Fast and Accurate Classification of Time Series Data Using Extended ELM: Application in Fault Diagnosis of Air Handling Units

The extreme learning machine (ELM) is famous for its single hidden-layer feed-forward neural network which results in much faster learning speed comparing with traditional machine learning techniques. Moreover, extensions of ELM achieve stable classification performances for imbalanced data. In this paper, we introduce a hybrid method combining the extended Kalman filter (EKF) with cost-sensitive dissimilar ELM (CS-D-ELM). The raw data are preprocessed by EKF to produce inputs for the CS-D-ELM classifier. Experimental results show that the proposed method is more suitable for real-time fault diagnosis of air handling units than traditional approaches.

[1]  Leslie K. Norford,et al.  Robust model-based fault diagnosis for air handling units , 2015 .

[2]  Bing Dong,et al.  A probabilistic approach to diagnose faults of air handling units in buildings , 2016 .

[3]  Shengwei Wang,et al.  Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method , 2005 .

[4]  Yonghua Zhu,et al.  Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal , 2012 .

[5]  Guang-Bin Huang,et al.  Convex Incremental Extreme Learning Machine , 2007 .

[6]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[7]  Zhimin Du,et al.  Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network , 2009 .

[8]  Kai Xiong,et al.  Robust Extended Kalman Filtering for Nonlinear Systems With Stochastic Uncertainties , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Kai Zhang,et al.  Extreme learning machine and adaptive sparse representation for image classification , 2016, Neural Networks.

[10]  Ke Yan,et al.  Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM , 2017, Neurocomputing.

[11]  Peng Liu,et al.  Two-stage extreme learning machine for high-dimensional data , 2016, Int. J. Mach. Learn. Cybern..

[12]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Wutao Chen,et al.  A Dataset Splitting Based Neural Network Ensemble Method for Tumor Classification , 2012 .

[15]  S. Haykin.  Kalman Filtering and Neural Networks , 2001 .

[16]  Huijuan Lu,et al.  Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification , 2016, Comput. Intell. Neurosci..

[17]  Wen Shen,et al.  ARX model based fault detection and diagnosis for chillers using support vector machines , 2014 .

[18]  Di Guo,et al.  Extreme Kernel Sparse Learning for Tactile Object Recognition , 2017, IEEE Transactions on Cybernetics.

[19]  Nan Liu,et al.  Voting based extreme learning machine , 2012, Inf. Sci..

[20]  N. Tudoroiu,et al.  Fault Detection and Diagnosis (FDD) in Heating Ventilation Air Conditioning Systems (HVAC) Using an Interactive Multiple Model Augmented Unscented Kalman Filter (IMMAUKF) , 2008, 2008 Conference on Human System Interactions.

[21]  Zhimin Du,et al.  Wavelet Neural Network-Based Fault Diagnosis in Air-Handling Units , 2008 .

[22]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[23]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[24]  Zhigang Zeng,et al.  Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis , 2013, Neural Computing and Applications.

[25]  Yimin Yang,et al.  Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning , 2016, IEEE Transactions on Cybernetics.

[26]  David Zhang,et al.  Evolutionary Cost-Sensitive Extreme Learning Machine , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Afshin Afshari,et al.  Kalman filter-based Fault Detection and Diagnosis for Air Handling Units , 2014 .

[28]  Xizhao Wang,et al.  Dynamic ensemble extreme learning machine based on sample entropy , 2012, Soft Comput..

[29]  Xin Yao,et al.  Multiclass Imbalance Problems: Analysis and Potential Solutions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  A. Omer.  Energy, environment and sustainable development , 2008 .

[31]  Zhu-Hong You,et al.  Predicting dynamic deformation of retaining structure by LSSVR-based time series method , 2014, Neurocomputing.

[32]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[33]  Annalisa Riccardi,et al.  Cost-Sensitive AdaBoost Algorithm for Ordinal Regression Based on Extreme Learning Machine , 2014, IEEE Transactions on Cybernetics.

[34]  Yi Lu,et al.  Dissimilarity based ensemble of extreme learning machine for gene expression data classification , 2014, Neurocomputing.

[35]  Haopeng Zhang,et al.  Optimal Balanced Coordinated Network Resource Allocation Using Swarm Optimization , 2015, IEEE Trans. Syst. Man Cybern. Syst..

[36]  Masami Suzuki,et al.  Typical faults of air conditioning systems and fault detection by ARX model and extended Kalman filter , 1996 .

[37]  Nan Liu,et al.  Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift , 2015, Neurocomputing.

[38]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[39]  Taghi M. Khoshgoftaar,et al.  Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[40]  Yaonan Wang,et al.  Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[41]  Min Xie,et al.  A Real-Time Fault Diagnosis Methodology of Complex Systems Using Object-Oriented Bayesian Networks , 2016, Bayesian Networks in Fault Diagnosis.

[42]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).