An LSTM-BINN Approach for Built-In Test Analog Signal State Recognition of Heavy-Duty Gas Turbine Controllers

Abstract

Built-in test (BIT) is widely employed in equipment state detection. However, the high false alarm rate (FAR) of conventional BIT leads to troubles for manufacturers and users. To solve this problem, a novel BIT analog signal recognition algorithm that integrates long short-term memory (LSTM) and biologically inspired neural network (BINN) is proposed in this article. The effects of signal noise and intermittent faults on BIT results are effectively reduced. The proposed algorithm is mainly used for the BIT analog signal of heavy-duty gas turbine controllers, LSTM is used for preliminary modeling of analog signals, and the BINN is introduced into the extra signal state recognition, serving as a classifier based on stimulation transmission among neurons. Furthermore, the gravitational search algorithm (GSA) is employed to optimize the parameters of the proposed algorithm to improve its performance. By employing BIT temperature signal data from heavy-duty gas turbine controllers, extensive experimental results were given to show that the proposed approach was reasonable and accurate by comparison with conventional BIT. By comparing with other common neural network algorithms, the developed approach can provide lower FAR and more accurate recognition results.