Inverse Filtering Method of Bare Thermocouple for Transient High-Temperature Test With Improved Deep Belief Network

In this paper, we focus on the inverse filtering method for restoring transient heat conduction processes. First, we address the defects of contact temperature sensor used in transient high temperature test, further we expand the distortion of transient calibration result caused by the lack of dynamic characteristics of bare thermocouple, then the inverse filtering idea is proposed. Second, we propose an improved deep belief network (DBN) to extract the characteristic of thermocouple dynamic nonlinearity on dynamic response signal. The continuous restricted Boltzmann machine (CRBM) is adopted to improve the input layer structure which the input samples can be continuous, and the back propagation (BP) neural network is adopted to realize input-output approximation at output layer. Additionally, dynamic response signal of the thermocouple is obtained through the constructed laser narrow pulse calibration system, and dynamic inverse filtering of thermocouple is realized by using the standard source as the approximation target. The performance of the inverse filtering is analyzed by comparing with the traditional linear least square(LS) method, and it is verified that this method can more accurately approximate the transient high temperature physical process in the calibration environment.