An improved bearing fault detection strategy based on artificial bee colony algorithm

Wang, Haiquan; Yue, Wenxuan; Xu, Xiaobin; Haasis, Hans–Dietrich; Su, Menghao; Liu, Ping; Zhang, Shanshan; Du, Panpan

doi:10.1049/cit2.12105

Cited by 22 publications

(16 citation statements)

References 32 publications

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“…Intelligent fault diagnostics have successfully been applied to identifying bearing faults without the need for human interference and the a priori knowledge of experts [3,4]. In the realm of bearing fault diagnosis, deep learning methods have surpassed conventional machine learning techniques, notably support vector machines [5] and artificial neural networks [6], demonstrating remarkable success in identifying faults.…”

Section: Introductionmentioning

confidence: 99%

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Hei,

Shi,

Fan

et al. 2024

Meas. Sci. Technol.

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Transfer learning (TL) has demonstrated effective application in diagnosing bearing faults under varying operating conditions. The current TL methods achieve domain alignment by minimizing the variation in the marginal distribution of data between the source and target domains in the feature space. However, this approach yields incomplete similarity, leading to domain shift and a decrease in diagnostic performance. To overcome this problem, this paper proposes a new distance-guided domain adaptation (DGDA) method that consists of two modules: Deep domain adaptive correlation alignment (Deep CORAL) combined with Joint Maximum Mean Discrepancy (JMMD) for guided domain adaptation. Deep CORAL employs nonlinear transformations to synchronize second-order statistical correlations across source and target domains, thus ensuring feature-level alignment between these domains. JMMD is utilized to align the joint distribution of input features and output labels within the activation layer in the deep network, thereby bolstering domain alignment. Building on this, we propose a network structure that merges ResNet and BiLSTM, powered by wavelet kernels, serving as a feature extractor. This structure is designed to learn domain-invariant features and incorporates attention mechanisms to amplify important information while diminishing the impact of redundant data. An analysis of bearing experiments is used to demonstrate the effectiveness of this method, and the proposed method significantly outperforms several popular methods in diagnostic performance.

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Section: Introductionmentioning

confidence: 99%

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Hei,

Shi,

Fan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The rapid development of communication technology and big data processing has facilitated in-depth research on complex data-driven decision models in various industry fields, such as fault detection and diagnosis [ 1 , 2 ], real-time decision-making in power systems [ 3 , 4 ], etc.…”

Section: Introductionmentioning

confidence: 99%

A Risk Evaluation Framework in System Control Subject to Sensor Degradation and Failure

Yuan,

Xu,

Adjallah

et al. 2024

Sensors

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Sensor degradation and failure often undermine users’ confidence in adopting a new data-driven decision-making model, especially in risk-sensitive scenarios. A risk assessment framework tailored to classification algorithms is introduced to evaluate the decision-making risks arising from sensor degradation and failures in such scenarios. The framework encompasses various steps, including on-site fault-free data collection, sensor failure data collection, fault data generation, simulated data-driven decision-making, risk identification, quantitative risk assessment, and risk prediction. Leveraging this risk assessment framework, users can evaluate the potential risks of decision errors under the current data collection status. Before model adoption, ranking risk sensitivity to sensor data provides a basis for optimizing data collection. During the use of decision algorithms, considering the expected lifespan of sensors enables the prediction of potential risks the system might face, offering comprehensive information for sensor maintenance. This method has been validated through a case study involving an access control.

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“…The occurrence of misfire will lead to serious vibration, insufficient power, weak acceleration, and high fuel consumption [2]. Therefore, it is of vital significance to monitor engine running state on-line and take corresponding measures [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

Qin

Jin

Zhang

et al. 2023

CAAI Trans on Intel Tech

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Currently, accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited. A novel multi‐scale CNN‐LSTM neural network (MSCNN‐LSTMNet) is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire diagnosis. First, a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is utilised. Considering the essential characteristics of measured vibration signals at different scales, a multi‐scale convolutional NN (CNN) block is designed to realize multi‐scale feature extraction. Specifically, multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features, enhancing the robustness of the model. On this basis, the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisation performances. The effectiveness of MSCNN‐LSTMNet is validated by experimental results of both one‐ and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working conditions. The results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing methods. Under strong noise conditions (−10 dB signal‐to‐noise ratio) for four datasets, MSCNN‐LSTMNet obtained 97.561% average accuracy, while average accuracy for random forest, deep neural network, CNN and MSCNNNet were 73.828%, 79.544%, 82.247%, and 89.741%, respectively. Moreover, for 11 noise generalisation tasks between different noise levels, MSCNN‐LSTMNet obtained at least 96.679%, 97.849%, 98.892%, and 94.010% accuracy on the four datasets, which are much higher than those of the existing methods.

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An improved bearing fault detection strategy based on artificial bee colony algorithm

Cited by 22 publications

References 32 publications

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

A Risk Evaluation Framework in System Control Subject to Sensor Degradation and Failure

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

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