Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?

Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. The lack of IS fault examples in the training data can pose severe risks to Fault Detection and Diagnosis (FDD) methods that are built upon Machine Learning (ML) techniques, because these faults can be easily mistaken as normal operating conditions. Ensemble models are widely applied in ML and are considered promising methods for detecting out-of-distribution (OOD) data. We identify common pitfalls in these models through extensive experiments with several popular ensemble models on two real-world datasets. Then, we discuss how to design more effective ensemble models for detecting and diagnosing IS faults.

[1]  Bo Zong,et al.  Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.

[2]  Dirk Van,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[3]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[4]  Guoqiang Hu,et al.  Fault detection and diagnosis for building cooling system with a tree-structured learning method , 2016 .

[5]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[6]  Alberto L. Sangiovanni-Vincentelli,et al.  Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).

[7]  Kilian Q. Weinberger,et al.  On Calibration of Modern Neural Networks , 2017, ICML.

[8]  Guoqiang Hu,et al.  A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis , 2016 .

[9]  Feifei Li,et al.  DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning , 2017, CCS.

[10]  Mark A. Richards,et al.  Fundamentals of Radar Signal Processing , 2005 .

[11]  Alberto L. Sangiovanni-Vincentelli,et al.  An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing , 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA).

[12]  Rich Caruana,et al.  Predicting good probabilities with supervised learning , 2005, ICML.

[13]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[14]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[15]  Jonathan Krause,et al.  Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy , 2017, Ophthalmology.

[16]  Ling Huang,et al.  In-Network PCA and Anomaly Detection , 2006, NIPS.

[17]  Yarin Gal,et al.  Uncertainty in Deep Learning , 2016 .

[18]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[19]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[20]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[21]  Charles Blundell,et al.  Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.

[22]  Dawn Xiaodong Song,et al.  Lifelong Anomaly Detection Through Unlearning , 2019, CCS.

[23]  Takehisa Yairi,et al.  Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction , 2014, MLSDA'14.

[24]  Siegfried Wahl,et al.  Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.

[25]  A. Azzouz.  2011 , 2020, City.

[26]  Songhang Chen,et al.  An Improved Mixture of Probabilistic PCA for Nonlinear Data-Driven Process Monitoring , 2019, IEEE Transactions on Cybernetics.

[27]  Alberto L. Sangiovanni-Vincentelli,et al.  A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).