Chiller Fault Diagnosis Based on Automatic Machine Learning
| Autor: | Huiyu Xue, Youyin Wang, Zhuolun Chen, Xin Ma, Chongyi Tian |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
transient co-simulation
Economics and Econometrics Computer science Energy Engineering and Power Technology Machine learning computer.software_genre Chiller Long short-term memory network General Works automatic machine learning Genetic algorithm Redundancy (engineering) Feature (machine learning) long short-term memory network Fault diagnosis Hyperparameter Renewable Energy Sustainability and the Environment business.industry chiller fault diagnosis Solver Automatic machine learning Support vector machine Fuel Technology Recurrent neural network Artificial intelligence Performance improvement Transient co-simulation business computer |
| Zdroj: | Frontiers in Energy Research, Vol 9 (2021) Tian, C, Wang, Y, Ma, X, Chen, Z & Xue, H 2021, ' Chiller Fault Diagnosis Based on Automatic Machine Learning ', Frontiers in Energy Research, vol. 9, 753732 . https://doi.org/10.3389/fenrg.2021.753732 |
| ISSN: | 2296-598X |
| DOI: | 10.3389/fenrg.2021.753732 |
| Popis: | Intelligent diagnosis is an important means of ensuring the safe and stable operation of chillers driven by big data. To address the problems of input feature redundancy in intelligent diagnosis and reliance on human intervention in the selection of model parameters, a chiller fault diagnosis method was developed in this study based on automatic machine learning. Firstly, the improved max-relevance and min-redundancy algorithm was used to extract important feature information effectively and automatically from the training data. Then, the long short-term memory (LSTM) model was used to mine the temporal correlation between data, and the genetic algorithm was employed to train and optimize the model to obtain the optimal neural network architecture and hyperparameter configuration. Finally, a transient co-simulation platform for building chillers based on MATLAB as well as the Engineering Equation Solver was built, and the effectiveness of the proposed method was verified using a dynamic simulation dataset. The experimental results showed that, compared with traditional machine learning methods such as the recurrent neural network, back propagation neural network, and support vector machine methods, the proposed automatic machine learning algorithm based on LSTM provides significant performance improvement in cases of low fault severity and complex faults, verifying the effectiveness and superiority of this method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|