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A Fault Diagnosis Model Based on Kernel Auto-encoder and Improved Chaos Firefly Optimization Algorithm.

, , , , , and . I2MTC, page 1-6. IEEE, (2019)

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A deep neural network based on kernel function and auto-encoder for bearing fault diagnosis., , , , and . I2MTC, page 1-6. IEEE, (2018)Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel-Ziv Complexity under Varying Conditions., , , and . Sensors, 23 (8): 4044 (2023)Nonlinear Characteristic of Clamp Loosing in Aero-Engine Pipeline System., , , , , and . IEEE Access, (2021)A Fault Diagnosis Model Based on Kernel Auto-encoder and Improved Chaos Firefly Optimization Algorithm., , , , , and . I2MTC, page 1-6. IEEE, (2019)Crack fault identification in rotor shaft with artificial neural network., and . ICNC, page 1629-1634. IEEE, (2010)Identification of Crack Location and Depth in Rotating Machinery Based on Artificial Neural Network., , , and . ISNN (2), volume 3973 of Lecture Notes in Computer Science, page 982-990. Springer, (2006)Design and development of an intelligent wearing system for adolescent spinal orthotics., , , and . Medical Biol. Eng. Comput., 62 (9): 2653-2667 (September 2024)Reliability assessment of rolling bearing based on principal component analysis and Weibull proportional hazard model., , , , , and . I2MTC, page 1-6. IEEE, (2017)Fault Features Uncertainty Quantification With Parameters Uncertainties of Data-Driven Models and Its Application in Rotor Systems Condition Assessment., , , , and . IEEE Trans. Instrum. Meas., (2024)The LPST-Net: A new deep interval health monitoring and prediction framework for bearing-rotor systems under complex operating conditions., , , , and . Adv. Eng. Informatics, (2024)