Predictive Battery Health Management with Transfer Learning and Online Model Correction

2021 ◽  
pp. 1-1
Author(s):  
Yunhong Che ◽  
Zhongwei Deng ◽  
Xianke Lin ◽  
Lin Hu ◽  
Xiaosong Hu
Keyword(s):  
Transfer Learning ◽  
Health Management ◽  
Model Correction

scholarly journals Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors

2021 ◽  
Vol 9 (1) ◽  
pp. 47
Author(s):  
Magnus Gribbestad ◽  
Muhammad Umair Hassan ◽  
Ibrahim A. Hameed
Keyword(s):  
Transfer Learning ◽  
Data Transfer ◽  
Health Management ◽  
Correct Diagnosis ◽  
Vital Role ◽  
Research Field ◽  
Remaining Useful Life ◽  
Prognostic Models ◽  
Classification Problems ◽  
Air Compressors

Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. Due to the requirements of system safety and reliability, the correct diagnosis or prognosis of abnormal condition plays a vital role in the maintenance of industrial systems. It is expected that new requirements in regard to autonomous ships will push suppliers of maritime equipment to provide more insight into the conditions of their systems. One of the stated challenges with these systems is having enough run-to-failure examples to build accurate-enough prognostic models. Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. In this paper, various deep learning models are used to predict the remaining useful life (RUL) of air compressors. Here, transfer learning is applied by building a separate prognostics model trained on turbofan engines. It has been found that several of the explored transfer learning architectures were able to improve the predictions on air compressors. The research results suggest transfer learning as a promising research field towards more accurate and reliable prognostics.

scholarly journals Transfer Learning Strategies for Deep Learning-based PHM Algorithms

2020 ◽  
Vol 10 (7) ◽  
pp. 2361
Author(s):  
Fan Yang ◽  
Wenjin Zhang ◽  
Laifa Tao ◽  
Jian Ma
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Learning ◽  
High Speed ◽  
Health Management ◽  
Learning Algorithm ◽  
Learning Technology ◽  
Deep Learning Algorithm ◽  
Transfer Strategies

As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy.

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

2021 ◽  
Vol 11 (5) ◽  
pp. 2370
Author(s):  
Kihoon Lee ◽  
Soonyoung Han ◽  
Van Huan Pham ◽  
Seungyon Cho ◽  
Hae-Jin Choi ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Large Scale ◽  
Health Management ◽  
Industrial Robot ◽  
Target Domain ◽  
Learning Network ◽  
Multi Objective ◽  
Instance Weighting

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.

Körperliche Beschwerden bei älteren Migranten in Deutschland 1Erstellt unter Mitwirkung der Arbeitsgruppe «Abuse of the Elderly in the European Region» (ABUEL): Joaquim F. J. Soares, Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden; Francisco Torres-González, Departmental Section of Psychiatry and Psychological Medicine, University of Granada, Spain; Elisabeth Ioannidi-Kapolou, National School of Public Health, Department of Sociology, Athens, Greece; Giovanni Lamura, Italian National Research Centre on Ageing Department of Gerontological Research, Unit of Health Economics, Ancona, Italy; Mindaugas Stankunas, Kaunas University of Medicine, Department of Health Management, A. Mickeviciaus, Kaunas, Lithuania; Henrique Barros, Department of Hygiene and Epidemiology, University of Porto Medical School, Porto, Portugal.

2010 ◽  
Vol 58 (3) ◽  
pp. 199-206 ◽  
Author(s):  
Rosina-Martha Csöff ◽  
Gloria Macassa ◽  
Jutta Lindert
Keyword(s):  
Public Health ◽  
Health Management ◽  
The Elderly ◽  
Health Department ◽  
Research Unit ◽  
Research Centre ◽  
Public Health Department ◽  
Karolinska Institute ◽  
Psychological Medicine ◽  
Körperliche Beschwerden

Körperliche Beschwerden sind bei Älteren weit verbreitet; diese sind bei Migranten bislang in Deutschland und international noch wenig untersucht. Unsere multizentrische Querschnittstudie erfasste körperliche Beschwerden bei Menschen im Alter zwischen 60 und 84 Jahren mit Wohnsitz in Stuttgart anhand der Kurzversion des Gießener Beschwerdebogens (GBB-24). In Deutschland wurden 648 Personen untersucht, davon 13.4 % (n = 87) nicht in Deutschland geborene. Die Geschlechterverteilung war bei Migranten und Nichtmigranten gleich; der sozioökonomische Status lag bei den Migranten etwas niedriger: 8.0 % (n = 7) der Migranten und 2.5 % (n = 14) der Nichtmigranten verfügten über höchstens vier Jahre Schulbildung; 12.6 % (n = 11) der Migranten und 8.2 % (n = 46) der Nichtmigranten hatten ein monatliches Haushaltsnettoeinkommen von unter 1000€; 26.4 % der Migranten und 38.1 % (n = 214) der Nichtmigranten verfügten über mehr als 2000€ monatlich. Somatische Beschwerden lagen bei den Migranten bei 65.5 % (n = 57) und bei den Nichtmigranten bei 55.8 % (n = 313). Frauen wiesen häufiger somatische Beschwerden auf (61.8 %) als Männer (51.8 %). Mit steigendem Alter nahmen somatische Beschwerden zu. Mit Ausnahme der Altersgruppe der 70–74-Jährigen konnte kein signifikanter Unterschied zwischen Migranten und Nichtmigranten hinsichtlich der Häufigkeit körperlicher Beschwerden gezeigt werden. Ausblick: Es werden dringend bevölkerungsrepräsentative Studien zu körperlichen Beschwerden bei Migranten benötigt.

Disease-Specific Stressors Among Youths With HIV/AIDS: Toward Tailored Health Management

2006 ◽  
Author(s):  
Lisa A. Orban ◽  
Renee Stein ◽  
Linda J. Koenig ◽  
Erika L. Rexhouse ◽  
Ricardo D. Lagrange ◽  
...  
Keyword(s):  
Health Management ◽  
Disease Specific ◽  
Hiv Aids

On-farm udder health monitoring

2011 ◽  
Vol 39 (02) ◽  
pp. 95-100
Author(s):  
J. C. van Veersen ◽  
O. Sampimon ◽  
R. G. Olde Riekerink ◽  
T. J. G. Lam
Keyword(s):  
Management Practices ◽  
Health Management ◽  
Action Plan ◽  
Real Life ◽  
Health Data ◽  
Control Points ◽  
Udder Health ◽  
Critical Control Points ◽  
On Farm ◽  
Clinical Problems

SummaryIn this article an on-farm monitoring approach on udder health is presented. Monitoring of udder health consists of regular collection and analysis of data and of the regular evaluation of management practices. The ultimate goal is to manage critical control points in udder health management, such as hygiene, body condition, teat ends and treatments, in such a way that results (udder health parameters) are always optimal. Mastitis, however, is a multifactorial disease, and in real life it is not possible to fully prevent all mastitis problems. Therefore udder health data are also monitored with the goal to pick up deviations before they lead to (clinical) problems. By quantifying udder health data and management, a farm is approached as a business, with much attention for efficiency, thought over processes, clear agreements and goals, and including evaluation of processes and results. The whole approach starts with setting SMART (Specific, Measurable, Acceptable, Realistic, Time-bound) goals, followed by an action plan to realize these goals.

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