ABSTRACT
As a kind of power supply equipment, diesel generator set has the characteristics of good mobility, fast start, stable power supply, convenient operation and maintenance. Diesel generator set is very important for power supply applications. The research on automatic fault diagnosis of diesel generator set is of great significance for monitoring the operation status of diesel generator and timely maintenance. Compared with traditional neural networks, deep believe network improves the learning efficiency of multi-layer networks by introducing restricted Boltzmann machine. A deep believe network based fault diagnosis for diesel generator set is developed. The sensor data collected from diesel generator set are processed to form a training dataset, and deep believe network is designed. The experimental results show that the deep believe network based method has the best fault diagnosis performance in recall, precision, accuracy and F1-score than other learning based methods.
- Evangelos M. and Agapios P. 2013. Availability assessment of diesel generator system of a ship: A case study, International Journal of Performability Engineering, 9, 5, 561--567.Google Scholar
- Dykas B. and Harris J. 2017. Acoustic emission characteristics of a single cylinder diesel generator at various loads and with a failing injector, Mechanical Systems and Signal Processing, 93, 397--414.Google ScholarCross Ref
- Liu Y., Qiao M., and Jia S. 2018. Anomaly detection for health assessment and prediction of diesel generator set, Proc. 4th International Conference on Communication and Information Processing (ICCIP), ACM Press, Nov. 2018, 212--216.Google Scholar
- LeCun Y. Bengio Y. and Hinton G. 2015. Deep learning, Nature, 521, 7553, 436--444, May 2015.Google Scholar
- Pouyanfar S. et al. 2018. A survey on deep learning: Algorithms, techniques, and applications, ACM Computing Surveys, 51, 5, 2018.Google ScholarDigital Library
- Ma L. Y. Ma C.K. Liu Y.J., Wang X.G. and Xie W. W. Diagnosis of thyroid diseases using SPECT images based on convolutional neural network, Journal of Medical Imaging and Health Informatics, 8, 8, 1684--1689, 2018.Google Scholar
- Ma L. Y. Ma C.K. Liu Y.J. and Wang X. G. Thyroid diagnosis from SPECT images using convolutional neural network with optimization, Computational Intelligence and Neuroscience, 2019, 1, Art. no. 6212759.Google ScholarCross Ref
- Ma L. Y. Xie W. and Zhang Y. 2019. Blister defect detection based on convolutional neural network for polymer lithium-Ion battery, Applied Sciences, 9 6, Art. no. 1085.Google Scholar
- Khan S. and Yairi T. 2018. A review on the application of deep learning in system health management, Mechanical Systems and Signal Processing, 107, 241--265.Google ScholarCross Ref
- Zhao R., Yan R., Chen Z., Mao K., Wang P. and Gao R. X. 2019. Deep learning and its applications to machine health monitoring, Mechanical Systems and Signal Processing, 115, 213--237.Google ScholarCross Ref
- Hinton G.E., Sejnowski T.J., Ackley D.H. 1984. Boltzmann Machine: Constraint Satisfaction Networks that Learn. Pittlsbrugh, Carnegie-Mellon University.Google Scholar
- Hinton G.E. 2010. A practical guide to training restricted Boltzmann machines. Doctoral Thesis, University of Toronto.Google Scholar
- Hinton G.E., Osindero T.Y. 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18, 7, 1527--1554.Google ScholarDigital Library
- Wang Y., Chang M., Chen H., Wang M.Q. 2014. Application of RBF neural network in intelligent fault diagnosis system. Advances in Intelligent Syetems and Computing. 250. 561--556.Google Scholar
- Hwang D. Youn Y., Sun J., Choi K., Lee J. and Kim Y. 2015. Support vector machine based bearing fault diagnosis for induction motors using vibration signals, Journal of Electrical Engineering and Technology. 10, 4, 1559--1566.Google ScholarCross Ref
- Gao F., Lv J. 2016. Fault diagnosis for engine based on single-stage extreme learning machine. Mathematical Problems in Engineering. Article number: 7939607.Google Scholar
Index Terms
- Fault diagnosis of diesel generator set based on deep believe network
Recommendations
Anomaly detection for health assessment and prediction of diesel generator set
ICCIP '18: Proceedings of the 4th International Conference on Communication and Information ProcessingDiesel generator set is widely used in a variety of fields including industry, agriculture and daily life. In order to obtain the operation status information of generator set in time to facilitate health management and fault prediction, a wireless ...
Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform
Wavelet transform has been widely used for the vibration signal based mechanical equipment fault diagnosis. However, the decomposition results of the discrete wavelet transform do not possess time invariant property, which may result in the loss of ...
Application of wavelet neural network in the fault diagnosis of turbine generator unit
AICI'12: Proceedings of the 4th international conference on Artificial Intelligence and Computational IntelligenceWavelet neural network(WNN) is a type of feedforward network which is designed by using wavelet function as the activation functions in neural networks. Based on the technique of WNN, a diagnostic method is presented for turbine generator unit. The ...
Comments