Missing Sensor Data Restoration: Computationally Intelligent Discovery of Reading Dependencies

该项目将探索使用计算智能新技术的可能性，以解决由复杂传感器阵列收集的数据中缺失值的输入任务。受限制环境中的传感器可以具有彼此相关的读数，这样一组故障传感器的丢失数据可以从剩余传感器的测量中恢复。当重建的传感器读数作为输入提供给一个过程，因此系统的操作仍然是可能的。如果有的话，整体性能会优雅地下降。数据约束的发现可以通过使用传感器数据训练自动编码器来实现。经过训练的编码器，通过经验发现数据之间的相互关系，然后可以用来恢复故障传感器丢失的读数。这可以通过交替投影到凸集（POCS）算法的应用或更传统的搜索来实现。缺失传感器数据（MISED）恢复的成功发展将对当前的一些技术产生重大影响。该项目将重点关注当前能源和航空电子领域开放和重要问题的应用。该项目得到了两个行业的大力支持。

0114483El-SharkawiThis project will explore the possibility of using new techniques from computational intelligence in order to address the task of imputing missing values in data collected by complex arrays of sensors. Sensors in a restricted environment can have readings relating to each other in such a matter that missing data from a set of failed sensors can be restored from the measurement of those remaining. When the reconstructed sensor readings are supplied as input to a process, the operation of the system is therefore still possible. Overall performance will degrade gracefully, if at all. Discovery of data constraints can be achieved by training an autoencoder using sensor data. The trained encoder, having empirically discovered interrelations among data, can then be used to restore lost readings from failed sensors. This is achieved through either application of an alternating projection onto convex set (POCS) algorithm or a more conventional search.Successful development of missing sensor data (MISED) restoration will have significant impact on a number of current technologies. The project will focus on applications to currently open and important problem in energy and avionics. The project has strong support from both industries.