Search

Dangerous gas detection technology, which plays an important role in industrial production, is one of the necessary guarantees for the safety of petrochemical and other enterprises. Firstly, the advantages and disadvantages of existing hazardous gas detection technologies were compared, clarifying that infrared imaging technology based on molecular absorption spectroscopy had the advantages of being suitable for long-range monitoring and low maintenance costs in hazardous gas detection thus was the ideal form of wide area surveillance. Secondly, the detection principle and hardware composition of infrared imaging technology were introduced. Next, some related researches on leakage image visualization and gas concentration analysis were enumerated. Then, the automatic gas leak detection algorithms based on infrared image analysis technology were analyzed, and the development status and problems of traditional image-processing-based methods and deep learning algorithms were analyzed in depth respectively. Finally, the technical difficulties and development trends of infrared spectral imaging were summarized. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that in the optimization of support vector machines, data-driven rolling bearing fault diagnosis, most of them are classified by support vector machine, however, the traditional support vector machine classification method is easy to fall into local optimum, and it is impossible to carry out fault diagnosis accurately, the feature selection of rolling bearing vibration signal and the optimization method of support vector machine were studied. The disadvantages of the support vector machine optimization by using genetic algorithm and particle swarm optimization were pointed out. In order to improve the accuracy of rolling bearing fault diagnosis, a cuckoo search algorithm based on Levy flight was introduced to find the optimal parameters of support vector machine. First, ensemble empirical mode decomposition was used to process signal data, and then the root mean square of the intrinsic mode function was put into the support vector machine optimized by cuckoo search algorithm to train and test this model. The results indicate that the proposed method can be used to analyze and diagnose the measured signals, and the location and severity of the faults can be accurately identified. The superiority of the algorithm is verified by comparison with traditional optimization methods. [ABSTRACT FROM AUTHOR]

Copyright of Research & Exploration in Laboratory is the property of Research & Exploration in Laboratory and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)