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Prediction of Failed Sensor Data Using Deep Learning Techniques for Space Applications

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Abstract

Recently, space applications have become complicated for post-flight analysis when a sensor fails. Various scholars have researched many types of research based on fault data prediction in aircraft applications, utilizing numerous deep learning techniques. Using these deep learning techniques, the sensor data is re-created with the help of other related sensor data and will help other post-flight analyses. Once the launch vehicle is lifted off, there is no possibility of solving a problem in sensors. Sometimes that particular sensor is very crucial for the onboard decisions. There have to adapt real-time sensor prediction techniques. So, this paper focused on designing an effective prediction technique for fault sensor data with the aid of its corresponding sensor data. The fault sensor data prediction process is performed in two stages such as real-time and offline. Here we apply three deep learning algorithms to predict the fault sensor data, and the three algorithms, LSTM, GRU and CNN, are applied for real-time and offline data prediction. Furthermore, the experimental setup helps for predicting accurate real-time fault sensor data. The results obtained through experimental and simulation analysis are closely matched for a failed sensor, which is very helpful for analyzing and validating the launch vehicle performance.

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Authors and Affiliations

  1. Vikram Sarabhai Space Centre (VSSC), Indian Space Research Organisation (ISRO), Thiruvananthapuram, India

    Renjith Das

  2. Department of Computer applications, Government Arts and Science College, Erstwhile Manonmaniam Sundaranar University Constituent College, Kanyakumari, Tamilnadu, India

    A. Ferdinand Christopher

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  1. Renjith Das
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Correspondence to Renjith Das.

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Das, R., Christopher, A.F. Prediction of Failed Sensor Data Using Deep Learning Techniques for Space Applications. Wireless Pers Commun 128, 1941–1962 (2023). https://doi.org/10.1007/s11277-022-10027-2

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