New Method of Fault Knowledge Acquisition of Electronic Equipment

Abstract

Interactive Electronic Technical Manual (IETM) serves as a key supporting technology of aeronautic equipment support informatization and is a carrier for structural, interactive and intelligent equipment technology data. In order to solve the problems that the traditional fault diagnosis system is single in diagnosis-oriented object, low in universality and insufficient in diagnosis knowledge and the like, accurate and efficient fault diagnosis can be performed by virtue of lots of fault diagnosis knowledge in aeronautic equipment IETM. However, knowledge source of the fault diagnosis knowledge exists in various systems of various stages of aeronautic equipment service life, knowledge acquisition model is established with respect to different fault diagnosis knowledge acquisition methods at various stages of study, and isomerism of each system is shielded, thereby realizing resource sharing, improving the fault diagnosis knowledge quality and providing more accurate fault diagnosis service for aeronautic equipment.

Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging

“…Reference [2] used the one-dimensional OTSU algorithm to segment the seed region and used the region growing algorithm to segment the fault region of the power equipment. Reference [3] extracted the fault region of infrared image of power equipment by simplifying the internal parameters of the pulse-coupled neural network and combining the local features in the boundary between the non-fault region and fault region neighborhood. Turbopixel superpixel segmentation algorithm was used in reference [4] to segment infrared fault images, and the fault areas were extracted with chromaticity information.…”