A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

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

2,627 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/5d2122828ee830fc214081e4df78e0dc4e31719c

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Citations in the corpus

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches (2015, methodology): “survey paper [1], model-based and signal-based diagnosis approaches were reviewed.”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “the time, frequency, and time-frequency domain [18], [19], and the representation learning of the device health is performed using the extracted features.”
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].”
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, neutral): “1(a)) [5]-[8].”
A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier (2018, methodology): “The model-based systems include both qualitative methods on the basis of symbolic intelligence (i.e. fault trees, diagraphs) and quantitative methods on the basis of machine learning intelligence (unsupervised and supervised learning systems) [1].”
A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, background): “In general, the fault diagnosis methods can be classified into model-based, signal-based, knowledgebased and hybrid/active approaches [14].”
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.”
Bayesian Networks in Fault Diagnosis (2017, background): “proach uses detected signals to diagnose possible abnormalities and faults by comparing detected signals with prior information of normal industrial systems [9].”
A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings (2021, methodology): “Generally, existing fault diagnosis methods consist of two categories: model-based [4] approaches and data-driven [5] methods.”
Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, background): “Fault diagnosis is widely applied in diverse areas such as manufacturing, aerospace, automotive, power generation, and transportation [1]–[4].”
Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions (2017, methodology): “These fault diagnosis methods can be divided into model driven methods [1], [2] and data driven methods.”
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (2018, neutral): “2774777 methods [3].”