类属
发表于 2025-3-21 19:08:08
书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0669661<br><br> <br><br>书目名称New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0669661<br><br> <br><br>
毁坏
发表于 2025-3-21 22:21:41
Sparse Model-Driven Deep Learning for Weak Fault Diagnosis of Rolling Bearingsoach to digging the fault features of vibration signals, but it is not liable to reliably extract the fault features while maintaining good generalization. Therefore, this chapter proposes a novel end-to-end Deep Network-based Sparse Denoising (DNSD) framework based on the model-data-collaborative l
细微差别
发表于 2025-3-22 00:39:57
Memory Residual Regression Autoencoder for Bearing Fault Detectionen only by normal data has received increasing attention in recent years. In this chapter, an innovative deep learning-based model, namely, Memory Residual Regression Autoencoder (MRRAE) is developed to improve the accuracy of anomaly detection in bearing condition monitoring. The memory module and
营养
发表于 2025-3-22 04:48:27
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大笑
发表于 2025-3-22 12:16:01
Performance Degradation Assessment Based on Transfer Learning for Bearinglenge of generalization for performance degradation assessment models. And it is costly and time-consuming to collect a large amount of labeled data for supervised diagnosis, especially when the task comes from a new operating condition. Thus in this chapter, a novel bearing degradation assessment m
多节
发表于 2025-3-22 16:06:33
Remaining Useful Life Prediction on Transfer Learning for Bearingying operational conditions, conventional RUL prediction models trained on some run-to-failure (RTF) datasets are unlikely to be generalized to a new degraded process. To increase the generalizability, recent studies have focused on the development of the deep domain adaptation methods for RUL predi
兽皮
发表于 2025-3-22 17:18:11
Deep Sequence Multi-distribution Adversarial Model for Abnormal Condition Detection in Industrysy in losing effective information due to manual features extracting. Deep learning-based methods can solve the problem effectively, but the detection accuracy is still not satisfactory. In addition, most of the methods cannot take the time-ordered specialty into account, which is significant for ti
胆大
发表于 2025-3-22 23:56:15
Multi-scale Lightweight Fault Diagnosis Model Based on Adversarial Learningmples is limited in industrial practice, and these samples usually are contained with complex environmental noise. Therefore, it is necessary to develop a generalizable DL model with strong feature learning ability. To tackle the above challenges, this chapter proposes a multi-scale lightweight faul
恶臭
发表于 2025-3-23 04:42:11
Performance Degradation Assessment Based on Adversarial Learning for Bearing crucial to monitor the health status of rolling bearings so as to ensure the safe and stable operation for mechanical equipment. After detecting and diagnosing faults, how to identify the extent of bearing failure and performance degradation becomes a key step in condition-based maintenance. Howeve
Astigmatism
发表于 2025-3-23 07:58:16
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