scholarly journals A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6139 ◽  
Author(s):  
Hongchao Cheng ◽  
Yiqi Liu ◽  
Daoping Huang ◽  
Chong Xu ◽  
Jing Wu
Keyword(s):  
Transfer Learning ◽  
Process Monitoring ◽  
Large Scale ◽  
Bayesian Learning ◽  
Relevance Vector Machine ◽  
Operating Conditions ◽  
Training Data ◽  
Stable Operation ◽  
Wastewater Treatment Process ◽  
Learning Machine

Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible.

Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation

2021 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Yan Liu ◽  
Bin Guo ◽  
Daqing Zhang ◽  
Djamal Zeghlache ◽  
Jingmin Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Cold Start ◽  
Weighting Scheme ◽  
Training Data ◽  
Chain Store ◽  
Useful Knowledge ◽  
Adversarial Network ◽  
Brick And Mortar ◽  
New City ◽  
Learning Machine

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

scholarly journals Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

2018 ◽  
Vol 8 (12) ◽  
pp. 2416 ◽  
Author(s):  
Ansi Zhang ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Yuxin Cui ◽  
Zhonghao Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recurrent Neural Networks ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Good Prediction ◽  
Useful Life

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

Towards Generalization of Intelligent Fault Detection for Roller Element Bearings via Distinct Dataset Transfer Learning

2021 ◽  
Author(s):  
Justin Larocque-Villiers ◽  
Patrick Dumond
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Training Data ◽  
Fault Classification ◽  
Target Domain ◽  
Bearing Faults

Abstract Through the intelligent classification of bearing faults, predictive maintenance provides for the possibility of service schedule, inventory, maintenance, and safety optimization. However, real-world rotating machinery undergo a variety of operating conditions, fault conditions, and noise. Due to these factors, it is often required that a fault detection algorithm perform accurately even on data outside its trained domain. Although open-source datasets offer an incredible opportunity to advance the performance of predictive maintenance technology and methods, more research is required to develop algorithms capable of generalized intelligent fault detection across domains and discrepancies. In this study, current benchmarks on source–target domain discrepancy challenges are reviewed using the Case Western Reserve University (CWRU) and the Paderborn University (PbU) datasets. A convolutional neural network (CNN) architecture and data augmentation technique more suitable for generalization tasks is proposed and tested against existing benchmarks on the Pb U dataset by training on artificial faults and testing on real faults. The proposed method improves fault classification by 13.35%, with less than half the standard deviation of the compared benchmark. Transfer learning is then used to leverage the larger PbU dataset in order to make predictions on the CWRU dataset under a challenging source-target domain discrepancy in which there is minimal training data to adequately represent unseen bearing faults. The transfer learning-based CNN is found to be capable of generalizing across two open-source datasets, resulting in an improvement in accuracy from 53.1% to 68.3%.

scholarly journals Transfer Learning for Inference of Metastatic Origin from Whole Slide Histology

2021 ◽  
Author(s):  
Geoffrey F. Schau ◽  
Hassan Ghani ◽  
Erik A. Burlingame ◽  
Guillaume Thibault ◽  
Joe W. Gray ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Primary Tumor ◽  
Large Scale ◽  
Metastatic Cancer ◽  
Clinical Diagnostics ◽  
Training Data ◽  
Fine Tuning ◽  
Learning Approach ◽  
Whole Slide Images

AbstractAccurate diagnosis of metastatic cancer is essential for prescribing optimal control strategies to halt further spread of metastasizing disease. While pathological inspection aided by immunohistochemistry staining provides a valuable gold standard for clinical diagnostics, deep learning methods have emerged as powerful tools for identifying clinically relevant features of whole slide histology relevant to a tumor’s metastatic origin. Although deep learning models require significant training data to learn effectively, transfer learning paradigms provide mechanisms to circumvent limited training data by first training a model on related data prior to fine-tuning on smaller data sets of interest. In this work we propose a transfer learning approach that trains a convolutional neural network to infer the metastatic origin of tumor tissue from whole slide images of hematoxylin and eosin (H&E) stained tissue sections and illustrate the advantages of pre-training network on whole slide images of primary tumor morphology. We further characterize statistical dissimilarity between primary and metastatic tumors of various indications on patch-level images to highlight limitations of our indication-specific transfer learning approach. Using a primary-to-metastatic transfer learning approach, we achieved mean class-specific areas under receiver operator characteristics curve (AUROC) of 0.779, which outperformed comparable models trained on only images of primary tumor (mean AUROC of 0.691) or trained on only images of metastatic tumor (mean AUROC of 0.675), supporting the use of large scale primary tumor imaging data in developing computer vision models to characterize metastatic origin of tumor lesions.

scholarly journals CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds

2022 ◽  
Vol 3 ◽  
Author(s):  
Yi Chang ◽  
Xin Jing ◽  
Zhao Ren ◽  
Björn W. Schuller
Keyword(s):  
Transfer Learning ◽  
Large Scale ◽  
Low Cost ◽  
Characteristic Curve ◽  
Training Data ◽  
Small Scale ◽  
Respiratory Sounds ◽  
Learning Framework ◽  
Robust Model ◽  
Cough Sound

Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR).

scholarly journals Evaluation of Flow-Induced Dynamic Stress and Vibration of Volute Casing for a Large-Scale Double-Suction Centrifugal Pump

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Fu-Jun Wang ◽  
Li-Xia Qu ◽  
Ling-Yan He ◽  
Jiang-Yong Gao
Keyword(s):  
Centrifugal Pump ◽  
Flow Rate ◽  
Large Scale ◽  
Maximum Stress ◽  
Dynamic Stress ◽  
Operating Conditions ◽  
Stable Operation ◽  
Vibration Displacement ◽  
Structural Dynamic ◽  
Suction Pump

The transient analysis was carried out to investigate the dynamic stress and vibration of volute casing for a large double-suction centrifugal pump by using the transient fluid-structure interaction theory. The flow pulsations at flow rate ranging from 60% to 100% of the nominal flow rate (Qd) were taken as the boundary conditions for FEM analysis of the pump volute casing structure. The results revealed that, for all operating conditions, the maximum stress located at the volute tongue region, whereas the maximum vibration displacement happened close to the shaft hole region. It was also found that the blade passing frequency and its harmonics were dominant in the variations of dynamic stress and vibration displacement. The amplitude of the dominant frequency for the maximum stress detected at 0.6Qdwas 1.14 times that atQd, lower than the related difference observed for pressure fluctuations (3.23 times). This study provides an effective method to quantify the flow-induced structural dynamic characteristics for a large-scale double-suction pump. It can be used to direct the hydraulic and structural design and stable operation, as well as fatigue life prediction for large-scale pumps.

Road segmentation of cross-modal remote sensing images using deep segmentation network and transfer learning

2019 ◽  
Vol 46 (3) ◽  
pp. 384-390 ◽  
Author(s):  
Hao He ◽  
Dongfang Yang ◽  
Shicheng Wang ◽  
Shuyang Wang ◽  
Xing Liu
Keyword(s):  
Remote Sensing ◽  
Transfer Learning ◽  
Large Scale ◽  
Training Data ◽  
Data Conversion ◽  
Remote Sensing Images ◽  
Content Type ◽  
Modal Data ◽  
The Cross ◽  
Road Segmentation

Purpose The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images. Design/methodology/approach First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction. Findings The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics. Originality/value The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.

scholarly journals On the application of transfer learning in prognostics and health management

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Ramin Moradi ◽  
Katrina Groth
Keyword(s):  
Transfer Learning ◽  
Data Storage ◽  
Health Management ◽  
Operating Conditions ◽  
Data Availability ◽  
Training Data ◽  
Fine Tuning ◽  
Prognostics And Health Management ◽  
Big Data Storage ◽  
Previous Training

Advancements in sensing and computing technologies, the development of human and computer interaction frameworks, big data storage capabilities, and the emergence of cloud storage and could computing have resulted in an abundance of data in modern industry. This data availability has encouraged researchers and industry practitioners to rely on data-based machine learning, specially deep learning, models for fault diagnostics and prognostics more than ever. These models provide unique advantages, however their performance is heavily dependent on the training data and how well that data represents the test data. This issue mandates fine-tuning and even training the models from scratch when there is a slight change in operating conditions or equipment. Transfer learning is an approach that can remedy this issue by keeping portions of what is learned from previous training and transferring them to the new application. In this paper, a unified definition for transfer learning and its different types is provided, Prognostics and Health Management (PHM) studies that have used transfer learning are reviewed in detail, and finally a discussion on TL application considerations and gaps is provided for improving the applicability of transfer learning in PHM.

scholarly journals Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

2020 ◽  
Author(s):  
Iason Katsamenis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Anastasios Doulamis ◽  
Nikolaos Doulamis
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
Large Scale ◽  
Training Data ◽  
Classification Models ◽  
Automated Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.

New online kernel method with the Tabu search algorithm for process monitoring

2018 ◽  
Vol 41 (10) ◽  
pp. 2687-2698 ◽  
Author(s):  
Hajer Lahdhiri ◽  
Khaoula Ben Abdellafou ◽  
Okba Taouali ◽  
Majdi Mansouri ◽  
Ouajdi Korbaa
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Reference Model ◽  
Kernel Method ◽  
Search Algorithm ◽  
Synthetic Data ◽  
Operating Conditions ◽  
Training Data ◽  
Adaptive Model ◽  
Tennessee Eastman Process

Process monitoring is an integral part of chemical process, required higher product quality and safety operation. Therefore, the objective of this paper is to ensure the suitable functioning and to improve the fault detection performance of conventional kernel Principal Components Analysis (KPCA). Thus, an online Reduced Rank KPCA (OnRR-KPCA) with adaptive model has been developed to monitor a dynamic nonlinear process. The developed method is proposed. Firstly, to extract the useful observations, from large amount of training data registered in normal operating conditions, in order to construct the reduced reference model. Secondly, to monitor the process online and update the reference model if a new useful observation is available and satisfies the condition of independencies between variables in feature space. To demonstrate the effectiveness of the OnRR-KPCA with adaptive model over the conventional KPCA and the RR-KPCA, the fault detection performances are illustrated through two examples: one using synthetic data, the second using a simulated Tennessee Eastman Process (TEP) data.

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