Fault Detection and Diagnosis in Industrial Systems

By T. McAvoy. 2002

806 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/2be9de203c31a2c14565c9246055f7c387aac690

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Steven X. Ding
2. Shen Yin
3. Haitao Zhao
4. Bo Jin
5. Shaoyuan Sun
6. Hao Luo
7. Biao Huang
8. Okyay Kaynak

In Mechanical Engineering

1. Steven X. Ding
2. Shen Yin
3. Haitao Zhao
4. Bo Jin
5. Shaoyuan Sun
6. Hao Luo
7. Xiaochen Xie
8. Biao Huang

Citations in the corpus

• Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference (2016, background): “The bench mark data generated in [1], which can be downloaded from http://web.”
• Bibliographical review on reconfigurable fault-tolerant control systems (2003, neutral): “…2000; Isermann, 2005; Venkatasubramanian, Rengaswamy, Yin, et al., 2003; Venkatasubramanian, Rengaswamy, & Kavuri, 2003; Venkatasubramanian, Rengaswamy, Kavuri, et al., 2003) and books (Chiang et al., 2001; Isermann, 2006; Russell et al., 2000; Vachtsevanos et al., 2006; Witczak, 2007) for detail.”
• A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, methodology): “For process monitoring, FDA is an efficient tool for the purpose of fault classification [36].”
• Sequential Fault Diagnosis Based on LSTM Neural Network (2018, methodology): “Considering the dynamic properties of raw process data, dynamic principal component analysis (DPCA) [16], [17]”
• Perspectives of fuzzy systems and control (2005, background): “The reader is referred to [14,5] and references therein for details.”
• Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations (2014, methodology): “At the time (1997) when the Committee did the proposal for the terminology in the field of supervision, fault detection and diagnosis, most of the applications were supporting fault detection by simple threshold logic or statistical hypothesis testing [2] whereas the works using more complex techniq”
• Survey on data-driven industrial process monitoring and diagnosis (2012, methodology): “…statistics for process monitoring that brought significant impact to the area in terms of methodology development and industrial practice (MacGregor & Koutoudi, 1995; Wise & Gallagher, 1996; Chiang et al., 2001; Qin, 2003; Cinar, Palazoglu, & Kayihan, 2007; Kano et al., 2008; Yao & Gao, 2009).”
• Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis (2015, methodology): “The data set used in this paper are given in [22] and is widely accepted for process monitoring and fault diagnosis.”
• Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations (2010, methodology): “1551-3203/$26.00 © 2009 IEEE Different from the above mentioned studies, Chiang et al. [5] employed canonical variate analysis to include the input and output variables for the estimation of the state-space variable.”