A general design technique for fault diagnostic systems. He, J., Zhou, Z., Zhao, Z., & Chen, S. In International Joint Conference on Neural Networks, 2001. Proceedings. IJCNN '01, volume 2, pages 1307--1311 vol.2, 2001.
doi  abstract   bibtex   
We put forward a design method for fault diagnostic systems (FDSs) by proposing a fault model and using the incremental hybrid learning algorithm which tightly combines symbolic learning and neural networks. It is capable of overcoming several shortcomings in existing diagnostic systems, such as the lack of universality, the unbalance in the use of fault prior knowledge and the dynamic data and the dilemma of stability and plasticity. Experiment showed the FDS implemented by this kind of method had a good diagnostic ability
@inproceedings{ he_general_2001,
  title = {A general design technique for fault diagnostic systems},
  volume = {2},
  doi = {10.1109/IJCNN.2001.939550},
  abstract = {We put forward a design method for fault diagnostic systems (FDSs) by proposing a fault model and using the incremental hybrid learning algorithm which tightly combines symbolic learning and neural networks. It is capable of overcoming several shortcomings in existing diagnostic systems, such as the lack of universality, the unbalance in the use of fault prior knowledge and the dynamic data and the dilemma of stability and plasticity. Experiment showed the FDS implemented by this kind of method had a good diagnostic ability},
  booktitle = {International {Joint} {Conference} on {Neural} {Networks}, 2001. {Proceedings}. {IJCNN} '01},
  author = {He, Jia-Zhou and Zhou, Zhi-Hua and Zhao, Zhi-Hong and Chen, Shi-Fu},
  year = {2001},
  keywords = {Artificial intelligence, Design methodology, Fault diagnosis, Fault model, Fault trees, Helium, Laboratories, Neural networks, Power system reliability, Stability, System identification, _done, _meta, _model_of_activations, _model_of_faults, fault diagnostic systems, general design technique, incremental hybrid learning algorithm, learning (artificial intelligence), neural nets, symbolic learning},
  pages = {1307--1311 vol.2}
}
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