A deep learning approach for fault detection and RUL estimation in bearings

Bono, Francesco Morgan; Cinquemani, Simone; Chatterton, Steven; Pennacchi, Paolo

doi:10.1117/12.2607084

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Its purpose is to sub-sample the feature map by retaining only the most attractive information extracted by the convolutional layer. There are many possible pooling functions, but in this work, the MaxPooling one is adopted, which takes only the max value out of a predefined sub-matrix [23].…”

Section: Dilated Cnnmentioning

confidence: 99%

Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN

Yu,

Fu,

Bao

2024

Applied Sciences

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At present, fishery resources are becoming increasingly depleted, and a reliable assessment of fishing activity is a key step in protecting marine resources. Correctly identifying the type of fishing operation can help identify illegal fishing vessels, strengthen fishing regulations, and prevent overfishing. Aiming to address these problems, this study first collects and preprocesses fishing vessel AIS data. Improvements are proposed on the basis of the convolutional neural network (CNN), long short-term memory (LSTM), and other models, changing the CNN to dilated CNN and LSTM to independently recurrent neural network (IndRNN). The results of the experiment show that the accuracy, precision, recall, and F-1 score of the model are finally obtained as 93.12%, 93.10%, 93.14%, and 93.10%, respectively. Overall, the new model proposed in this study offers a significant improvement in performance compared to the models of other scholars in the past.

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Section: Dilated Cnnmentioning

confidence: 99%

Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN

Yu,

Fu,

Bao

2024

Applied Sciences

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“…To bring the data in the format required for their processing with neural networks, some manipulations are performed according to the method described in [27]. Firstly, the data are arranged in a matrix, whose dimensions are N samples × 18, with 18 being the number of measurements used by the model.…”

Section: Data Preprocessing and Models Architecturementioning

confidence: 99%

A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals

et al. 2023

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Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure’s health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge’s health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others’ N−1 measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness.

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Adaptive feature fusion and disturbance correction for accurate remaining useful life prediction of rolling bearings

Guo,

Ma,

et al. 2024

Engineering Applications of Artificial Intelligence

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A deep learning approach for fault detection and RUL estimation in bearings

Cited by 3 publications

References 25 publications

Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN

Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN

A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals

Adaptive feature fusion and disturbance correction for accurate remaining useful life prediction of rolling bearings

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