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基于深度學(xué)習的傳感器故障數據分析系統設計
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航空工業(yè)西安飛機工業(yè)集團有限公司

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Design of sensor fault data analysis system based on deep learning
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    摘要:

    傳統傳感器故障數據分析系統硬件及程序設計不夠兼容,存在實(shí)時(shí)性差,分析結果不夠精準的問(wèn)題。據此,提出基于深度學(xué)習設計了一種新的傳感器故障數據分析系統,由傳感器、ARM數據處理器、主電路板、FODI數據處理器、集成采集接口板、故障數據傳感器、多轉質(zhì)感器、場(chǎng)效應傳感器、GKCL儲存器組成系統的硬件結構,ASVH248的最大特點(diǎn)就是分辨率高,能夠有效提高系統顯示的清晰度。分別設計了故障數據采集程序、數據處理程序和數據存儲程序。為了檢測系統的有效性,由采集程序采集傳感器內部數據,處理程序對數據結果進(jìn)行分析,存儲程序負責記錄分析后的結果。設定對比實(shí)驗,結果表明,基于深度學(xué)習設計的傳感器故障數據分析系統分析結果精準度提高了15.28%,實(shí)時(shí)性更強,使用價(jià)值更高。

    Abstract:

    The traditional sensor fault data analysis system hardware and program design are not compatible enough, and there is a problem that the real-time performance is poor and the analysis result is not accurate enough. Based on this, a new sensor fault data analysis system based on deep learning is proposed, which consists of sensor, ARM data processor, main circuit board, FODI data processor, integrated acquisition interface board, fault data sensor, multi-turn sensor, The field effect sensor and GKCL memory form the hardware structure of the system. The biggest feature of ASVH248 is the high resolution, which can effectively improve the clarity of the system display. Fault data acquisition programs, data processing programs, and data storage programs are designed separately. In order to check the effectiveness of the system, the internal data of the sensor is collected by the acquisition program, the processing program analyzes the data result, and the storage program is responsible for recording the result of the analysis. The contrast experiment was set up. The results show that the accuracy of the analysis results of the sensor fault data analysis system based on deep learning design is 15.28%, and the real-time performance is stronger and the use value is higher.

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王心宇,魏詩(shī)朦,陳韻秋.基于深度學(xué)習的傳感器故障數據分析系統設計計算機測量與控制[J].,2020,28(6):266-270.

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歷史
  • 收稿日期:2019-10-23
  • 最后修改日期:2019-11-08
  • 錄用日期:2019-11-11
  • 在線(xiàn)發(fā)布日期: 2020-06-17
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