Support vector machine in machine condition monitoring and fault diagnosis

By Achmad Widodo, Bo-Suk Yang. 2007

1,575 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/00a69e58ad3b145e608ee8c9b6d51a4a0012ecfa

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In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, methodology): “With regard to the method for fault classification, some artificial intelligence methods are adopted [15], such as artificial neural network (ANN), support vector machine (SVM) [13], [16], [17], k-nearest neighbor [18], fuzzy inference system [11], and random forest [19].”
In situ monitoring of FDM machine condition via acoustic emission (2015, methodology): “It has been widely applied in manufacturing system monitoring [9, 48, 57, 62].”
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches (2015, neutral): “in [29].”
Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes (2016, methodology): “In this paper we follow [1] and make a survey of works in the same field from 2007 to 2015.”
Machine learning in manufacturing: advantages, challenges, and applications (2016, methodology): “Among those are, e.g. immune to over-fitting (Widodo & Yang, 2007), bias, and variance (therefore bias–variance tradeoff) (Quadrianto & Buntine, 2011).”
Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 (2020, methodology): “In the PdM of industrial equipment, SVMs have been widely applied for identifying a specific status based on the acquired signal [55].”
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches (2015, background): “chinery system [24], [25], and swarm systems (consisting of multiple intelligent interconnected nodes and possessing swarm capability) [26], respectively.”
Fault diagnosis of ball bearings using machine learning methods (2011, background): “Various artificial intelligent (AI) techniques such as hidden Markov models (HMM) (Li, Wu, He, & Fulei, 2005), artificial neural networks (ANN) (Vyas & Satishkumar, 2001) and support vector machines (SVM) (Widodo & Yang, 2007; Yuan & Chu, 2007) have been used in the fault diagnosis of machines.”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, neutral): “status based on the acquired signal [68].”
A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, background): “Widodo and Yang (2007) proposed a survey of machine condition monitoring and fault diagnosis using support vector machine in order to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis.”