Transfer Learning Strategies for Deep Learning-based PHM Algorithms

Yang, Fan; Zhang, Wenjin; Tao, Laifa; Ma, Jian

doi:10.3390/app10072361

Cited by 40 publications

(32 citation statements)

References 16 publications

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“…Genotype data can be treated as sequential data so the information obtained from SNPs in any chromosome can be used for final prediction. Deep Learning methods lend themselves to transfer learning [ 19 ], which facilitates the transfer of knowledge from large datasets to smaller ones. …”

Section: Introductionmentioning

confidence: 99%

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Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Muneeb

Henschel

2021

BMC Bioinformatics

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Background Genotype–phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97%. Conclusion Genotype–phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

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Section: Introductionmentioning

confidence: 99%

“…Deep Learning methods lend themselves to transfer learning [ 19 ], which facilitates the transfer of knowledge from large datasets to smaller ones.…”

Section: Introductionmentioning

confidence: 99%

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Muneeb

Henschel

2021

BMC Bioinformatics

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“…Shao et al [22] transferred the structure and parameters of the VGG16 network trained from the ImageNet dataset for image recognition and used them for fault diagnosis of an induction motor, bearing, and planetary gearbox. Yang et al [23] used a CNN and transfer learning for fault diagnosis of rotating machinery under different working conditions. In addition, Cao et al [24] performed gear fault diagnosis using a deep CNN and transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis

et al. 2021

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Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis.

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“…Data-driven methods attempt to extract features from measured data using machine learning techniques. In recent years, deep learning has emerged as a powerful tool to extract the representative feature from the collected signals [30,31]. Different deep learning architecture, including convolutional neural network (CNN) [29,32], recurrent neural network (RNN) [33], autoencoder [27], and generative adversarial network (GAN) [34], are successfully used to extract features automatically.…”

Section: Introductionmentioning

confidence: 99%

Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

2020

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Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (HI) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the HI, most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the HI. For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the HI. The proposed method was tested on a benchmark bearing dataset and compared with several other traditional HI construction models. Experimental results indicate that the constructed HI exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.

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Transfer Learning Strategies for Deep Learning-based PHM Algorithms

Cited by 40 publications

References 16 publications

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis

Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform

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