2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)最新文献_第2页

An Online Wear State Identification Method for Axial Piston Pump Key Friction Pair based on FSANN 基于FSANN的轴向柱塞泵关键摩擦副磨损状态在线识别方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058342

Dandan Wang, Shihao Liu, Weidi Huang, Jun-hui Zhang, Bing Xu

{"title":"An Online Wear State Identification Method for Axial Piston Pump Key Friction Pair based on FSANN","authors":"Dandan Wang, Shihao Liu, Weidi Huang, Jun-hui Zhang, Bing Xu","doi":"10.1109/ICSMD57530.2022.10058342","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058342","url":null,"abstract":"Wear state identification of the axial piston pump is of great importance to secure the modern hydraulic system. Offline intelligent fault diagnosis methods are significant to solve the wear state identification problems. Nevertheless, these methods cannot meet the demand of real-time wear state identification. In this paper, an accurate and online wear state identification method based on edge computing using feature selected artificial neural network (FSANN) is proposed for the axial piston pump key friction pair. To reduce latency, an edge end node including data collection, signal pre-processing, feature extraction, fault classification is established. To cut down the amount of calculation and transmission while retaining accuracy, features sensitive to the fault are selected. The embedding performance of one-against-all support vector machine (OAA-SVM), artificial neural network (ANN), deep belief network (DBN) is compared and ANN is chosen as the embedded diagnostic model. The experimentally verified accuracy of this method is 99.0%. The single diagnosis time(SDT) is about 0.24s. Compared to transmitting raw data to the host computer, this method cut down the amount of data by about 200 times. The proposed method could diagnose the slipper wear state accurately and quickly and provide a potential way for real-time fault diagnosis for axial piston pump.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"103 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116113712","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

Data-Driven VICAR Modeling of Nonstationary Planetary Gearbox Vibration 非平稳行星齿轮箱振动的数据驱动VICAR建模

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058256

Yuejian Chen, Gang Niu

引用次数: 0

Remaining Useful Life Prediction Based on Transformer with A Tiny Representation Network 基于微小表示网络的变压器剩余使用寿命预测

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058279

G. Wang, Dongdong Liu, Lingli Cui

{"title":"Remaining Useful Life Prediction Based on Transformer with A Tiny Representation Network","authors":"G. Wang, Dongdong Liu, Lingli Cui","doi":"10.1109/ICSMD57530.2022.10058279","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058279","url":null,"abstract":"Remaining useful life (RUL) prediction is of great significance to the prognostic and health management of rolling bearings. The effectiveness of the typical RUL prediction relies on the constructed health indicator (HI) which only represents limited degradation information. In addition, rolling bearing degradation is a long-term process, while existing RUL prediction models show a limited ability to learn a long-distance dependency. To fill the above research gap, we propose a novel RUL prediction Transformer (RPT) which consists of a tiny convolution-based representation network (RN) and an advanced Transformer feature extractor. In the proposed RPT, the row vibration signals are concisely and efficiently embedded into a tiny feature space by the RN. Then, embedded vectors of historical run-to-failure data are input into the transformer feature extractor to learn potential prediction knowledge. Due to the global attention machine, the RPT can learn long-distance dependency, which significantly improves the RUL prediction. Compared with state-of-the-art models, RPT attains more accurate RUL prediction.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122524333","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

Use the Direction of the Combined Vector of Accelerometers to Classify Daily Activities 使用加速度计组合矢量的方向对日常活动进行分类

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058213

Zhouyang Wang, L. Mo

引用次数: 0

Source-free Unsupervised Domain Adaptation for Privacy-Preserving Intelligent Fault Diagnosis 面向隐私保护的无源无监督域自适应智能故障诊断

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058437

Mengliang Zhu, Xiangyu Zeng, Jie Liu, Kaibo Zhou

{"title":"Source-free Unsupervised Domain Adaptation for Privacy-Preserving Intelligent Fault Diagnosis","authors":"Mengliang Zhu, Xiangyu Zeng, Jie Liu, Kaibo Zhou","doi":"10.1109/ICSMD57530.2022.10058437","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058437","url":null,"abstract":"Rolling bearing is of vital significance in industrial applications and Intelligent fault diagnosis (IFD) have been widely exploited in this field. However, cross-machine variations hinder the model performance across different machines in varying working conditions. To solve this issue, many unsupervised domain adaptation (UDA) approaches have been proposed recently, requiring direct access to the fully labeled source domain. However, privacy concerns are raised in traditional UDA scenario, since raw signals contain various private information. To address this issue, the source-free unsupervised domain adaptation (SFUDA) scenario is considered, where only the pre-trained source model is required, instead of the fully labeled source domain. In this paper, a SFUDA approach for intelligent fault diagnosis (IFD) is proposed. It consists of two steps: 1) source model generalization, where virtual adversarial training, R-Drop and label smoothing techniques are adopted to improve the generalization ability of the source model; and 2) target model adaptation, where information maximization is used for cluster assumption and mean teacher training paradigm is utilized to alleviate the catastrophic forgetting phenomenon. The proposed approach is verified on the Case Western Reserve University (CWRU) dataset. Experimental results on the show that the proposed SFUDA approach outperforms several typical UDA approaches, and its effectiveness is verified.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114684989","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: 2

Pathological Voice Classification Using Multiresolution Time Series Classification Network 基于多分辨率时间序列分类网络的病理语音分类

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058311

Denghuang Zhao, Xincheng Zhu, Jinyang Qian, Xiaojun Zhang, Yi-Shen Xu, Zhi Tao

{"title":"Pathological Voice Classification Using Multiresolution Time Series Classification Network","authors":"Denghuang Zhao, Xincheng Zhu, Jinyang Qian, Xiaojun Zhang, Yi-Shen Xu, Zhi Tao","doi":"10.1109/ICSMD57530.2022.10058311","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058311","url":null,"abstract":"The detection of pathological voices has achieved good results in recent years. However, due to the complexity of pathological voice, traditional feature based methods are not effective to further classify different voice disease types. In recent years, deep learning methods have shown excellent performance in deep feature extraction and classification of time series. In this paper, we propose a multiresolution time series classification network based on 1-D and 2-D dilated convolutional neural networks to perform the pathological voice multi-classification task. In our method, we used the combination of raw voice, glottal wave signal and the first order difference of glottal wave as the multivariate input of the network. The dilated convolutional layers with different dilation rates were designed to capture features from different scales of voice signals. We trained our network in the MEEI, SVD and HUPA databases and collected voices with a voice recorder to test the network's effect. An improvement of 17% in distinguishing healthy voices, neuromuscular disorders and structural disorders was obtained. The experimental result shows that the structure we proposed can significantly improve the performance of multi-classification task of voices.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"1086 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115873248","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

Only-Current-Signal-Driven Working Condition Identification Method for Induction Motor 感应电动机纯电流信号驱动工况识别方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058399

Yunfei Ling, Zhiliang Liu, Chuan Xie, Minzjian Zuo

{"title":"Only-Current-Signal-Driven Working Condition Identification Method for Induction Motor","authors":"Yunfei Ling, Zhiliang Liu, Chuan Xie, Minzjian Zuo","doi":"10.1109/ICSMD57530.2022.10058399","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058399","url":null,"abstract":"Condition identification is the basis of induction motor condition monitoring. However, existing non-invasive methods have different degrees of problems in accuracy, robustness and generalization. To solve this challenge, this paper proposes a new method to identify induction motor working conditions, including speed and load torque. This method is a physical-empirical hybrid model method, which combines the advantages of a clear mechanism of the physical model method and easy implementation of the empirical model method. The proposed method introduces the multi-dimensional empirical information contained in the stator current, and the fitting function adopted has a solid physical basis, so the proposed method has the innate advantages of high identification accuracy and strong robustness. The experimental results show that the working conditions identified by the proposed method are in good agreement with the real values. Moreover, by comparing with other existing condition identification methods, the advantages of the proposed method in condition identification accuracy are further verified.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132122344","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

An On-orbit Calculation Method of Imaging Parameters of Medium Elliptical Orbit Satellites 中椭圆轨道卫星成像参数在轨计算方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058264

Zeying Dong, Longfei Tian, Guohua Liu, Yang Liu, Huiyuan Wang, Dakan Zhou

{"title":"An On-orbit Calculation Method of Imaging Parameters of Medium Elliptical Orbit Satellites","authors":"Zeying Dong, Longfei Tian, Guohua Liu, Yang Liu, Huiyuan Wang, Dakan Zhou","doi":"10.1109/ICSMD57530.2022.10058264","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058264","url":null,"abstract":"The application of medium elliptical orbit satellites in the field of optical remote sensing is becoming more and more extensive. In order to improve the satellite's on-board autonomy, this paper proposes a strategy for on-orbit autonomous adjustment of camera imaging parameters suitable for global full-time detection of targets. In this paper, both the irradiation environment and mission efficiency are considered at the same time, and the orbit is optimized to reduce the ionizing radiation dose of high-energy protons from the source. The orbit is in the form of injection into the orbit base point, and the J2 model is used for orbit recursion 1 hour before and after the base point. Then, according to the requirements of the satellite in-motion imaging mission, considering the influence of orbit altitude change, observation target position, observation target surface temperature, and taking into account the limitations of on-board storage, the on-orbit calculation method of TDI line period, focus, gain and integration time is finally given. Using this method can improve the imaging quality of in-orbit remote sensing satellites, enhance the operational flexibility and on-board autonomy of satellites, and reduce the number of injections on the ground.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130013624","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

Multi-granularity Cross-Domain Temporal Regression Network for Remaining Useful Life Estimation of Aero Engines 航空发动机剩余使用寿命估算的多粒度跨域时间回归网络

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058343

Jiaxian Chen, Zhuyun Chen, Jingyan Xia, Ruyi Huang, Weihua Li

{"title":"Multi-granularity Cross-Domain Temporal Regression Network for Remaining Useful Life Estimation of Aero Engines","authors":"Jiaxian Chen, Zhuyun Chen, Jingyan Xia, Ruyi Huang, Weihua Li","doi":"10.1109/ICSMD57530.2022.10058343","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058343","url":null,"abstract":"Remaining useful life (RUL) prediction is a crucial task for predictive maintenance of industrial equipment. Benefiting from advanced sensing and artificial intelligence technologies, data-driven RUL prediction methods based on multimodal data analytics achieved rapid development in recent years. However, traditional RUL prediction methods often fail to meet the demand and challenge of data distribution discrepancy under different working conditions. To solve this issue, a novel aero-engine RUL estimation approach is proposed based on multi-sensor fusion and deep transfer learning. First, a multi-granularity cross-domain temporal regression (MCDTR) network is constructed to learn effective degradation information via fusing a coarse-grained learn strategy executed on the source domain and a fine-grained update strategy applied to the target domain. With such a multi-granularity transfer strategy, this network can exploit robust temporal features for accurate RUL prediction. In addition, the uncertainty quantification of predictive results based on the bootstrap method is also examined to improve the reliability and stability of RUL prediction for industrial aero engines. Related comparative experiments on the N-CMAPSS 2021 Challenge Dataset suggest the effectiveness and robustness of the proposed approach, which provides a valuable reference for prognostics and health management in industrial applications.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134025139","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

IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings iding:未知轴承的域概化剩余使用寿命预测方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058352

Juan Xu, Zhen Xu

{"title":"IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings","authors":"Juan Xu, Zhen Xu","doi":"10.1109/ICSMD57530.2022.10058352","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058352","url":null,"abstract":"Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756455","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