This paper describes an original method of global machine condition assessment for infrared condition monitoring and diagnostics systems. This method integrates two approaches: the first is processing and analysis of infrared images in the frequency domain by the use of 2D Fourier transform and a set of F-image features, the second uses fusion of classification results obtained independently for F-image features. To find the best condition assessment solution, the two different types of classifiers, k-nearest neighbours and support vector machine, as well as data fusion method based on Dezert–Smarandache theory have been investigated. This method has been verified using infrared images recorded during experiments performed on the laboratory model of rotating machinery. The results obtained during the research confirm that the method could be successfully used for the identification of operational conditions that are difficult to be recognised.

本文介绍了一种用于红外状况监测和诊断系统的全局机器状况评估的原始方法。该方法集成了两种方法：第一种是通过使用2D傅里叶变换和一组F图像特征来处理和分析频域中的红外图像，第二种方法是使用针对F图像特征独立获得的分类结果的融合。为了找到最佳状态评估解决方案，这两种不同类型的分类，ķ研究了近邻和支持向量机，以及基于Dezert-Smarandache理论的数据融合方法。该方法已使用在旋转机械的实验室模型上进行的实验期间记录的红外图像进行了验证。在研究过程中获得的结果证实，该方法可以成功地用于识别难以识别的操作条件。