Skip to main content

Advertisement

SpringerLink
Log in

Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

A Correction to this article was published on 18 November 2022

This article has been updated

Abstract

Developing fault diagnosis for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. This paper mainly studies and explores the compound fault diagnosis for the cooling dehumidifier. Firstly, the dehumidifier data acquisition system is built, which can be applied to the data acquisition, work status simulation and fault diagnosis. Secondly, a compound fault diagnosis model based on radial basis function neural network (RBFNN) improved by kernel principle component analysis (KPCA) and adaptive genetic algorithm (AGA) is proposed. Aiming at the problems that the selection of RBF width depends on expert experience and knowledge, the network structure scale is large or the training speed is slow in the conventional RBFNN models; on the one hand, AGA and K-means clustering algorithm are employed to automatically optimize the RBF width, the number of hidden layer neurons and the neuron centers, which guarantees the model has small structure and fast running speed on the premise of sufficient output precision, and the local optimization problem of K-means clustering algorithm is well suppressed by AGA group calculation; this also ensures the performance of RBFNN; on the other hand, KPCA is employed to reduce the dimension of the model input data, which not only effectively extract the nonlinear features, but also further simplify the network structure. Finally, the proposed method is validated and compared with the conventional models. The results show that this proposed model can not only be effectively applied to the dehumidifier compound fault diagnosis, but also has prominent application advantages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

Change history

References

  • Ashena R, Moghadasi J (2011) Bottom hole pressure estimation using evolved neural networks by real coded ant colony optimization and genetic algorithm. J Petrol Sci Eng 77(3–4):375–385

    Article  Google Scholar 

  • Balaga H, Gupta N, Vishwakarma DN (2015) GA trained parallel hidden layered ANN based differential protection of three phase power transformer. Electr Power Energy Syst 67(3):286–297

    Article  Google Scholar 

  • Belagoune S, Bali N, Bakdi A, Baadji B, Atif K (2021) Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. Measurement 177:1–14

    Article  Google Scholar 

  • Cao LJ, Chua KS, Chong WK (2003) A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1–2):321–336

    Google Scholar 

  • Chen YM, Lan LL (2009) A fault detection technique for air-source heat pump water chiller/heaters. Energy Build 41(8):881–887

    Article  Google Scholar 

  • Chen Y, Ji HQ, Liu HL, Sun LZ (2016) A traffic identification based on PSO-RBF neural network in peer-to-peer network. Int J Comput Sci Eng 13(2):158–164

    Google Scholar 

  • Chen S, Ge H, Li H, Sun Y, Qian X (2021) Hierarchical deep convolution neural networks based on transfer learning for transformer rectifier unit fault diagnosis. Measurement 167:1–21

    Article  Google Scholar 

  • Chiddarwar SS, Babu NR (2010) Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach. Eng Appl Artif Intell 23(7):1083–1092

    Article  Google Scholar 

  • Chui KT, Gupta BB, Liu RW, Vasant P (2021) Handling data heterogeneity in electricity load disaggregation via optimized complete ensemble empirical mode decomposition and wavelet packet transform. Sensors 21:3133:1–3133:13. https://doi.org/10.3390/s21093133.

  • Chui KT, Gupta BB, Vasant P (2021) A genetic algorithm optimized RNN-LSTM model for remaining useful life prediction of turbofan engine. Electronics 10: 285:1–285:15. https://doi.org/10.3390/electronics10030285.

  • Dahiya A, Gupta BB (2021) A reputation score policy and Bayesian game theory based incentivized mechanism for DDoS attacks mitigation and cyber defense. Futur Gener Comput Syst 117:193–204

    Article  Google Scholar 

  • Du ZM, Jin XQ, Yang YY (2009) Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network. Appl Energy 86(9):1624–1631

    Article  Google Scholar 

  • Du ZM, Fan B, Jin XQ, Chi JL (2014) Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Build Environ 73(3):1–11

    Article  Google Scholar 

  • Elgendy IA, Zhang WZ, He H, Gupta BB, Abd El-Latif AA (2021) Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms. Wireless Netw 27:2023–2038

    Article  Google Scholar 

  • Fan B, Du ZM, Jin XQ, Yang XB, Guo YB (2010) A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis. Build Environ 45(12):2698–2708

    Article  Google Scholar 

  • Gao YG, Liu SB, Li F, Liu ZG (2016) Fault detection and diagnosis method for cooling dehumidifier based on LS-SVM NARX model. Int J Refrig 61(1):69–81

    Article  Google Scholar 

  • Gonzalez J, Rojas I, Pomares H (2002) A new clustering technique for function approximation. IEEE Trans Neural Netw 13(1):132–142

    Article  Google Scholar 

  • Han H, Cao ZK, Gu B, Ren N (2010) PCA-SVM-Based automated fault detection and diagnosis (AFDD) for vapor-compression refrigeration systems. HVAC&R Res 16(3):295–313

    Article  Google Scholar 

  • Haves P, Salsbury TI, Wright JA (1996) Condition monitoring in HVAC subsystems using first principles. ASHRAE Trans 102(1):519–527

    Google Scholar 

  • House JM, Lee WY, Shin DR (1999) Classification techniques for fault detection and diagnosis of an air-handling unit. ASHARE Trans 105(2):1087–1097

    Google Scholar 

  • Irani R, Nasimi R (2011) Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir. Expert Syst Appl 38(8):9862–9866

    Article  Google Scholar 

  • Jani DB, Mishra M, Sahoo PK (2016) Performance prediction of solid desiccant-vapor compression hybrid air-conditioning system using artificial neural network. Energy 103:618–629.

    Article  Google Scholar 

  • Kamar HM, Ahmad R, Kamsah NB, Mustafa AFM (2013) Artificial neural networks for automotive air-conditioning systems performance prediction. Appl Therm Eng 50(1):63–70

    Article  Google Scholar 

  • Katipamula S, Brambley MR (2005a) Methods for fault detection, diagnostics, and prognostics for building systems-a review, part I. HVAC&R Res 11(1):3–25

    Article  Google Scholar 

  • Katipamula S, Brambley MR (2005b) Methods for fault detection, diagnostics, and prognostics for building systems-a review, part II. HVAC&R Res 11(2):169–187

    Article  Google Scholar 

  • Khorram A, Khalooei M, Rezghi M (2021) End-to-end CNN plus LSTM deep learning approach for bearing fault diagnosis. Appl Intell 51:736–751

    Article  Google Scholar 

  • Lee SH, Yik FWH (2010) A study on the energy penalty of various air-side system faults in buildings. Energy Build 42(1):2–10

    Article  Google Scholar 

  • Lee WY, House JM, Kyong N (2004) Subsystem level fault diagnosis of a building’s air-handling unit using general regression neural networks. Appl Energy 77(2):153–170

    Article  Google Scholar 

  • Mohammad AT, Mat SB, Sulaiman MY, Sopian K, Al-abidi AA (2013) Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system. Appl Therm Eng 59(1–2):389–397

    Article  Google Scholar 

  • Moody JE, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1:281–294

    Article  Google Scholar 

  • Palmes PP, Hayasaka T, Usui S (2005) Mutation-based genetic neural networks. IEEE Trans Neural Netw 16(3):587–600

    Article  Google Scholar 

  • Pecháč P, Sága M (2017) Memetic algorithm with normalized RBF ANN for approximation of objective function and secondary RBF ANN for error mapping. Proc Eng 177:540–547.

    Article  Google Scholar 

  • Peitsman HC, Bakker V (1996) Application of black-box models to HVAC systems for fault detection. ASHRAE Trans 102(1):628–640

    Google Scholar 

  • Qian XH, Huang H, Chen XP, Huang TG (2017) Generalized hybrid constructive learning algorithm for multioutput RBF networks. IEEE Trans Cybern 47(11):3634–3648

    Google Scholar 

  • Ren PZ, Xiao Y, Chang XJ, Huang PY, Li ZH, Gupta BB, Chen XJ, Wang X (2021) A survey of deep active learning. ACM Comput Surv 54(9):180:1–180:40. https://doi.org/10.1145/3472291.

  • Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  • Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667

    Article  Google Scholar 

  • Wei DF (2017) Network traffic prediction based on RBF neural network optimized by improved gravitation search algorithm. Neural Comput Appl 28(8):2303–2312

    Article  Google Scholar 

  • Wu JN, Wang J, Lui L (2007) Feature extraction via KPCA for classification of gait patterns. Human Movement Sci 26(3):393–411.

    Article  Google Scholar 

  • Ye Z, Yu JB (2021) Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis. Mech Syst Signal Process 161(5):1–17

    Google Scholar 

  • Yoshida H, Kumar S (1999) ARX and AFMM model-based on-line real-time data base diagnosis of sudden fault in AHU of VAV system. Energy Convers Manage 40(11):1191–1206

    Article  Google Scholar 

Download references

Acknowledgements

The authors would be like to thank all scholars and engineers who previously provided theory and technique support.

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. Hunan Sany Polytechnic College, Changsha County, Changsha, China

    Yunguang Gao & An Sheng

  2. Xi’an Research Institute of Hi-Tech, Hongqing Town, Xi’an, China

    Changlin Ma

Authors
  1. Yunguang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changlin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. An Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunguang Gao.

Ethics declarations

Conflict of interests

The authors have not disclosed any competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original article has been updated: Due to textual changes.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, Y., Ma, C. & Sheng, A. Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm. Soft Comput 27, 1599–1613 (2023). https://doi.org/10.1007/s00500-022-07509-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07509-7

Keywords

Search

Navigation