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首页> 外文期刊>Tribology letters >A comparison of texture feature extraction methods for machine condition monitoring and failure analysis
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A comparison of texture feature extraction methods for machine condition monitoring and failure analysis

机译：用于机器状态监测和故障分析的纹理特征提取方法的比较

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摘要

The diagnosis of worn and damaged surfaces is an important issue in machine failure analysis and condition monitoring, Of many approaches used, image classification based on feature parameters has often proven to be particularly useful, Accurate classification can, however, be limited by the fact that feature parameters vary with scale and orientation Hence, it is essential to determine which feature parameters are both scale and rotation invariant. This paper presents a performance evaluation of feature extraction methods currently used in pattern recognition. A comparison of six methods is conducted, in order to find the method that provides the most consistent results over a large range of image sizes and rotations. The methods analysed are: co-occurrence matrix, discrete wavelet transform, combination of wavelet and co-occurrence features, Gabor filter, circular Gaussian Markov random field and local binary patterns, For the comparison, four datasets of images with different scales and rotations are used, i.e.. Brodatz textures, artificially generated isotropic fractal images and Talysurf images of sandblasted and abraded steel surfaces. The performance of each method is evaluated on the datasets using k-nearest neighbours and linear based normal densities classifiers. The results showed that the combined feature extraction method produced the most robust and accurate results for each of the datasets, and appears to be suitable for the classification of tribological surfaces.
机译：在机器故障分析和状态监控中，磨损和损坏表面的诊断是一个重要问题。在许多使用的方法中，基于特征参数的图像分类通常被证明是特别有用的，但是，准确分类可能会受到以下事实的限制：特征参数随比例和方向而变化因此，必须确定哪些特征参数既是比例也是旋转不变。本文介绍了目前在模式识别中使用的特征提取方法的性能评估。为了找到在较大的图像尺寸和旋转范围内提供最一致结果的方法，对六种方法进行了比较。分析的方法是：共现矩阵，离散小波变换，小波和共现特征的组合，Gabor滤波器，圆形高斯马尔可夫随机场和局部二进制模式，为了进行比较，使用了具有不同比例和旋转度的四个图像数据集使用的是Brodatz纹理，人工生成的喷砂和磨钢表面的各向同性分形图像和Talysurf图像。使用k最近邻和基于线性的法线密度分类器在数据集上评估每种方法的性能。结果表明，组合特征提取方法为每个数据集提供了最可靠，最准确的结果，并且似乎适合于摩擦表面的分类。

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