Optimized SVM-Driven Multi-Class Approach by Improved ABC to Estimating Ship Systems State

Cao, Hui; Zhang, Jundong; Cao, Xu; Li, Ran; Wang, Yiru

doi:10.1109/access.2020.3037251

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…They proposed a novel approach for diesel engine fault identification based on adaptive WVD, improved FCBF, and relevance vector machine (RVM). Cao [17] introduced an optimization-driven approach for improving the training speed and testing accuracy of diesel engine fault state models. This approach is based on an enhanced artificial bee colony (IABC) optimization technique, aimed at addressing the global parameter optimization problem in support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Marine Diesel Engines Based on Efficient Channel Attention-Improved Convolutional Neural Networks

Wang,

Cao,

Cui

et al. 2023

Processes

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With the rapid development of smart ships, the ship maintenance model is also changing. In order to extract the fault characteristics of diesel engine thermal parameters more easily, reduce the model’s complexity and improve the model’s accuracy, a new approach is proposed: first, the traditional convolutional neural networks (improved convolutional neural networks (ICNN)) are improved by using Meta-ACON as the activation function, improved AdamP as the optimizer, and label smoothing regularization (LSR) as the loss function, which enhances the stability of the model. Secondly, efficient channel attention (ECA) is added to achieve the mastery of global feature information, reduce the complexity of the traditional self-attention module, and enhance the model’s feature extraction ability. Lastly, the accuracy and reliability of the model are verified through ablation and comparison experiments. The accuracy rate reaches 97.6%, which is significantly improved by 32.1% compared with the original model, and the robustness of the model is verified through the introduction of noise. The experimental results demonstrate the applicability of the model in the field of diesel engine fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Marine Diesel Engines Based on Efficient Channel Attention-Improved Convolutional Neural Networks

Wang,

Cao,

Cui

et al. 2023

Processes

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“…Xu et al [6] fused an artificial neural network (ANN) model, a belief rule-based reasoning (BRB) model, and an ER rule model and used a genetic algorithm to optimize the importance weights of each model to improve the overall performance of the fusion system, and finally, through the three models, joint decision-making to realize the fault diagnosis of the ship system. In recent years, algorithmic improvements have achieved significant results in the field of industrial fault diagnosis [7][8][9][10][11]. Samet [12] proposed a new kNN-based classifier (PFS-kNN) to find the k-nearest neighbors using Minkowski's metric of the image fuzzy soft matrix, which was validated on the UCI medical dataset, and the PFS-kNN outperformed the most state-of-the-art kNN-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion

Ai,

Cao,

Wang

et al. 2023

Applied Sciences

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At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis.

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“…Support Vector Machine (SVM) method is a widely used classifier and has gained momentum for its efficiency in various application domains. An optimized SVM approach is proposed in [2] to solve the global parameters optimization problem for ship systems state estimation. In the problem of the SVM classification of imbalanced datasets, authors in [3] suggested an approach to optimal parameters selection for the synthetic minority over-sampling technique algorithm.…”

Section: Introductionmentioning

confidence: 99%

Efficient Machine Learning Algorithm for Embedded Tactile Data Processing

Saleh

Ibrahim

Menichelli

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Employing Machine learning algorithms in tactile sensing systems have emerged recently to recognize/classify touch patterns. The high computational complexity of the ML algorithms makes challenging the embedded implementation of tactile data processing. This paper proposes a complexity optimized tensorial-based machine learning algorithm for touch modality classification. The aim is to introduce an efficient algorithm minimizing the system complexity in terms of number of operations and memory storage which directly affect time latency and power consumption. With respect to the state of the art, the proposed approach reduces the number of operations per inference from 545 M-ops to 18 M-ops and the memory storage from 52.2 KB to 1.7 KB. Moreover, the proposed method speeds up the inference time by a factor of 43× at a cost of only 2% loss in accuracy.

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Optimized SVM-Driven Multi-Class Approach by Improved ABC to Estimating Ship Systems State

Cited by 6 publications

References 27 publications

Intelligent Fault Diagnosis of Marine Diesel Engines Based on Efficient Channel Attention-Improved Convolutional Neural Networks

Intelligent Fault Diagnosis of Marine Diesel Engines Based on Efficient Channel Attention-Improved Convolutional Neural Networks

Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion

Efficient Machine Learning Algorithm for Embedded Tactile Data Processing

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