Fault Diagnosis in Industries: How to Improve the Health Assessment of Rotating Machinery
Author:
Calabrese FrancescaORCID, Regattieri AlbertoORCID, Bortolini MarcoORCID, Galizia Francesco GabrieleORCID
Publisher
Springer Singapore
Reference32 articles.
1. Liu, L., Zhi, Z., Zhang, H., Guo, Q., Peng, Y., Liu, D.: Related entropy theories application in condition monitoring of rotating machineries. Entropy 21, 1061 (2019) 2. Wei, Y., Li, Y., Xu, M., Huang, W.: A review of early fault diagnosis approaches and their applications in rotating machinery. Entropy 21, 409 (2019) 3. Sarih, H., Tchangani, A.P., Medjaher, K., Pere, E.: Data preparation and preprocessing for broadcast systems monitoring in PHM framework. In: 2019 6th Int. Conf. Control. Decis. Inf. Technol. CoDIT 2019, no. Cm, pp. 1444–1449 (2019) 4. Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20(7), 1483–1510 (2006) 5. Bin, G.F., Gao, J.J., Li, X.J., Dhillon, B.S.: Early fault diagnosis of rotating machinery based on wavelet packets — empirical mode decomposition feature extraction and neural network. Mech. Syst. Signal Process. 27(1), 696–711 (2012)
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|