期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 14, 期 12, 页码 5553-5562出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2816925
关键词
Condition monitoring; fault diagnosis; industry applications; infrared imaging; rotating machines; self-organizing feature maps
The procedures for condition monitoring of electromechanical systems are undergoing a reformulation, mainly, due to the current thermographic affordability of infrared cameras to be incorporated in industrial applications. However, high-performing multifault data-driven methodologies must be investigated in order to infer reliable condition information from the thermal distribution of not only electrical motors but also of shafts and couplings. To address this issue, a novel thermography-based methodology is proposed. First, the infrared capture is processed to obtain a thermographic residual image of the kinematic chain. Second, the thermal distribution of the image's regions of interest is characterized by means of statistical features. Finally, a distributed self-organizing map structure is used to model the nominal thermal distribution to subsequently perform a fault detection and identification. The method provides a reliability quantification of the resulting condition assessment in order to avoid misclassifications and identify the actual fault root-causes. The performance and the effectiveness of the proposed methodology is validated experimentally and compared with the classical maximum temperature gradient procedure.
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