Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network

Fang Dao, Yun Zeng and Jing Qian

Energy, 2024, vol. 290, issue C

Abstract: The hydro-turbine is the core equipment of the hydropower station, and it is essential to diagnose and identify its faults. A fault diagnosis model based on Bayesian optimization (BO), which incorporates convolutional neural network (CNN) and long short-term memory (LSTM) methods for the hydro-turbine, is proposed (BO–CNN-LSTM). CNN adaptively extracts and down-scales fault features, fed into the LSTM model for feature learning and training. The BO algorithm is employed to address the challenge of model hyperparameter selection. A hydro-turbine fault experiment bench is constructed to train and validate the model. Experimental results demonstrate the superior performance of the proposed BO-CNN-LSTM model in hydro-turbine fault diagnosis, achieving accuracies of 92.7 %, 98.4 %, and 90.4 %, respectively, surpassing CNN, LSTM, and CNN-LSTM models. The BO-CNN-LSTM model improves accuracy by 5.5 %, 6.3 %, and 9.0 %, respectively, Compared to the unoptimized CNN-LSTM model. The BO algorithm is introduced to optimize CNN-LSTM from the perspective of acoustic vibration signals, which can be a beneficial supplement to the existing hydro-turbine fault diagnosis.

Keywords: Hydro-turbine; Fault diagnosis; CNN-LSTM; Bayesian optimization; Deep learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224000975
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544224000975

DOI: 10.1016/j.energy.2024.130326

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().