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Research on automatic diagnosis and optimal control of faults in housing heating systems

Nan Luo

Nan Luo

Suzhou Vocational Institute of Industrial TechnologySuzhou, China
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Luo, Nan
  
05 févr. 2025
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Publié en ligne: 05 févr. 2025
Reçu: 10 sept. 2024
Accepté: 30 déc. 2024
DOI: https://doi.org/10.2478/amns-2025-0071
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© 2025 Nan Luo, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Overall structure of the system
Overall structure of the system

Figure 2.

The optimal scheme of housing heating control system
The optimal scheme of housing heating control system

Figure 3.

Architecture of recurrent neural network
Architecture of recurrent neural network

Figure 4.

The relationship between PID parameter setting and error and error change rate
The relationship between PID parameter setting and error and error change rate

Figure 5.

Simulation results of three temperature control methods
Simulation results of three temperature control methods

Figure 6.

Comparison of simulation results of heating system with fault signal
Comparison of simulation results of heating system with fault signal

Figure 7.

Sample data characteristic quantity correlation analysis heat map
Sample data characteristic quantity correlation analysis heat map

Figure 8.

LSTM-DT recognition effect with different window length and signal-to-noise ratio
LSTM-DT recognition effect with different window length and signal-to-noise ratio

Figure 9.

Comparison diagram of four methods to identify system faults
Comparison diagram of four methods to identify system faults

All kinds of sample data sets after random screening

Type of working condition Quantity (item)
Normal working condition 1892
Window opening condition 988
Clogging condition 1205
Actuator fault condition 900
Gross measurement error 161
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Sujets de la revue:
Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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