Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader
TLDR
Four machine learning tools are applied to address the challenge in the IFDI of cutting arms and the experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods.Abstract:
Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.read more
Citations
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References
More filters
Journal ArticleDOI
Spatial filtering and Bayesian data fusion for mapping soil properties: a case study combining legacy and remotely sensed data in Iran.
Zahra Rasaei,Patrick Bogaert +1 more
TL;DR: This paper addresses the issue of combining at best soil data coming from legacy soil surveys with soil information that is indirectly obtained from remote sensing (RS) and shows how the relationship between them is improved by using a filtered kriging technique that allows them to focus on their long range component only.
Journal ArticleDOI
A Hybrid Genetic Algorithm and Back-Propagation Classifier for Gearbox Fault Diagnosis
Sunil Tyagi,S. K. Panigrahi +1 more
TL;DR: An Artificial Neural Network (ANN) classifier trained by a hybrid GA-BP method for diagnosis of gear faults is presented here that can be incorporated in an online fault diagnostic system of vital gearboxes.
Journal ArticleDOI
An incremental model transfer method for complex process fault diagnosis
Xiaogang Wang,Xiyu Liu,Yu Li +2 more
TL;DR: An incremental model transfer fault diagnosis method that can identify M faults more in the new process with the thought of incremental learning and offers a solution to a series of problems caused by the increase of fault classes.
Journal ArticleDOI
SVM for FT‐MIR prostate cancer classification: An alternative to the traditional methods
Laurinda F.S. Siqueira,Camilo De Lelis Medeiros-De Morais,Raimundo Fernandes de Araújo Júnior,Aurigena Antunes de Araújo,Kássio M. G. Lima +4 more
TL;DR: Results showed that variable reduction and selection methods followed by SVM can reduce drawbacks of independent SVM analysis, and GA‐SVM was the best classification approach, particularly in early stages, being better than traditional methods of diagnosis.
Journal ArticleDOI
A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm
TL;DR: A novel model for fault detection of rolling bearing faults based on a high-performance support vector machine that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO) is proposed.