Chiller sensor fault diagnosis based on circle mapping and adaptive t-distribution Runge-Kutta algorithm optimized LSTM.

ABSTRACT: Faulty or inaccurate sensors can lead to increased energy consumption, disrupt control systems, and potentially cause damage to equipment. To tackle this challenge, we propose the Circle Mapping and Adaptive t-Distribution Runge-Kutta optimizer (CTRUN) to optimize the hyperparameters of a Long Short-Term Memory (LSTM) neural network designed explicitly for chiller sensor fault detection. CTRUN incorporates circle chaotic mapping for initialization and utilizes adaptive t-Distribution mutation. We compared CTRUN's performance against several optimization algorithms, including the Runge-Kutta optimizer (RUN), particle swarm optimization (PSO), sparrow search algorithm (SSA), simulated annealing particle swarm optimization algorithm (SimuAPSO), adaptive spiral flying sparrow search algorithm

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12780203 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature