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As an important kind of data for device status evaluation, the increasing infrared image data in electrical system puts forward a new challenge to traditional manually processing mode. To overcome this problem, this paper proposes a feasible way to automatically process massive infrared fault images. We take advantage of the imaging characteristics of infrared fault images and detect fault regions together with its belonging device part by our proposed algorithm, which first segment images into superpixels, and then adopt the state-of-the-art convolutional and recursive neural network for intelligent object recognition. In the experiment, we compare several unsupervised pre-training methods considering the importance of a pre-train procedure, and discuss the proper parameters for the proposed network. The experimental results show the good performance of our algorithm, and its efficiency for infrared analysis.

Big data platform for equipment condition assessment is built for comprehensive analysis. The platform has various application demands. According to its response time, its application can be divided into offline, interactive and real-time types. For real-time application, its data processing efficiency is important. In general, data cache is one of the most efficient ways to improve query time. However, big data caching is different from the traditional data caching. In the paper we propose a distributed cache management framework of big data for equipment condition assessment. It consists of three parts: cache structure, cache replacement algorithm and cache placement algorithm. Cache structure is the basis of the latter two algorithms. Based on the framework and algorithms, we make full use of the characteristics of just accessing some valuable data during a period of time, and put relevant data on the neighborhood nodes, which largely reduce network transmission cost. We also validate the performance of our proposed approaches through extensive experiments. It demonstrates that the proposed cache replacement algorithm and cache management framework has higher hit rate or lower query time than LRU algorithm and round-robin algorithm.