Chiller Fault Diagnosis Based on Automatic Machine Learning

Chongyi Tian, Youyin Wang, Xin Ma*, Zhuolun Chen, Huiyu Xue

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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.
Original languageEnglish
Article number753732
JournalFrontiers in Energy Research
Volume9
Number of pages17
ISSN2296-598X
DOIs
Publication statusPublished - 2021

Keywords

  • Chiller
  • Fault diagnosis
  • Long short-term memory network
  • Automatic machine learning
  • Transient co-simulation

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