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Parallel deep neural network for scalable coupling fault diagnosis in HVAC systems.

Number: 0564

Author(s) : CHEN S., LIU Z., CHEN K., ZHU X., JIN X., DU Z.

Summary

Reliable  fault  detection  and  diagnosis  (FDD)  models  are  essential  for  ensuring  operation  safety  and  decreasing energy wastage in HVAC systems. However, most existing studies ignore coupling faults and are short of scalability, which limit the practical application. To this end, a parallel deep neural network was proposed for scalable coupling fault diagnosis. The parallel network features a main network for fault feature extraction and several sub networks for diagnosing each type of faults, which has high scalability and twice training speed than serial network. To verify the technical feasibility of proposed method, the experiments were  conducted  in  a  typical  refrigeration  system  for  simulating  common  three  single  faults  and  three  coupling  faults.  The  diagnosis  accuracy  of  presented  model  was  99.57%,  which  was  higher  than  other  machine learning algorithms such as support vector machine (93.77%), artificial neural network (91.98%) and logistic  regression  (86.70%).  Our  study  aims  to  promoting  practical  application  of  FDD  models  in  HVAC  systems.

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Pages: 12

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Details

  • Original title: Parallel deep neural network for scalable coupling fault diagnosis in HVAC systems.
  • Record ID : 30031809
  • Languages: English
  • Subject: Technology
  • Source: Proceedings of the 26th IIR International Congress of Refrigeration: Paris , France, August 21-25, 2023.
  • Publication date: 2023/08/21
  • DOI: http://dx.doi.org/10.18462/iir.icr.2023.0564

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