Abstract
The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures.
Highlights
Nowadays, the rolling bearing has been widely used in the applications of modern industrial society, and its working conditions are of vital importance
Rolling bearing faults appear with a great incidence, due to its complexity and poor working conditions, and bearing signals are usually drowned by noises in practice, which make fault diagnosis difficult [1,2]
Combined with fast NL-Means filtering and envelope spectrum analysis, the proposed approach in this paper can be expressed as follows, and the scheme diagram is shown in Figure 8: (1)
Summary
The rolling bearing has been widely used in the applications of modern industrial society, and its working conditions are of vital importance. Many methods have been proposed for signal denoising, such as wavelet threshold [3], blind source separation [4] and singular value decomposition [5] These methods make great contributions to fault diagnosis. In traditional image denoising technologies, the methods are only for local areas and would cause large deviations for local statistical texture areas of rich information These methods effectively work on removing noises in homogeneous areas, but cannot retain complete image structure information, while always making the edge details fuzzy. We propose a patch-based method named NL-Means for rolling bearing fault diagnosis, while applying envelop spectrum analysis [17] as post-processing.
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