Transfer learning for remaining useful life prediction of multi-conditions bearings based on bidirectional-GRU network

Measurement ◽  
2021 ◽  
pp. 109287
Author(s):  
Yudong Cao ◽  
Minping Jia ◽  
Peng Ding ◽  
Yifei Ding
Keyword(s):  
Transfer Learning ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hao Zhang ◽  
Qiang Zhang ◽  
Siyu Shao ◽  
Tianlin Niu ◽  
Xinyu Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Life Prediction ◽  
Feature Learning ◽  
High Accuracy ◽  
Remaining Useful Life ◽  
Learning Ability ◽  
Model Parameters ◽  
Deep Model ◽  
Useful Life

Deep learning has a strong feature learning ability, which has proved its effectiveness in fault prediction and remaining useful life prediction of rotatory machine. However, training a deep network from scratch requires a large amount of training data and is time-consuming. In the practical model training process, it is difficult for the deep model to converge when the parameter initialization is inappropriate, which results in poor prediction performance. In this paper, a novel deep learning framework is proposed to predict the remaining useful life of rotatory machine with high accuracy. Firstly, model parameters and feature learning ability of the pretrained model are transferred to the new network by means of transfer learning to achieve reasonable initialization. Then, the specific sensor signals are converted to RGB image as the specific task data to fine-tune the parameters of the high-level network structure. The features extracted from the pretrained network are the input into the Bidirectional Long Short-Term Memory to obtain the RUL prediction results. The ability of LSTM to model sequence signals and the dynamic learning ability of bidirectional propagation to time information contribute to accurate RUL prediction. Finally, the deep model proposed in this paper is tested on the sensor signal dataset of bearing and gearbox. The high accuracy prediction results show the superiority of the transfer learning-based sequential network in RUL prediction.

scholarly journals Remaining Useful Life Prediction from 3D Scan Data with Genetically Optimized Convolutional Neural Networks

2021 ◽  
Author(s):  
Giovanni Diraco ◽  
Pietro Siciliano ◽  
Alessandro Leone
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Support Vector Regression ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Life Prediction ◽  
Point Clouds ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Useful Life

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, Convolutional Neural Network-based Deep Neural Network techniques are investigated for the Remaining Useful Life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pre-trained models, using a classic machine learning approach, i.e., Support Vector Regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE=0.058) to that of transfer learning which, instead, remains at a lower or slightly higher level (MAPE=0.416) than Support Vector Regression (MAPE=0.857).

scholarly journals A Transfer Learning-based Approach to Predict the Shelf life of Fruit

2021 ◽  
Vol 24 (67) ◽  
pp. 102-120
Author(s):  
Varsha Bhole ◽  
Arun Kumar
Keyword(s):  
Shelf Life ◽  
Transfer Learning ◽  
Life Prediction ◽  
Mangifera Indica ◽  
Remaining Useful Life ◽  
Shelf Life Prediction ◽  
Challenging Tasks ◽  
Temperature Parameter ◽  
Useful Life ◽  
Agriculture And Food

Shelf-life prediction for fruits based on the visual inspection and with RGB imaging through external features becomes more pervasive in agriculture and food business. In the proposed architecture, to enhance the accuracy with low computational costs we focus on two challenging tasks of shelf life (remaining useful life) prediction: 1) detecting the intrinsic features like internal defects, bruises, texture, and color of the fruits; and 2) classification of fruits according to their remaining useful life. To accomplish these tasks, we use the thermal imaging technique as a baseline which is used as non-destructive approach to find the intrinsic values of fruits in terms of temperature parameter. Further to improve the classification tasks, we combine it with a transfer learning approach to forecast the shelf life of fruits. For this study, we have chosen „Kesar? (Mangifera Indica Linn cv. Kesar) mangoes and for the purpose of classification, our designed dataset images are categorized into 19 classes viz. RUL-1 (Remaining Useful Life-1) to RUL-18 (Remaining Useful Life-18) and No-Life as after harvesting, the storage span of „Kesar? is near about 19 days. A comparative analysis using SqueezeNet, ShuffleNet, and MobileNetv2 (which are prominent CNN based lightweight models) has been performed in this study. The empirical results show a highest achievable accuracy of 98.15±0.44% with an almost a double speedup in training the entire process by using thermal images.

Deep Transfer Learning Remaining Useful Life Prediction of Different Bearings

2021 ◽  
Author(s):  
Juan Xu ◽  
Mengting Fang ◽  
Weihua Zhao ◽  
Yuqi Fan ◽  
Xu Ding
Keyword(s):  
Transfer Learning ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life
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