Abstract
Abstract
The high-pressure heater system is an important part of the return heat system of thermal power units, which can significantly reduce the boiler fuel consumption and is of great significance to the safe and economic operation of the units. Taking into consideration the issues that the high-pressure heater system data has strong non-linear characteristics and the fault diagnosis accuracy is low, this paper proposes a hybrid model-based fault monitoring and diagnosis method for a high-pressure heater system. Firstly, an improved particle swarm optimization algorithm (IEDPSO) is proposed. A differential evolution operation is introduced to enhance particle diversity, and the inertia weight coefficients and learning factor parameters are improved to optimize the particle position and velocity update process. The problem that PSO tends to fall into local optimum at the late stage of iterative optimization search is solved. Numerical simulation experiments demonstrate that IDEPSO has high convergence speed and accuracy in the optimization process of the test function. Secondly, a fault monitoring and diagnosis method based on a hybrid kernel principal component analysis (KPCA)-IDEPSO-probabilistic neural network (PNN) model is proposed. Non-linear features are extracted using KPCA for fault monitoring. The IDEPSO algorithm is used to iteratively find the best PNN to improve the fault diagnosis accuracy. Simulation experiments prove that compared with the traditional PNN model, the fault diagnosis accuracy of the KPCA-IDEPSO-PNN model is improved by 4.9% and the number of fault misclassifications is reduced by 34, effectively improving the fault diagnosis accuracy of the high-pressure heater system and ensuring the safe and stable operation of thermal power units.
Funder
S&T Program of Hebei
Natural Science Foundation of Hebei Province
Central universities basic research business expenses special funds
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)
Reference29 articles.
1. Feature extraction method of rolling bearing based on adaptive divergence matrix linear discriminant analysis;Mingfeng;Meas. Sci. Technol.,2021
2. Local coordinates and global structure preservation for fault detection and diagnosis;Yuanjian;Meas. Sci. Technol.,2021
3. Sparsity-based fractional spline wavelet denoising via overlapping group shrinkage with non-convex regularization and convex optimization for bearing fault diagnosis;Lei;Meas. Sci. Technol.,2019
4. Adaptive tacholess order tracking method based on generalized linear chirplet transform and its application for bearing fault diagnosis;Duan;ISA Trans.,2021
5. Robust fault diagnosis filter design for linear time-varying time-delay continuous-discrete description systems;Tiantian;Chin. J. Inertial Technol.,2018
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献