Abstract
The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid typeJand order parameterqin algorithm, the value range ofJand the cut-off condition ofqwere optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.
Highlights
Modern industry is gradually developing in the direction of large-scale, continuous, high speed, and artificial intelligence, with the main advantage of improving productivity, reducing the rejection rate, and ensuring quality of products
Compared with the obvious differences in dimensions and parameters for different bearing failures, the method can be used for fault diagnosis
Generalized dimension spectrum has been obtained after the signals are calculated by doing time-frequency domain generalized dimension method; slight difference in different singles can be distinguished based on different values of generalized dimension spectrum
Summary
Modern industry is gradually developing in the direction of large-scale, continuous, high speed, and artificial intelligence, with the main advantage of improving productivity, reducing the rejection rate, and ensuring quality of products. The fault diagnosis analysis of rolling bearing, especially the correct detection of the early failure has practical value in extending service life and reducing cost. There is a wide range of needs in the exploration and application of bearing fault diagnostic It has practical significance, broad market prospect, and economic value in the social development [2, 3]. The faults of complex machinery system can be diagnosed, which can improve the fault identification and analysis ability. It is a practical and promising signal analyzing method for machinery devices. Compared with the obvious differences in dimensions and parameters for different bearing failures, the method can be used for fault diagnosis
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