Abstract
In this paper, relying on the Volterra series nonlinear system model and the high-order kernel Hilbert’s reconstructed kernel fast solved algorithm, a fault feature frequency domain identification method based on Volterra high-order kernel generalized frequency response graph analysis is proposed. Firstly, the method uses the system input and output vibration signals to determine the Volterra model. Then, the Volterra high-order kernel function is solved quickly by reproducing kernel Hilbert space method, and the generalized frequency response function is used to identify the model. Finally, multidimensional high-order spectral pattern analysis is used to separate and extract the fault and degree characteristic information implied by frequency and phase coupling in the third-order kernel function. Following the theoretical approach, in the experimental part, this paper uses the planetary gearbox fault loading test rig to complete the data collection and establishes the Volterra experimental model through the measured data. The generalized frequency responses of each order kernel function are compared and analyzed and the capability of distinguishing and the adaptability of different order kernel functions for the degree of crack failure are discussed. The effects of changing the memory length of the Volterra model and the order of the kernel function on the recognition result are verified. The final experimental results show that the use of reproducing kernel Hilbert space can effectively avoid the dimension disaster problem that occurs in the high-order kernel solution process. Moreover, the third-order kernel can describe more intuitively the nonlinear system model under multifactor coupling than the second-order kernel. Finally, Volterra series model the third-order kernel’s generalized frequency response can effectively distinguish between nondefective and faulty gears, and its resolution is enough to distinguish the degree of failure of gear cracks.
Highlights
Among the advantages of planetary gear transmission system, high transmission ratio, strong load capacity, small size, and light weight contributed to its success in many industrial applications such as wind power equipment, engineering machinery, and aerospace [1, 2]
Through a new analysis of the Volterra series theory, this paper found an improved method for improving the identification resolution of the Volterra model, that is, increase the memory length m of Volterra model and the order n of the kernel function
This paper addresses a series of nonlinear problems related to the diagnosis of gear cracks in planetary gearboxes
Summary
Among the advantages of planetary gear transmission system, high transmission ratio, strong load capacity, small size, and light weight contributed to its success in many industrial applications such as wind power equipment, engineering machinery, and aerospace [1, 2]. Jiang et al propose a signal denoising technology based on adaptive Morlet wavelet and singular value decomposition (SVD) and applied it to extract pulse characteristics of second stage reducer planetary gearbox for wind turbine [19]. The above studies have achieved specific results in the identification of faults, they still focus on the failure of a single component and do not fully meet the actual engineering needs, that asks for the extraction and prediction of the vibration signal characteristics of the cracks and the degree of evolution of planetary gearbox gears in various mechanical equipment under actual conditions. In response to the above-unsolved problems and considering our previous research results, this paper proposes a novel identification method for crack fault evolutionary characteristics of planetary gearboxes based on the Volterra highorder kernel function theory [24, 25]. The correctness, universality, and superiority of the proposed method are verified proving that it further enriches and promotes fundamental research in the field of mechanical and electrical equipment fault diagnosis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
