Abstract
Effective condition monitoring provides some benefits such as improving safety and reliability. Roller bearing is the key component of rotating machinery, and a novel roller bearing condition monitoring method based on rational Hermite interpolation-local characteristic-scale decomposition (RHLCD) and fusion variable predictive model-based class discriminate method (FVPMCD) is proposed in this paper. RHLCD can adaptively decompose any complex signal into a sum of rational intrinsic scale components (RISCs), whose instantaneous frequency has physical meaning. In addition, targeting the limitation of variable predictive model-based class discriminate method (VPMCD), FVPMCD is presented. First, four kinds of common models are used to recognize a sample. Then, the recognition results of each model are satisfied, and the recognition probability of each state is calculated. Finally, the largest recognition probability of the state is chosen to recognize categories. The analytical results of experimental signals indicate that the proposed condition monitoring approach can identify the states of roller bearing effectively.
Highlights
Roller bearing is the key component of the rotating machinery, whose fault is the common fault of mechanical system
THE ROLLER BEARING CONDITION MONITORING ALGORITHM BASED ON rational Hermite interpolation-local characteristic-scale decomposition (RHLCD) AND fusion variable predictive model-based class discriminate method (FVPMCD) Firstly, the roller bearing vibration signal is decomposed by RHLCD, and several rational intrinsic scale components (RISCs) without end effect are obtained
In view of the shortcomings of previous decomposition methods, a rational Hermite interpolation—local characteristicscale decomposition (RHLCD) method is put forward, which can effectively decompose arbitrary non-linear or nonstationary signals, and some continuity and smoothness of RISCs are obtained without energy leakage
Summary
Roller bearing is the key component of the rotating machinery, whose fault is the common fault of mechanical system. To solve the problem of cubic spline interpolation, Li et al applied the Hermite interpolation to ITD method [17], which has a characteristic of shape preservation, and this method is superior to the analysis of strong impact non-stationary signals. To adjust the shape of the determined curve and optimize the fitting effect, Li et al put forward a rational Hermite interpolation [18]. A novel roller bearing fault on-line condition monitoring method based on RHLCD and FVPMCD is put forward. Compared with cubic spline interpolation, cubic Hermite interpolation is applied to fit the local extrema, and it can guarantee the continuity and smoothness of successive points This method has an excellent characteristic of shape preservation and is superior to the analysis of strong impact nonstationary signals. The shape controlling parameter determining criterion is introduced in the literature [18]
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