A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies
By V. Venkatasubramanian, Raghunathan Rengaswamy, Surya N. Kavuri. Computers and Chemical Engineering, 2003
1,441 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/9e24fc75c4dba9121aba46c63bb6d35310cc4643
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Petr Kadlec
- Sibylle Strandt
- Xiaosong Hu
- Zhiqiang Ge
- Bogdan Gabrys
- Simona Onori
- Xiaoxu Liu
- Zhiwei Gao
In Control and Systems Engineering
- Zhiqiang Ge
- Michael Z. Q. Chen
- Xiaoxu Liu
- Kailong Liu
- Xiaosong Hu
- Bogdan Gabrys
- Simona Onori
- Kai Zhang
Citations in the corpus
- A Survey on Deep Learning for Data-Driven Soft Sensors (2021, background): “There are three main types of approaches to establish soft sensing models, namely mechanism-based, knowledge-based, and data-driven methods [5].”
- Data-driven Soft Sensors in the process industry (2009, background): “The second part of the series (Venkatasubramanian et al., 2003a) deals with qualitative model representations and search strategies for process fault detection.”
- Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances (2016, methodology): “In the threepart review paper [9-11], fault diagnosis methods were classified into quantitative model based methods, qualitative model methods, and process history-based methods.”
- Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures (2020, background): “Furthermore, some data-driven methods also present inherent limitations, such as the need for a large amount of historical data, accompanied by a high computational cost and training complexity [70].”