Titlebook: New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques; Advanced Machine Lea Guangrui Wen,Zihao Lei,Xin Huang B

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書(shū)目名稱New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques讀者反饋學(xué)科排名

毀壞

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

細(xì)微差別

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

營(yíng)養(yǎng)

大笑

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

多節(jié)

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

獸皮

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

膽大

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

惡臭

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

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