. Therefore, fault detection for rotating turbine engine disk is particularly significant. An adaptive Gaussian mixture model is put forward to deal with the dynamic working process of rotating turbine engine disk. Different from the standard expectation–maximization algorithm, the criterion based on minimum within-cluster distance and maximum between-cluster distance is used to update the weight of each Gaussian component. The incoming samples obtained by a forgetting mechanism are adopted to modify the fault detection model by exponentially weighted algorithm, which improves the ability of fault detection model to accommodate the actual situation of turbine engine disk. The comparison results between the multiple principal component analysis, the classical Gaussian mixture model, and the proposed algorithm show that the proposed approach has much superiority on the fault detection of turbine engine disk.

[1]  Haitao Zhao,et al.  Multimode Process Monitoring Based on Sparse Principal Component Selection and Bayesian Inference-Based Probability , 2015 .

[2]  Jason Poon,et al.  Model-Based Fault Detection and Identification for Switching Power Converters , 2017, IEEE Transactions on Power Electronics.

[3]  Shenfang Yuan,et al.  On-line updating Gaussian mixture model for aircraft wing spar damage evaluation under time-varying boundary condition , 2014 .

[4]  Mark R. Woike,et al.  Rotor health monitoring combining spin tests and data-driven anomaly detection methods , 2012 .

[5]  Jie Yu,et al.  A new fault diagnosis method of multimode processes using Bayesian inference based Gaussian mixture contribution decomposition , 2013, Eng. Appl. Artif. Intell..

[6]  Fuli Wang,et al.  Novel Monitoring Strategy Combining the Advantages of the Multiple Modeling Strategy and Gaussian Mixture Model for Multimode Processes , 2015 .

[7]  H. Shi,et al.  Dynamic Multimode Process Modeling and Monitoring Using Adaptive Gaussian Mixture Models , 2012 .

[8]  Biao Huang,et al.  Expectation–Maximization Approach to Fault Diagnosis With Missing Data , 2015, IEEE Transactions on Industrial Electronics.

[9]  Jie Ma,et al.  Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique , 2015 .

[10]  Mark R. Woike,et al.  Propulsion health monitoring of a turbine engine disk using spin test data , 2010, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[11]  Jiusheng Chen,et al.  Fault detection for turbine engine disk based on an adaptive kernel principal component analysis algorithm , 2016, J. Syst. Control. Eng..

[12]  Sheng Chen,et al.  Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Khashayar Khorasani,et al.  Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines , 2015, IEEE Transactions on Control Systems Technology.

[14]  Chee Khiang Pang,et al.  Gaussian Mixture Model Using Semisupervised Learning for Probabilistic Fault Diagnosis Under New Data Categories , 2017, IEEE Transactions on Instrumentation and Measurement.

[15]  Biao Huang,et al.  GMM and optimal principal components-based Bayesian method for multimode fault diagnosis , 2016, Comput. Chem. Eng..

[16]  Shenfang Yuan,et al.  An Improved Gaussian Mixture Model for Damage Propagation Monitoring of an Aircraft Wing Spar under Changing Structural Boundary Conditions , 2016, Sensors.

[17]  Roy E. Welsch,et al.  Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring , 2016 .

[18]  Yuanqing Xia,et al.  A new sampling method in particle filter based on Pearson correlation coefficient , 2016, Neurocomputing.

[19]  Dražen Slišković,et al.  Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models , 2013, Comput. Chem. Eng..

[20]  Weihua Gui,et al.  An efficient multi-PCA based on-line monitoring scheme for multi-stages imperial smelting process , 2013 .

[21]  Chris Bingham,et al.  Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models , 2017 .

[22]  Douglas A. Reynolds.  Gaussian Mixture Models , 2009, Encyclopedia of Biometrics.