Training products of experts by minimizing contrastive divergence
Neural Computation
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
A fast learning algorithm for deep belief nets
Neural Computation
On the quantitative analysis of deep belief networks
Proceedings of the 25th international conference on Machine learning
Bearing Faults Diagnosis Based on Teager Energy Operator Demodulation Technique
ICMTMA '09 Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01
Learning deep generative models
Learning deep generative models
Some useful properties of Teager's energy operators
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
Expert Systems with Applications: An International Journal
Fault Diagnosis for Reciprocating Air Compressor Valve Using P-V Indicator Diagram and SVM
ISISE '10 Proceedings of the 2010 Third International Symposium on Information Science and Engineering
Fault detection in reciprocating compressor valves for steady-state load conditions
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
On amplitude and frequency demodulation using energy operators
IEEE Transactions on Signal Processing
Energy separation in signal modulations with application to speechanalysis
IEEE Transactions on Signal Processing
Acoustic Modeling Using Deep Belief Networks
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 12.05 |
This paper presents an approach to implement vibration, pressure, and current signals for fault diagnosis of the valves in reciprocating compressors. Due to the complexity of structure and motion of such compressor, the acquired vibration signal normally involves transient impacts and noise. This causes the useful information to be corrupted and difficulty in accurately diagnosing the faults with traditional methods. To reveal the fault patterns contained in this signal, the Teager-Kaiser energy operation (TKEO) is proposed to estimate the amplitude envelopes. In case of pressure and current, the random noise is removed by using a denoising method based on wavelet transform. Subsequently, statistical measures are extracted from all signals to represent the characteristics of the valve conditions. In order to classify the faults of compressor valves, a new type of learning architecture for deep generative model called deep belief networks (DBNs) is applied. DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines (RBMs) and works through a greedy layer-by-layer learning algorithm. In pattern recognition research areas, DBN has proved to be very effective and provided with high performance for binary values. However, for implementing DBN to fault diagnosis where most of signals are real-valued, RBM with Bernoulli hidden units and Gaussian visible units is considered in this study. The proposed approach is validated with the signals from a two-stage reciprocating air compressor under different valve conditions. To confirm the superiority of DBN in fault classification, its performance is compared with that of relevant vector machine and back propagation neuron networks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.