Fault Diagnosis of Welded Joints through Vibration Signals Using Naïve Bayes Algorithm

Kumar, Mukesh; Hemanth, K; Gangadhar, N.; Kumar, Hemantha; Krishna, Prasad

doi:10.1016/j.mspro.2014.07.514

Cited by 10 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kumar [20] also examined the wear of bearings by using the Naïve Bayes Classifier to classify them. The Naïve Bayes Classifier and the Bayesian Network Algorithm were also used in the monitoring and the vibration-based fault diagnosis of a monoblock centrifugal pump [21]. Feng used the Naïve Bayes Classifier for the prediction of slope stability [22].…”

Section: Future Opportunitiesmentioning

confidence: 99%

Classification of Impact Damage on A Rubber-Textile Conveyor Belt: A Review

Alviari

Anggamawarti

Sanjiwani³

et al. 2020

MECHTA

View full text Add to dashboard Cite

A conveyor belt is one type of goods transportation in technological processes, particularly in the mining industry. The belt is the important material and principal part of the conveyor belt. The overall quality of the conveyor belt as its service life and impact loads are very important factors. Therefore, the purpose of this paper is to classify the types of impact damage that may occur in rubber-textile conveyor belts. In many works, many types of conveyor belts are tested at various levels and the type of impacting material. The level of damage occurred is investigated by using probability theory. Particularly, the evaluation of experimental test data and predictive modeling is carried out using the Naïve Bayes classification.

show abstract

Section: Future Opportunitiesmentioning

confidence: 99%

Classification of Impact Damage on A Rubber-Textile Conveyor Belt: A Review

Alviari

Anggamawarti

Sanjiwani³

et al. 2020

MECHTA

View full text Add to dashboard Cite

show abstract

“…where P(A i |Pa i ) is the conditional distribution of A i , given Pa i , which denotes the parent set of A i . A conditional distribution for each variable has a parametric form that can be learned by the maximum likelihood estimation [18].…”

Section: Bayes Netmentioning

confidence: 99%

Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm

2017

Self Cite

View full text Add to dashboard Cite

This paper presents engine gearbox fault diagnosis based on empirical mode decomposition (EMD) and Naïve Bayes algorithm. In this study, vibration signals from a gear box are acquired with healthy and different simulated faulty conditions of gear and bearing. The vibration signals are decomposed into a finite number of intrinsic mode functions using the EMD method. Decision tree technique (J48 algorithm) is used for important feature selection out of extracted features. Naïve Bayes algorithm is applied as a fault classifier to know the status of an engine. The experimental result (classification accuracy 98.88%) demonstrates that the proposed approach is an effective method for engine fault diagnosis.

show abstract

“…Numerous research in weld quality monitoring and diagnosis has been carried out on single-conditioned welding tasks. Many research works have been focusing on offline diagnosis of weld quality [9][10][11], such as using acoustic emission method [10], X-ray image analysis [11], and so forth. Recently, advanced robotic welding has been applied increasingly in various industries, and online For this purpose, various sensor fusion and signal processing techniques have been carried out [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design of adaptive weld quality monitoring for multiple‐conditioned robotic welding tasks

Xia

Pang

Mamun

et al. 2021

Asian Journal of Control

View full text Add to dashboard Cite

Multiple-conditioned welding monitoring is a challenging issue in complex robotic welding tasks. In practice, the monitoring system has to be sensitive to different welding conditions (WCs) and weld quality changes. In this paper, a swing high temperature sensor system is used in order to obtain the temperature distribution curve under different WCs. A sigmoid feature extraction (SFE) method is proposed to obtain the geometric features of the temperature distribution curve, and a weld monitoring algorithm is proposed for multiple-conditioned welding tasks using multi-layer perceptron (MLP) classifier and Kalman filter-based Gaussian probability density function (PDF) prediction for the probabilistic weld quality estimation. When there are unknown WCs, the proposed method uses an efficient incremental learning for the MLP and an online maximum likelihood estimation for the Gaussian models of the unknown WCs. The experimental results show that the proposed framework can accurately reflect the weld quality changing in both single-WC and multiple-WC tasks. In addition, the proposed adaptive updating methodology can achieve comparable performance with unknown WCs, as compared to the results with knowing all the WCs.

show abstract

Fault Diagnosis of Welded Joints through Vibration Signals Using Naïve Bayes Algorithm

Cited by 10 publications

References 8 publications

Classification of Impact Damage on A Rubber-Textile Conveyor Belt: A Review

Classification of Impact Damage on A Rubber-Textile Conveyor Belt: A Review

Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm

Design of adaptive weld quality monitoring for multiple‐conditioned robotic welding tasks

Contact Info

Product

Resources

About