A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

Zhang

Journal of Healthcare Engineering

et al. 2021

In order to study the influence of quantitative magnetic susceptibility mapping (QSM) on them. A 2.5D Attention U-Net Network based on multiple input and multiple output, a method for segmenting RN, SN, and STN regions in high-resolution QSM images is proposed, and deep learning realizes accurate segmentation of deep nuclei in brain QSM images. Experimental results show data first cuts each layer of 0 100 case data, based on the image center, from 384 × 288 to the size of 128 × 128. Image combination: each layer of the image in the layer direction combines with two adjacent images into a 2.5D image, i.e., (It − m It; It + i), where It represents the layer i image. At this time, the size of the image changes from 128 × 128 to 128 × 128 × 3, in which 3 represents three consecutive layers of images. The SNR of SWP I to STN is twice that of SWI. The small deep gray matter nuclei (RN, SN, and STN) in QSM images of the brain and the pancreas with irregular shape and large individual differences in abdominal CT images can be automatically segmented.

“…e basic energy function used in the experiment is Tversky loss, as shown in equation (8), and β � 0.7 is set in this experiment:…”

Section: Experimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Deep Learning-Based QSM Magnetic Resonance Imaging on the Subthalamic Nucleus

Zhang

Journal of Healthcare Engineering

et al. 2021

“…Consequently, the learning model may not yield correct labels for the unlabeled target domain and may diagnose the damage improperly for the target structure. From the domain adaptation perspective, the distribution shift between source and target domain should be addressed (Singh, Azamfar, Ainapure, & Lee, 2020;J. Li, Li, He, & Qu, 2020).…”

Section: Domain Adaptation In Shmmentioning

confidence: 99%

Domain Adaptation for Structural Fault Detection under Model Uncertainty

Ozdagli

Koutsoukos

2021

IJPHM

In the last decade, the interest in machine learning (ML) has grown significantly within the structural health monitoring (SHM) community. Traditional supervised ML approaches for detecting faults assume that the training and test data come from similar distributions. However, real-world applications, where an ML model is trained, for example, on numerical simulation data and tested on experimental data, are deemed to fail in detecting the damage. The deterioration in the prediction performance is mainly related to the fact that the numerical and experimental data are collected under different conditions and they do not share the same underlying features. This paper proposes a domain adaptation approach for ML-based damage detection and localization problems where the classifier has access to the labeled training (source) and unlabeled test (target) data, but the source and target domains are statistically different. The proposed domain adaptation method seeks to form a feature space that is capable of representing both source and target domains by implementing a domain-adversarial neural network. This neural network uses H-divergence criteria to minimize the discrepancy between the source and target domain in a latent feature space. To evaluate the performance, we present two case studies where we design a neural network model for classifying the health condition of a variety of systems. The effectiveness of the domain adaptation is shown by computing the classification accuracy of the unlabeled target data with and without domain adaptation. Furthermore, the performance gain of the domain adaptation over a well-known transfer knowledge approach called Transfer Component Analysis is also demonstrated. Overall, the results demonstrate that the domain adaption is a valid approach for damage detection applications where access to labeled experimental data is limited.

“…Besides, more expressive features can be extracted automatically by deep transfer learning and it utilizes the self-extraction of deep learning together with transfer learning to acquire "new knowledge", which assist to solve small sample data problem well in few-shot learning [11]. This learning method is commonly used for mechanical fault diagnosis [12][13][14][15][16]. For example, Zhao et al [17] reported a transfer learning framework based on the deep multi-scale convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings of Different Working Conditions Based on Multi-Feature Spatial Domain Adaptation

2021

The running state of rolling bearings is complex in operation, and the data are generally collected under different working conditions. However, when single-source domain adaptation is used to model the heterogeneously distributed data obtained under different working conditions, the domaininvariant representations can hardly be used for representation, which directly affects the fault diagnosis rate. To this end, a method for the fault diagnosis of rolling bearings under different working conditions based on multi-feature spatial domain adaptation is proposed. Firstly, all the data from source and target domains are mapped into a feature space to learn the common representations of all domains. Secondly, the data for each pair of source and target domains are mapped into different feature spaces to get the fault feature representations under various working conditions. And the multi-domain adaptation network is used for the domain-specific distribution alignment to learn multiple domain-invariant representations. Thirdly, these representations are used to train multiple domain-specific classifiers, thus obtaining the recognition result for each domain-invariant representation. Finally, the domain-specific decision boundaries predicted by multiple classifiers are employed to align the classifiers' output of target samples and thus to reduce the influence from different classifiers. The effectiveness and feasibility of this proposed method have been verified by diagnostic experiments conducted according to the rolling bearing data from Case Western Reserve University and Laboratory, respectively.