Abstract
Collected mechanical signals usually contain a number of noises, resulting in erroneous judgments of mechanical condition diagnosis. The mechanical signals, which are nonlinear or chaotic time series, have a high computational complexity and intrinsic broadband characteristic. This paper proposes a method of gear and bearing fault classification, based on the local subspace projection noise reduction and PE. A novel nonlinear projection noise reduction method, smooth orthogonal decomposition (SOD), is proposed to denoise the vibration signals of various operation conditions. SOD can decompose the reconstructed multiple strands to identify smooth local subspace. In the process of projection from a high dimension to a low dimension, a new weight matrix is put forward to achieve a better denoising effect. Afterwards, permutation entropy (PE) is applied in the detection of time sequence randomness and dynamic mutation behavior, which can effectively detect and amplify the variation of vibration signals. Hence PE can characterize the working conditions of gear and bearing under different conditions. The experimental results illustrate the effectiveness and superiority of the proposed approach. The theoretical derivations, numerical simulations and experimental studies, all confirm that the proposed approach based on the smooth local subspace projection method and PE, is promising in the field of the fault classification of rotary machinery.
Highlights
Gear and bearing are very important parts in mechanical systems, because their working conditions can directly affect safety and stable operations
There is no extra noise in the denoised signal in time, three evaluation indicators of signal to Visual noise identification ratio (SNR),1Cm and mean square error (MSE) can be utilized to measure the effectiveness of the smooth local subspace projection denoising method quantitatively
The research work in this paper elaborates on the theoretical effectiveness of the proposed method based on the smooth local subspace projection and permutation entropy (PE)
Summary
Gear and bearing are very important parts in mechanical systems, because their working conditions can directly affect safety and stable operations. Christoph Bandt et al [35] proposed the concept of the average entropy parameter in the application of measuring the complexity of one dimensional time series, namely permutation entropy (PE) It was an algorithm study about describing irregular and nonlinear systems [36], which cannot, or are hard to be, quantitatively described, in a relatively simple way. This paper proposes a novel approach to conduct fault classification of gear and bearing signals, based on the smooth local subspace projection denoising and PE. The organization of this paper is as follows: Section 2 introduces the basic principles and characteristics of a smooth local decomposition and an algorithm, as well as the concept of PE It describes the theory of the fault classification method based on the smooth local subspace projection and PE.
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