With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …

Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …

摘要提出了一种全新的分类框架, 将故障诊断方法整体分为两大类, 即定性分析的方法和定量
分析的方法. 对现有的方法在此框架下进行了划分, 并详细介绍了每种方法的基本思想 …

The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …

Safety and reliability are absolutely important for modern sophisticated systems and
technologies. Therefore, malfunction monitoring capabilities are instilled in the system for …

Modelling wastewater processes supports tasks such as process prediction, soft sensing,
data analysis and computer assisted design of wastewater systems. Wastewater treatment …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

Data-driven modeling and applications in plant-wide processes have recently caught much
attention in both academy and industry. This paper provides a systematic review on data …

The main roles of fault detection and diagnosis (FDD) for industrial processes are to make
an effective indicator which can identify faulty status of a process and then to take a proper …