Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

As one of the representative unsupervised data augmentation methods, generative
adversarial networks (GANs) have the potential to solve the problem of insufficient samples …

The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

As the globally increasing population drives rapid urbanization in various parts of the world,
there is a great need to deliberate on the future of the cities worth living. In particular, as …

Although the existing generative adversarial networks (GAN) have the potential for data
augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal …

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …

This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …

Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …