Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

Li, Chuan; Sánchez, René–Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

doi:10.1016/j.neucom.2015.06.008

Cited by 259 publications

(95 citation statements)

References 41 publications

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“…Finally, the health value was used to predict RUL via a similarity-based life prediction algorithm. In [68], a multi-modal deep support vector classification approach was proposed for fault diagnosis of gearboxes. Firstly, three modalities features including time, frequency and time-frequency ones were extracted from vibration signals.…”

Section: B Rbm and Its Variants For Machine Health Monitoringmentioning

confidence: 99%

Deep learning and its applications to machine health monitoring

Zhao

Yan

Chen

et al. 2019

Mechanical Systems and Signal Processing

1,965

813

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Section: B Rbm and Its Variants For Machine Health Monitoringmentioning

confidence: 99%

Deep learning and its applications to machine health monitoring

Zhao

Yan

Chen

et al. 2019

Mechanical Systems and Signal Processing

1,965

813

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“…Compared with the "shallow" models, DL has many hierarchical levels in a hidden layer, that is, the information representation is delivered from lower levels to higher levels, which makes the information representation more abstract and nonlinear for the higher levels. Through representations by the hierarchical levels, the "deeper" feature of multi-parameter manufacturing quality can be fitted by regression models sufficiently [22]. To our best knowledge, there has been little literature that has reported on applications for manufacturing quality prediction using the deep framework.…”

Section: Methodologiesmentioning

confidence: 99%

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

et al. 2017

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Abstract:Under the international background of the transformation and promotion of manufacturing, the Chinese government proposed the "Made in China 2025" strategy, which focused on the improvement of a quality-based innovation ability. Moreover, predicting manufacturing quality is one of the crucial measures for quality management. Accurate prediction is closely related to the feature learning of manufacturing processes. Therefore, two categories of intelligent learning approaches, i.e., shallow learning and deep learning, are investigated and compared for manufacturing quality prediction in this paper. Specifically, the feed forward neural network (FFNN) with one hidden layer and the least squares support vector machine (LSSVM) with no hidden layers are selected as the representatives for shallow learning, and the deep restricted Boltzmann machine (DRBM) and the stack autoencoder (SAE) are chosen as the representatives for deep learning. The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China. The experiments show that the deep framework overwhelms the shallow architecture in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. In addition, the prediction results also indicate that the performances depend on the length of the training data. That is, the bigger the sample size is, the better the performance is.

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“…Deep learning methods [10]- [13], have attracted large amount of attention by promising better performance without the need of hand-craft features. However, it is known that when a trained model is deployed on unseen operating conditions, the performance can deteriorate dramatically because of the operating condition difference, in other words, data distribution difference, between training and testing machines.…”

Section: Related Workmentioning

confidence: 99%

Domain Adaptive Transfer Learning for Fault Diagnosis

Wang

Michau

Fink

2019

2019 Prognostics and System Health Management Conference (PHM-Paris)

113

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Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models from one machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although datadriven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising solution to this problem is to use domain adaptation techniques. It aims to improve model performance on the target new machine. Inspired by its successful implementation in computer vision, we introduced Domain-Adversarial Neural Networks (DANN) to our context, along with two other popular methods existing in previous fault diagnosis research. We then carefully justify the applicability of these methods in realistic fault diagnosis settings, and offer a unified experimental protocol for a fair comparison between domain adaptation methods for fault diagnosis problems.

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Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

Cited by 259 publications

References 41 publications

Deep learning and its applications to machine health monitoring

Deep learning and its applications to machine health monitoring

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Domain Adaptive Transfer Learning for Fault Diagnosis

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