A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches

By Zhiwei Gao, Carlo Cecati, Steven X. Ding. IEEE transactions on industrial electronics (1982. Print), 2015

862 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/a4656e544b95f9048733513da82847da65277c7f

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. Liang Gao
2. Xinyu Li
3. Long Wen
4. Michael Z. Q. Chen
5. Xiaoxu Liu
6. Boyuan Yang
7. Ruonan Liu
8. Chuang Sun

In Mechanical Engineering

1. Liang Gao
2. Xinyu Li
3. Long Wen
4. Xiaoxu Liu
5. Michael Z. Q. Chen
6. DONG GAO
7. Bei-ke Zhang
8. Xiaohan Chen

Citations in the corpus

• Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions (2016, background): “A detailed survey of fault diagnosis and fault-tolerant techniques, which have been developed in different domains, is provided in [13, 14].”
• Bearing fault diagnosis base on multi-scale CNN and LSTM model (2020, methodology): “Fault diagnosis using AI was first attempted in the 1990s (Gao et al. 2015), and has subsequently developed rapidly.”
• Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances (2016, methodology): “In the recent two-part survey paper [12, 13], fault diagnosis approaches were categorized into model-based methods, signal-based methods, knowledge-based methods, and hybrid/active methods.”
• Deep Model Based Domain Adaptation for Fault Diagnosis (2017, background): “MACHINE learning technologies have already achieved significant success in the field of fault diagnosis [1] recently.”
• A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, background): “2754287 establish explicit system models or signal symptoms [3].”
• A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (2018, background): “FAULT diagnosis has attracted many studies in the recent years [1], [2].”
• Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine (2017, background): “maintenance costs and prevent the accidents [1], [2].”