A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Zhou, Funa; Hu, Po; Yang, Shuai; Wen, Chenglin

doi:10.3390/s18103521

Cited by 27 publications

(10 citation statements)

References 43 publications

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“…(1) Data set description and description of some parameters The roller bearing data sets we use are all from the public datasets of Case Western Reserve University (CRWU) [37,38]. Figure 7 shows the test platform of CRWU.…”

Section: Case Twomentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer

Wen

2021

Sensors

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Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, this paper proposes a fault diagnosis method based on knowledge transfer in deep learning. First, we describe the data collected during the operation as a two-dimensional image with both time and frequency-domain characteristics. Second, we transform the trained source domain model into a shallow model suitable for small samples in the target domain, and we train the shallow model with small samples with labels. Third, we input a large number of unlabeled samples into the shallow model, and the output result of the system is regarded as the label of the input sample. Fourth, we combine the original data and the data annotated by the shallow model to train the new deep CNN fault diagnosis model so as to realize the migration of knowledge from the expert system to the deep CNN. The newly built deep CNN model is used for the online fault diagnosis of rotating machinery. The FFCNN-SVM shallow model tagger method proposed in this paper compares the fault diagnosis results with other transfer learning methods at this stage, and its correct rate has been greatly improved. This method provides new ideas for future fault diagnosis under small samples.

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Section: Case Twomentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer

Wen

2021

Sensors

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“…When the working environment of the machine changes or the signal contains a lot of noise, the weighting voting has better performance than the majority voting. There are other ensemble methods for integration features, such as the learning method which outputs features to form a new data set, and learning with a new model [42,43].…”

Section: Weighting Strategymentioning

confidence: 99%

A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

Ouyang

Chen

et al. 2020

Sensors

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Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.

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“…In this case, the publicly available roller bearing condition dataset from Case Western Reserve University (CRWU) is analyzed [42,43] . Fig.7 shows the test platform.…”

Section: Case Two: Bearing Fault Diagnosis 1) Dataset Preparation Andmentioning

confidence: 99%

Deep Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples

et al. 2020

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A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Cited by 27 publications

References 43 publications

Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer

Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer

A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

Deep Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples

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