Work

Measuring System Entropy with a Deep Recurrent Neural Network Model

Year: 2019

Type: conference-paper

Abstract: In this paper, a methodology for assessing the unpredictability of systems with memory was developed. The proposed approach consists in approximating the probability distribution exhibited by the response of a system, understood as a stochastic process, with a deep recurrent neural network; such networks offer increased forecasting capability by exploiting an accumulative register of previous system states. Once the probability distribution is computed, the uncertainty or entropy of the underlying process is measured. This measure determines the degree of regularity in the system, and identifies how atypical the system dynamics are. The proposed model was validated by identifying industrial gas turbine engine faults from recorded sensor data.

Source: IEEE International Conference on Industrial Informatics (INDIN)

DOI: 10.1109/indin41052.2019.8972068

Authors: Miguel Martínez-García, Yu Zhang, Kenji Suzuki, Yudong Zhang

Institutions: University of Lincoln, University of Leicester, Illinois Institute of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Award: G1067029627