Research on Health Management Technology of Oil Production Machine Based on Vibration Signal

2021 ◽  
Author(s):  
Yingshun Li ◽  
Yu Tian ◽  
Xiaojian Yi ◽  
Haiyang Liu
Keyword(s):  
Health Management ◽  
Oil Production ◽  
Vibration Signal ◽  
Management Technology

scholarly journals Research on health management technology of ship cabin equipment

2021 ◽  
Vol 1976 (1) ◽  
pp. 012057
Author(s):  
Jie Zhuo ◽  
Maopu Wu ◽  
Qingxin Sun
Keyword(s):  
Health Management ◽  
Management Technology

scholarly journals Laplace Prior-Based Bayesian Compressive Sensing Using K-SVD for Vibration Signal Transmission and Fault Detection

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 517 ◽  
Author(s):  
Yunfei Ma ◽  
Xisheng Jia ◽  
Qiwei Hu ◽  
Daoming Xu ◽  
Chiming Guo ◽  
...  
Keyword(s):  
Fault Detection ◽  
Compressive Sensing ◽  
Health Management ◽  
Signal Transmission ◽  
Vibration Signal ◽  
Experimental Tests ◽  
Reconstructed Signal ◽  
Signal Simulation ◽  
Value Decomposition ◽  
Laplace Priors

Vibration signal transmission plays a fundamental role in equipment prognostics and health management. However, long-term condition monitoring requires signal compression before transmission because of the high sampling frequency. In this paper, an efficient Bayesian compressive sensing algorithm is proposed. The contribution is explicitly decomposed into two components: a multitask scenario and a Laplace prior-based hierarchical model. This combination makes full use of the sparse promotion under Laplace priors and the correlation between sparse blocks to improve the efficiency. Moreover, a K-singular value decomposition (K-SVD) dictionary learning method is used to find the best sparse representation of the signal. Simulation results show that the Laplace prior-based reconstruction performs better than typical algorithms. The comparison between a fixed dictionary and learning dictionary also illustrates the advantage of the K-SVD method. Finally, a fault detection case of a reconstructed signal is analyzed. The effectiveness of the proposed method is validated by simulation and experimental tests.

scholarly journals Prognostics and health management technology for radar system

2020 ◽  
Vol 309 ◽  
pp. 04009
Author(s):  
Yongle Lyu ◽  
Zhuo Pang ◽  
Chuang Zhou ◽  
Peng Zhao
Keyword(s):  
Model Building ◽  
Health Management ◽  
Radar System ◽  
Prognostics And Health Management ◽  
Failure Model ◽  
Long Distance ◽  
Maintenance System ◽  
Management Technology ◽  
Key Techniques ◽  
Selection Of

Information-based war in the future has a higher requirement to the maintenance and support ability of radar system. Prognostics and Health Management(PHM) technology represents the research hotspot of maintenance system, and following key techniques need to be resolved to research on the radar PHM technology such as the acquirement and selection of health information and fault signs of a radar’s electronical components, mass data warehousing and mining, fusion of multi-source test data and multi-field characteristic information, failure model building and forecasting, automatic decision-making on maintenance, and at the same time improving the self built-in test abilities of radar’s components based on the optimization of Design For Testability(DFT). The radar PHM technology has the trend of “built-in to integrate”, “together with DFT” and “long-distance and distributed”. However, subjected to radar’s complexity and current PHM technique level, radar PHM engineering still meets many challenges, but has bright future.

scholarly journals Study on the Types of Elderly Intelligent Health Management Technology and the Influencing Factors of Its Adoption

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1494
Author(s):  
Zhu Chen ◽  
Huiying Qi ◽  
Luman Wang
Keyword(s):  
Health Services ◽  
Influencing Factors ◽  
Health Risks ◽  
Remote Monitoring ◽  
Social Problem ◽  
Health Management ◽  
The Elderly ◽  
Self Monitoring ◽  
Management Technology ◽  
Use Behavior

[Background]: In recent years, aging has become a global social problem. Intelligent health management technology (IHMT) provides solutions for the elderly to deal with various health risks. However, the elderly are facing many difficulties in using IHMT. Studying the application types of IHMT and the influencing factors of the elderly’s acceptance of it will help to improve the use behavior of the elderly. [Methods]: This paper summarizes the application types of IHMT, identifies the influencing factors of the elderly’s adaption of IHMT, and makes a systematic comment on the influencing factors. [Results]: We divide the different functions of IHMT for the elderly into four types: self-monitoring, medical care, remote monitoring, and health education. The influencing factors are divided into three types: individual, social, and technology. [Conclusions]: This study finds that IHMT’s application covers all aspects of the health services of the elderly. Among these applications, self-monitoring is the most used. We divided the influencing factors of the elderly’s acceptance of IHMT into three categories and nine subcategories, having 25 variables.

scholarly journals Research on DC-DC power health management technology based on degradation test and deep learning

2021 ◽  
Vol 1207 (1) ◽  
pp. 012005
Author(s):  
Linlin Shi ◽  
Pengfei Yu ◽  
Shilie He ◽  
Zhenwei Zhou ◽  
Linghui Meng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Supply ◽  
Health Management ◽  
Fault Prediction ◽  
Health State ◽  
Current Output ◽  
Dc Power ◽  
Management Technology ◽  
Degradation Test ◽  
Dc Power Supply

Abstract The health state of DC-DC power supply is the key factor to determine whether the electronic equipment can operate normally. The failure and deterioration of the power supply system will lead to the collapse of the entire electronic system. The research in this paper is based on the long-term high temperature degradation test data of a certain type of DC-DC power supply. The degradation law of power supply is studied by data preprocessing and noise reduction of sensitive parameters such as input current, output current, input voltage and output voltage. On this basis, we use the method of deep learning to model the efficiency of power supply in the process of degradation test. The experimental results show that the efficiency time series modeling of power supply degradation using LSTM method can effectively reflect the law of power supply efficiency degradation. Based on the DC-DC power health management technology combining degradation test and deep learning, the advanced fault prediction model is used to reflect the change law of power supply in the real degradation process. This method has certain theoretical and engineering value for power PHM modeling and application.

scholarly journals An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1041 ◽  
Author(s):  
Yang Liu ◽  
Lixiang Duan ◽  
Zhuang Yuan ◽  
Ning Wang ◽  
Jianping Zhao
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Vibration Signal ◽  
Intelligent Fault Diagnosis ◽  
Traditional Methods ◽  
Learning Abilities ◽  
Fault Features ◽  
Correlation Criterion ◽  
Long Time ◽  
Diagnosis Method

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

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