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Correction

Frontiers in Mechanical Engineering ·

Volume 12 - 2026 | https://doi.org/10.3389/fmech.2026.1977529

Correction: Fault diagnosis and prediction of industrial hydraulic pumps based on KOA-CNN-BILSTM model

  • Ting Zhong1
  • Weiyi Zhu1*corresponding author
  1. College of Big Data and Statistics, Sichuan Tourism University, Chengdu, China
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Abstract

Keywords

Equation 4 in 2.2 Section was erroneously given as y k l = ReLU ∑ i = 1 K c ω k , j · x l + i − 1 + b k. The correct equation is y k l = ReLU ∑ i = 1 K c ω k , i · x l + i − 1 + b k.

Among them, Please revise the sentence. The sentence in Line 383 of Section 2.2 was erroneously given as “In Equation 4, ω k , j is the weight of the ith conv kernel”. The correct sentence is “In Equation 4, ω k , i is the weight of the ith conv kernel”.

Equation 6 in 2.2 Section was erroneously given as f t = S i g m o i d W f · h t − 1 , x t + b f d t = S i g m o i d W d · h t − 1 , x t + b d C t ∼ = tanh W c · h t − 1 , x t + b c C t = f t ⊙ C t − 1 + i i ⊙ C t ∼ o t = S i g m o i d W o · h t − 1 , x t + b o h t = o t ⊙ tanh C t.

The correct equation isf t = S i g m o i d W f · h t − 1 , x t + b f i t = S i g m o i d W d · h t − 1 , x t + b d C t ∼ = tanh W c · h t − 1 , x t + b c C t = f t ⊙ C t − 1 + i t ⊙ C t ∼ o t = S i g m o i d W o · h t − 1 , x t + b o h t = o t ⊙ tanh C t.

Among them, Please revise the sentence. The sentence in Line 400 of Section 2.2 was erroneously given as “input gate d t”. The correct sentence is “input gate i t”.

Equation 8 in 2.2 Section was erroneously given as y j ∼ = exp W j + b j ∑ k = 1 k exp W k + b k. The correct equation is y j ∼ = exp W j · v + b j ∑ k = 1 k exp W k + b k.

Equation 12 in 2.2 Section was erroneously given as Q i t + 1 = Q i t + α r 3 ⊙ Q s u n − Q i t + 1. The correct equation is Q i t + 1 = Q i t + α r 3 ⊙ Q s u n − Q i t.

The original version of this article has been updated.

There was a mistake in Table 1 as published. The corrected Table 1 appears below.

TABLE 1. Parameter settings of the KOA-CNN-BILSTM.

ModuleParameterValue/Range
CNNNumber of conv layers2
Number of filters (Layer 1)64
Number of filters (Layer 2)128
Kernel size3
Pooling size2
Stride2
Activation functionReLU
BILSTMHidden units128
Number of layers1
Dropout rate0.5
TrainingLearning rate0.001
Batch size64
Epochs200
OptimizerAdam
Loss functionCross-entropy
KOAPopulation size20
Maximum iterations50
Optimization dimension4
Inertia weight0.9 linearly decreasing to 0.4
Learning factors2.0, 2.0
Rotation coefficient0.1

There was a mistake in Figure 9 as published. The corrected Figure 9 appears below.

There was a mistake in the caption of Figure 5 as published. The corrected caption of Figure 5 appears below.

Comparison of accuracy and precision curves. **(a)** Accuracy; **(b)** Precision.
Figure 1. Comparison of accuracy and precision curves. (a) Accuracy; (b) Precision.

“Figure 5 Comparison of accuracy and precision curves. (a) Accuracy; (b) Precision”.

There was a mistake in the caption of Figure 7 as published. The corrected caption of Figure 7 appears below.

Comparison of training time, inference time and the GFLOPs **(a)** Training time; **(b)** Inference time; **(c)** GFLOPs.
Figure 2. Comparison of training time, inference time and the GFLOPs (a) Training time; (b) Inference time; (c) GFLOPs.
The average predicted probabilities for each health status under various levels of wear and tear.
Figure 3. The average predicted probabilities for each health status under various levels of wear and tear.

“Figure 7 Comparison of training time, inference time and the GFLOPs (a) Training time; (b) Inference time; (c) GFLOPs”.

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Footnotes

Edited and reviewed by: Donato Perfetto, Università della Campania Luigi Vanvitelli, Italy

Citation

Zhong, T. & Zhu, W. (2026). Correction: Fault diagnosis and prediction of industrial hydraulic pumps based on KOA-CNN-BILSTM model. Frontiers in Mechanical Engineering, Volume 12. https://doi.org/
Received
Accepted
Published
Volume
12 - 2026

Copyright

Copyright © 2026 Zhong and Zhu.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Weiyi Zhu

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