Journal of Dynamics, Monitoring and Diagnostics最新文献_第3页

Wavelet Denoising Applied to Hardware Redundant Systems for Rolling Element Bearing Fault Detection 小波去噪在滚动轴承故障检测中的应用

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-06-05 DOI: 10.37965/jdmd.2023.231

Dustin Helm, M. Timusk

引用次数: 0

Constraint Guided Autoencoders to Enforce a Predefined Threshold on Anomaly Scores: An Application in Machine Condition Monitoring 约束引导自编码器在异常分数上强制预定义阈值:在机器状态监测中的应用

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-06-05 DOI: 10.37965/jdmd.2023.234

Maarten Meire, Quinten Van Baelen, T. Ooijevaar, P. Karsmakers

{"title":"Constraint Guided Autoencoders to Enforce a Predefined Threshold on Anomaly Scores: An Application in Machine Condition Monitoring","authors":"Maarten Meire, Quinten Van Baelen, T. Ooijevaar, P. Karsmakers","doi":"10.37965/jdmd.2023.234","DOIUrl":"https://doi.org/10.37965/jdmd.2023.234","url":null,"abstract":"Anomaly detection (AD) is an important task in a broad range of domains. A popular choice for AD are Deep Support Vector Data Description models. When learning such models, normal data is mapped close to and anomalous data is mapped far from a center, in some latent space, enabling the construction of a sphere to separate both types of data. Empirically it was observed: (i) that the center and radius of such sphere largely depends on the training data and model initialization which leads to difficulties when selecting a threshold, and (ii) that the center and radius of this sphere strongly impacts the model AD performance on unseen data. In this work, a more robust AD solution is proposed that (i) defines a sphere with a fixed radius and margin in some latent space and (ii) enforces the encoder, which maps the input to a latent space, to encode the normal data in a small sphere and the anomalous data outside a larger sphere, with the same center. Experimental results indicate that the proposed algorithm attains higher performance compared to alternatives and that the difference in size of the two spheres has a minor impact on the performance.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127538900","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: 3

RMA-CNN: A Residual Mixed-Domain Attention CNN for Bearings Fault Diagnosis and its Time-Frequency Domain Interpretability RMA-CNN:一种用于轴承故障诊断的残差混合域关注CNN及其时频可解释性

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-04-21 DOI: 10.37965/jdmd.2023.156

Dandan Peng, Huan Wang, W. Desmet, K. Gryllias

{"title":"RMA-CNN: A Residual Mixed-Domain Attention CNN for Bearings Fault Diagnosis and its Time-Frequency Domain Interpretability","authors":"Dandan Peng, Huan Wang, W. Desmet, K. Gryllias","doi":"10.37965/jdmd.2023.156","DOIUrl":"https://doi.org/10.37965/jdmd.2023.156","url":null,"abstract":"Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations. Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in machinery fault diagnosis. However, complex and varying working conditions can lead to inter-class similarity and intra-class variability in datasets, making it more challenging for CNNs to learn discriminative features. Furthermore, CNNs are often considered \"black boxes\" and lack sufficient interpretability in the fault diagnosis field. To address these issues, this paper introduces a Residual Mixed-Domain AttentionCNN method, referred to as RMA-CNN. This method comprises multiple ResidualMixed Domain Attention Modules (RMAMs), each employing one attention mechanism to emphasize meaningful features in both time and channel domains. This significantly enhances the network's ability to learn fault-related features. Moreover, we conduct an in-depth analysis of the inherent feature learning mechanism of the attention module RMAM to improve the interpretability of CNNs in fault diagnosis applications. Experiments conducted on two datasets—a high-speed aeronautical bearing dataset and a motor bearing dataset—demonstrate that the RMA-CNN achieves remarkable results in diagnostic tasks.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128544395","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: 5

A Novel Deep Model with Meta-learning for Rolling Bearing Few-shot Fault Diagnosis 基于元学习的滚动轴承少弹故障诊断深度模型

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-04-18 DOI: 10.37965/jdmd.2023.164

Xiaoxia Liang, M. Zhang, Guojin Feng, Yuchun Yu, D. Zhen, F. Gu

{"title":"A Novel Deep Model with Meta-learning for Rolling Bearing Few-shot Fault Diagnosis","authors":"Xiaoxia Liang, M. Zhang, Guojin Feng, Yuchun Yu, D. Zhen, F. Gu","doi":"10.37965/jdmd.2023.164","DOIUrl":"https://doi.org/10.37965/jdmd.2023.164","url":null,"abstract":"Machine learning, especially deep learning, has been highly successful in data-intensive applications, however, the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement. This leads to the so-called Few-Shot Learning (FSL) problem, which requires the model rapidly generalize to new tasks that containing only a few labeled samples. In this paper, we proposed a new deep model, called deep convolutional meta-learning networks (DCMLN), to address the low performance of generalization under limited data for bearing fault diagnosis. The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data. The proposed method was compared to several few-shot learning methods, including methods with and without pre-training the embedding mapping, and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain. The comparisons are carried out on one-shot and ten-shot tasks using the CWRU bearing dataset and a cylindrical roller bearing dataset. The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions. In addition, we found that the pre-training process does not always improve the prediction accuracy.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128566254","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: 3

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm 基于斐波那契搜索和遗传算法的风电机组最优预防性维护调度

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-04-18 DOI: 10.37965/jdmd.2023.158

Ekamdeep Singh, Sajad Saraygord Afshari, Xihui Liang

{"title":"Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm","authors":"Ekamdeep Singh, Sajad Saraygord Afshari, Xihui Liang","doi":"10.37965/jdmd.2023.158","DOIUrl":"https://doi.org/10.37965/jdmd.2023.158","url":null,"abstract":"Maintenance scheduling is essential and crucial for wind turbines to avoid breakdowns and reduce maintenance costs. Many maintenance models have been developed for wind turbines' maintenance planning, such as corrective, preventive and predictive maintenance. Due to communities' dependence on wind turbines for electricity needs, preventative maintenance is the most widely used method for maintenance scheduling. The downside to using this approach is that preventive maintenance is often done in fixed intervals, which is inefficient. In this paper, a more detailed maintenance plan for a 2MW wind turbine has been developed. The paper's focus is to minimize a wind turbine's maintenance cost based on a wind turbine's reliability model. This study uses a two-layer optimization framework: Fibonacci and Genetic Algorithm (GA). The first layer in the optimization method (Fibonacci) finds the optimal number of preventive maintenance required for the system. In the second layer, the optimal times for preventative maintenance and optimal components to maintain have been determined to minimize maintenance costs. The Monte Carlo simulation estimates wind turbine component failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the overall corrective and preventive maintenance costs during the system's lifetime. Finally, an optimal preventive maintenance schedule is proposed for a 2MW wind turbine using the presented method. The method used in this paper can be expanded to a wind farm or similar engineering systems.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758047","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: 3

Experimental Study on Entropy Features in Machining Vibrations of A Thin-walled Tubular Workpiece 薄壁管状工件加工振动熵特征的实验研究

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-03-16 DOI: 10.37965/jdmd.2023.155

Kaibo Lv, Xin Wang, Xun Chen, Xinyu Pang, F. Gu

{"title":"Experimental Study on Entropy Features in Machining Vibrations of A Thin-walled Tubular Workpiece","authors":"Kaibo Lv, Xin Wang, Xun Chen, Xinyu Pang, F. Gu","doi":"10.37965/jdmd.2023.155","DOIUrl":"https://doi.org/10.37965/jdmd.2023.155","url":null,"abstract":"In machining processes, chatter vibrations are always regarded as one of the major limitations for production quality and efficiency. Accurate and timely monitoring of chatter is helpful to maintain stable machining operations. At present, most chatter monitoring methods are based on the energy level at specified chatter frequencies or frequency bands. However, the spectral features of chatter could change during machining operations due to complexity and time-varying dynamics of the physical machining process. The purpose of this paper is to investigate the time-varying chatter features in turning of thin-walled tubular workpieces from the perspective of entropy. The airborne acoustics was selected as the source of information for machining condition monitoring. First, corresponding to the distinguishing surface topographies relevant to machining conditions, the features of the sound signal emitted during turning of the thin-walled cylindrical workpieces were extracted using the spectral analysis and wavelet packet transform, respectively. It was shown that the dominant vibration frequency as well as the energy distribution could shift with the transition of the machining status. After that, two relative entropy indicators based on the spectrum and the wavelet packet energy were constructed to identify chattering events in turning of the thin-walled tubes. The experimental results demonstrate that the proposed indicators could accurately reflect the transition of machining conditions with high sensitivity and robustness in comparison with the traditional FFT-based methods. The achievement of this study lays the foundations of the online chatter monitoring and control technique for turning of the thin-walled tubular workpieces.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123391923","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

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities 旋转机械的复合故障诊断:最新技术、挑战和机遇

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-03-02 DOI: 10.37965/jdmd.2023.152

Ruyi Huang, Jingyan Xia, Bin Zhang, Zhuyun Chen, Weihua Li

{"title":"Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities","authors":"Ruyi Huang, Jingyan Xia, Bin Zhang, Zhuyun Chen, Weihua Li","doi":"10.37965/jdmd.2023.152","DOIUrl":"https://doi.org/10.37965/jdmd.2023.152","url":null,"abstract":"Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127798660","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: 13

Prognostics and Remaining Useful Life Prediction of Machinery: Advances, Opportunities and Challenges 机械预测和剩余使用寿命预测:进展、机遇和挑战

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-02-20 DOI: 10.37965/jdmd.2023.148

JDMD Editorial Office, N. Gebraeel, Y. Lei, Naipeng Li, Xiaosheng Si, E. Zio

引用次数: 7

U-shaped Vision Transformer and Its Application in Gear Pitting Measurement u形视觉变压器及其在齿轮点蚀测量中的应用

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2022-11-24 DOI: 10.37965/jdmd.2022.130

Sijun Wang, Yi Qin, Dejun Xi, Chen Liang

{"title":"U-shaped Vision Transformer and Its Application in Gear Pitting Measurement","authors":"Sijun Wang, Yi Qin, Dejun Xi, Chen Liang","doi":"10.37965/jdmd.2022.130","DOIUrl":"https://doi.org/10.37965/jdmd.2022.130","url":null,"abstract":"Although convolutional neural networks (CNNs) have become the mainstream segmentation model, the locality of convolution makes them cannot well learn global and long-range semantic information. To further improve the performance of segmentation models, we propose u-shaped vision Transformer (UsViT), a model based on Transformer and convolution. Specifically, residual Transformer blocks are designed in the encoder of UsViT, which take advantages of residual network and Transformer backbone at the same time. What’s more, transpositions in each Transformer layer achieve the information interaction between spatial locations and feature channels, enhancing the capability of feature learning. In the decoder, for enhancing receptive filed, different dilation rates are introduced to each convolutional layer. In addition, residual connections are applied to make the information propagation smoother when training the model. We first verify the superiority of UsViT on Automatic Portrait Matting public dataset, which achieves 90.43% Acc、95.56% DSC and 94.66% IoU with relatively fewer parameters. Finally, UsViT is applied to gear pitting measurement in gear contact fatigue test, and the comparative results indicate that UsViT can improve the accuracy of pitting detection.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127670399","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

Keynote Summaries of the First International Symposium on Dynamics, Monitoring and Diagnostics 第一届动力学、监测和诊断国际研讨会的主题摘要

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2022-10-19 DOI: 10.37965/jdmd.2022.134

JDMD Editorial Office, J. Antoni, P. Heyns, Jing Lin, H. Ouyang, Stephan Schmidt, Wade A. Smith, D. Wilke

引用次数: 7