A State-Of-The-Art Review of Industrial Time Series Data Analysis: Methods and Applications
TL;DR
A systematic review of state-of-the-art methods—spanning statistical approaches, machine learning (ML), deep learning (DL), and cutting-edge large models—along with their applications in industrial decision-making to inspire innovations in realizing data-driven intelligent decision-making for next-generation IIoT systems.
Abstract
The Industrial Internet of Things (IIoT) is characterized by the generation of vast amounts of time-series data. Modern IIoT systems enable efficient collection, storage, and querying of massive industrial time-series data, making the processing and analysis of such data a key enabler for data-driven decision-making in modern manufacturing. To provide researchers and practitioners with comprehensive guidance on industrial time series data analysis, this paper presents a systematic review of state-of-the-art methods—spanning statistical approaches, machine learning (ML), deep learning (DL), and cutting-edge large models—along with their applications in industrial decision-making. It details the application status of these methods in key equipment condition monitoring, manufacturing process supervision, and energy network management. Additionally, the paper discusses existing gaps between methods and real-world applications, as well as future trends and challenges, such as optimizing data structures for cost-sensitive learning, exploring causality and time-series-oriented model architectures, and developing cascaded/hybrid pipelines for end-to-end industrial use cases. Ultimately, this review aims to inspire innovations in realizing data-driven intelligent decision-making for next-generation IIoT systems.