Restricted Boltzmann machine and softmax regression for fault detection and classification

A unique technique is proposed based on restricted Boltzmann machine (RBM) and softmax regression for automated fault detection and classification using the acoustic signal generated from IC (Internal Combustion) engines. This technique uses RBM for unsupervised fault feature extraction from the frequency spectrum of the noisy acoustic signal. These extracted features are then used to reduce the dimensionality of the training and testing data vectors. These reduced dimensionality data vectors are used by softmax regression-based classifier for classification of the engine into faulty and healthy class. The proposed technique does not require any hand-engineered feature extraction, as usually done. This technique performs very well with a small number of training data. The overall performance of this technique for four different fault classes is more than 99% on the industrial IC engine data. In a typical case, with only 38 training data sets and 210 test data sets, the performance is 99.52%.

[1]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[2]  Geoffrey E. Hinton,et al.  Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.

[3]  Yunde Jia,et al.  Vehicle Type Classification Using Unsupervised Convolutional Neural Network , 2014, 2014 22nd International Conference on Pattern Recognition.

[4]  Jinfu Yang,et al.  Natural scene recognition based on Convolutional Neural Networks and Deep Boltzmannn Machines , 2015, 2015 IEEE International Conference on Mechatronics and Automation (ICMA).

[5]  Hao Zhang,et al.  Gender classification in face images based on stacked-autoencoders method , 2014, 2014 7th International Congress on Image and Signal Processing.

[6]  Jian-Da Wu,et al.  An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network , 2009, Expert Syst. Appl..

[7]  D. Böhning.  Multinomial logistic regression algorithm , 1992 .

[8]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[9]  Prem Kumar Kalra,et al.  Audio Signature-Based Condition Monitoring of Internal Combustion Engine Using FFT and Correlation Approach , 2011, IEEE Transactions on Instrumentation and Measurement.

[10]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[11]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[12]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Kuo-Chung Lin,et al.  Wavelet packet feature extraction for vibration monitoring , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[14]  S.V.P. Sankar Nidadavolu,et al.  Condition monitoring of Internal Combustion engines using empirical mode decomposition and Morlet wavelet , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.

[15]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[16]  Grgoire Montavon,et al.  Neural Networks: Tricks of the Trade , 2012, Lecture Notes in Computer Science.

[17]  Miguel Á. Carreira-Perpiñán,et al.  On Contrastive Divergence Learning , 2005, AISTATS.

[18]  Prem Kalra,et al.  Automatic fault diagnosis of internal combustion engine based on spectrogram and artificial neural network , 2010 .

[19]  Praveen Chopra,et al.  Fault detection and classification by unsupervised feature extraction and dimensionality reduction , 2015, Complex & Intelligent Systems.

[20]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.