2022 Prognostics and Health Management Conference (PHM-2022 London)最新文献_第5页

CBR-Based Decision Support System for Maintenance Text Using NLP for an Aviation Case Study 基于cbr的航空维修文本NLP决策支持系统

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00067

Syed Meesam Raza Naqvi, Mohammad Ghufran, Safa Meraghni, C. Varnier, J. Nicod, N. Zerhouni

{"title":"CBR-Based Decision Support System for Maintenance Text Using NLP for an Aviation Case Study","authors":"Syed Meesam Raza Naqvi, Mohammad Ghufran, Safa Meraghni, C. Varnier, J. Nicod, N. Zerhouni","doi":"10.1109/PHM2022-London52454.2022.00067","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00067","url":null,"abstract":"Recently, Prognostics and Health Management (PHM) has emerged to promote predictive maintenance as a methodological key to overcome the limitations of traditional reliability analysis. The Natural Language Processing (NLP) methods allow the maintenance log usage for maintenance diagnostics and decision making. The Maintenance Work Orders (MWOs) contain vital health indicators and decades of experience related to various maintenance actions. However, due to the unstructured nature of maintenance text, it is not common to develop a tool using these textual maintenance entries. This paper proposes a textual Case-Based Reasoning (CBR) approach combined with Technical Language Processing (TLP) to find solutions for new problems based on previous experiences. The Bidirectional Encoder Representations from Transformers (BERT) model is adopted for maintenance data using unsupervised finetuning technique Transformer-based Sequential Denoising AutoEncoder (TSDAE) for aviation case study. Results show that the pre-trained BERT model can adopt domain-specific data and produce semantic matches with only a small amount (1000 samples) of domain specific data.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Remaining Useful Life Prediction of IGBT Module Based on Particle Filter Combining with Particle Swarm Optimization 基于粒子滤波和粒子群优化的IGBT模块剩余使用寿命预测

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00031

Maogong Jiang, Qianqian Lv, Peilei Li, Hantian Gu, Chongyang Gu, Wei Zhang, Guicui Fu

引用次数: 1

Research on Diagnostic Reasoning of Cloud Data Center Based on Bayesian Network and Knowledge Graph 基于贝叶斯网络和知识图的云数据中心诊断推理研究

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00056

Chao Lou, Wang Luo, Dequan Gao, Z. Zhao, Fenggang Lai, Shengya Han, Chao Ma

{"title":"Research on Diagnostic Reasoning of Cloud Data Center Based on Bayesian Network and Knowledge Graph","authors":"Chao Lou, Wang Luo, Dequan Gao, Z. Zhao, Fenggang Lai, Shengya Han, Chao Ma","doi":"10.1109/PHM2022-London52454.2022.00056","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00056","url":null,"abstract":"Cloud Data Center (CDC) has the characteristics of multi-level and multi-domain complex system relations. It is difficult to analyze the alarm information manually to obtain the fault devices and fault cause. In this paper, a knowledge graph is used to track the dynamic changes of CDC topology, and Bayesian Network (BN) diagnosis model with probability attribute is dynamically generated through graph search. Firstly, based on the dynamic topology of CDC tracked in the KG, and the collected fault symptoms from the server log, the graph search is carried out to construct the BN topology, which contains possible fault devices, fault modes and causes. Then with the proposed Causality Strength and Leakage Probability, which could be stored in the KG database, the Condition Probability Table is calculated. Combined with the a priori probability, the Bayesian Network model is established. Finally, the fault cause with the largest a posteriori probability is obtained through the calculation of BN. If the fault cannot be solved by eliminating this cause, reason again with the rest causes. During the maintenance process, constantly update the fault symptoms and causes to make the BN model more accurate. Two fault diagnosis cases show that this method is of great significance to the operation and maintenance of CDC.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133131127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault prediction of fire control system based on Grey rough set and BP neural network 基于灰色粗糙集和BP神经网络的火控系统故障预测

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00009

Baoqi Xie, Yingshun Li, Haiyang Liu, Xing-dang Kang, Yang Zhang

引用次数: 0

Condition Monitoring of Wind Turbine Main Bearing Using SCADA Data and Informed by the Principle of Energy Conservation 基于SCADA数据和节能原则的风电主轴承状态监测

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00055

Adaiton Moreira De Oliveira-Filho, Philippe Cambron, Antoine Tahan

引用次数: 1

Degradation State Assessment Modeling Using Causality Discovery 基于因果关系发现的退化状态评估模型

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00102

Chen Feng, Xiaochen Liu, Shulei Bi, Jian Kang

{"title":"Degradation State Assessment Modeling Using Causality Discovery","authors":"Chen Feng, Xiaochen Liu, Shulei Bi, Jian Kang","doi":"10.1109/PHM2022-London52454.2022.00102","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00102","url":null,"abstract":"In order to solve the problem of equipment degradation state assessment, one idea was to use data-driven method to build equipment health state model and evaluate equipment degradation based on residual. However, most current data-driven models revealed the correlation between condition monitoring variables and equipment state rather than the causal relationship, so the rationality of the model construction lacked explanation. Therefore, causality discovery algorithm was introduced in this work to find variables that were causally related to degradation state to build a state model and improve the interpretability of the model. In this paper, the COmbined Diesel eLectric And Gas (CODLAG) Propulsion system degradation dataset was used for experiments. The Fast Causal Inference (FCI) algorithm was used to discover the causal relationships among the variables, as shown in the causal graph. Based on the causal graph, 4 groups of variables were selected to train the Long Short Term Memory (LSTM) neural networks as models to assess the degradation state. The experimental results showed that those variables that had strong causal relationships with the equipment state were sufficient for the training of the model. And the trained LSTM neural network had good performance for the degradation state assessment. More importantly, the model trained by this way had better interpretability.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134565596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Aircraft sensor fault detection based on SLD-LMS algorithm 基于SLD-LMS算法的飞机传感器故障检测

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00051

Ting Ma, Sensen Zhu, Zihang Ge, Fangyi Wan, Chunlin Zhang, Guanghui Liu

{"title":"Aircraft sensor fault detection based on SLD-LMS algorithm","authors":"Ting Ma, Sensen Zhu, Zihang Ge, Fangyi Wan, Chunlin Zhang, Guanghui Liu","doi":"10.1109/PHM2022-London52454.2022.00051","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00051","url":null,"abstract":"In the aviation field, people have always paid great attention to flight safety. Various sensors are often placed on the aircraft to detect the structural health of the aircraft, so as to ensure the safe life of the aircraft and reduce the occurrence of safety accidents. Along with the rapid development of sensor technology, sensor networks with sensing ability, computing ability and wireless communication ability are developing rapidly, and the advantages of wireless sensor networks in aviation monitoring are becoming more and more obvious. However, there may be malicious attack nodes in actual wireless sensor networks. It tampers with its own observation data to interfere with or attack the whole network. When wireless sensor networks are in an insecure environment, it will affect information transmission and parameter estimation. On this basis, this paper proposes a distributed diffusion least mean square algorithm based on single channel communication to detect and eliminate Byzantine attacks on special nodes. Through MATLAB simulation, the proposed algorithm has high feasibility, reduces the traffic and can get good parameter estimation.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125584771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bidirectional Attention LSTM Networks for Non-instructive Load Monitoring 用于非指导性负荷监测的双向关注LSTM网络

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00076

Yuwei Fan, Chao Liu, Tengbo Guo, D. Jiang

{"title":"Bidirectional Attention LSTM Networks for Non-instructive Load Monitoring","authors":"Yuwei Fan, Chao Liu, Tengbo Guo, D. Jiang","doi":"10.1109/PHM2022-London52454.2022.00076","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00076","url":null,"abstract":"Non-instructive load monitoring (NILM) is a data processing method that decomposes the total energy consumption and estimates the power of individual electrical appliances. The application of NILM can provide additional information for optimal control strategy of smart grid, to achieve the purpose of saving energy by fine management. However, the accuracy of traditional NILM methods doesn’t have high accuracy of decomposed power value. In this work, we apply long short-term memory (LSTM) and achieve good accuracy by enhancing the LSTM model with bidirectional and attention mechanisms, as well as kernel density estimation. The model first normalizes the total energy consumption and converts the normalized data to time series of fixed length. LSTM extracts features from the time series, with the bidirectional mechanism to operate from both normal and reverse order and the attention mechanism to calculate the attention weights of different time steps. Besides, kernel density estimation is used to fit the training data and modify the output of the deep learning model, which upgrades the disaggregation accuracy. The proposed model is tested on UK-dale dataset.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Diesel Engine Assembly Quality Detection Method Based on Cross-point Frequency Response and Static and Dynamic Information Fusion 基于交叉点频响和动静信息融合的柴油机装配质量检测方法

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00018

Xinwei Wang, Hongxia Pan, Heng Zhang, Xu An

{"title":"A Diesel Engine Assembly Quality Detection Method Based on Cross-point Frequency Response and Static and Dynamic Information Fusion","authors":"Xinwei Wang, Hongxia Pan, Heng Zhang, Xu An","doi":"10.1109/PHM2022-London52454.2022.00018","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00018","url":null,"abstract":"For the problem that the early fault information of diesel engine system is weak and difficult to identify and diagnose, an early fault diagnosis method based on cross-point frequency response and static and dynamic information fusion was proposed for the assembly quality of diesel engine system. The dynamic vibration response signal and static cross-point frequency response signal of the diesel engine system were collected by reasonable layout of measuring points. After CEEMD reconstruction and de-noising, the sample entropy and approximate entropy were extracted as characteristic parameters of the dynamic signal, and the frequency response features were extracted from the static signal. The static and dynamic information of the two kinds of information was integrated by PCA. The optimized support vector machine is used to identify the dynamic information and the static and dynamic fusion information respectively. The results show that this method can effectively detect the assembly quality of key components of diesel engine system, and the accuracy of diagnosis is up to 95%, and the recognition rate after static and dynamic information fusion is better than that of dynamic information. The method presented in this paper has a good application prospect in the assembly quality inspection and early fault diagnosis of diesel engine system.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124619620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Calculation of Extreme Condition of Telescopic Boom of Drilling Jumbo Based on New Biogeography-Based Optimization Algorithm 基于新生物地理学优化算法的钻井大车伸缩臂极限工况计算

2022 Prognostics and Health Management Conference (PHM-2022 London) Pub Date : 2022-05-01 DOI: 10.1109/PHM2022-London52454.2022.00016

Yancheng Lv, Lin Lin, Jie Liu, Hao Guo, Chang-sheng Tong, Zhiquan Cui

{"title":"The Calculation of Extreme Condition of Telescopic Boom of Drilling Jumbo Based on New Biogeography-Based Optimization Algorithm","authors":"Yancheng Lv, Lin Lin, Jie Liu, Hao Guo, Chang-sheng Tong, Zhiquan Cui","doi":"10.1109/PHM2022-London52454.2022.00016","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00016","url":null,"abstract":"As a key part of the boom structure of drilling jumbo, the structural stability of the telescopic boom plays a decisive role in the operational reliability of the drilling jumbo. However, the extreme condition of the telescopic boom in the existing optimization cases is determined according to the experience of designers, and there is a lack of research on the extreme condition of the telescopic boom. Given the above problem, the calculation model of the load at the top of the telescopic boom is constructed, and the Biogeography-Based Optimization (BBO) algorithm is used to optimize the pose parameters of the boom structure with the maximum optimization objective of the calculation results of the model. To solve the problem of insufficient adaptability of the linear migration model, 12 nonlinear migration models are proposed and combined with the original BBO algorithm. The performance tests of various migration models are carried out by calculating the limit value of the load at the top of the telescopic boom, the results show that the overall performance and stability of the BBO algorithm based on the exponential migration model is better than other classic optimization algorithms and BBO algorithms based on other migration models. The exponential migration model can better adapt to the nonlinear migration problem, and the corresponding BBO algorithm has better optimization ability.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122882042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0