Abstract The Internet of Things benefits connectivity and functionality in industrial
environments, while Cloud Computing boosts computational capability. Hence, historical …

This survey paper provides a review and perspective on intermediate and advanced
reinforcement learning (RL) techniques in process industries. It offers a holistic approach by …

Feature extraction from process signals enables process monitoring models to be effective
in industrial processes. Deep learning presents extensive possibilities for extracting abstract …

The massive high-dimensional measurements accumulated by distributed control systems
bring great computational and modeling complexity to the traditional fault diagnosis …

This paper introduces a novel sparse dynamic inner principal component analysis (SDiPCA)
based monitoring for multimode dynamic processes. Different from traditional multimode …

Fault diagnosis plays a crucial role in chemical processes to prevent major accidents.
Recent advancements have leveraged deep learning to enhance fault diagnosis capabilities …

The anomaly detection for multimode industrial process is a challenging problem, because
the multiple operation modes present various main distributions of monitored variables, and …

Multi-mode processing is a central feature of modern industry. The application of monitoring
technology to multi-mode processing is crucial to ensure process safety and to enhance …

Identifying transition between modes is a key important issue in monitoring industrial
processes with multiple operations. This paper proposes a new transition-identification …

海量高维度的过程测量信息给传统的故障诊断算法带来极大的计算复杂度和建模复杂度,
且传统诊断算法存在难以利用高阶量进行在线估计的不足. 鉴于深度学**技术**大的数据表示 …