Fault diagnosis of self-aligning troughing rollers in belt conveyor system using k-star algorithm

Ravikumar, S.; Kanagasabapathy, H.; Muralidharan, V.S.

doi:10.1016/j.measurement.2018.10.001

Cited by 42 publications

(17 citation statements)

References 21 publications

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“…Initially, 10 MLs were scanned using all protein descriptors as explanatory variables, while the response variable was set to the presence or absence of ACs. The screened learners were: K-Star [27], Locally weighted learning (LWL) [28,29], Logistic Model Trees (LMT) [30,31], LogitBoost [32], Support Vector Machine (SVM) [33,34], Naïve Bayes [35], Random Forests [36], Probabilistic Neural Network (PNN) [37,38], Xgboost [39] and k-nearest neighbors (k-NN) [40]. The MLs were implemented as corresponding KNIME 4.3.3 nodes with default parameters.…”

Section: Screening Machine Learners (Mls)mentioning

confidence: 99%

“…Classi cation with K* is made by summing the probabilities from the new instance to all of the members of a category [27]. On the other hand, eXtreme Gradient Boosting (XGBoost, or XGB) is a tree-based standardized ensemble method that relies on an ensemble of weak decision tree (DT)-type models to create new subsequent boosted DT-type models of a reduced loss function.…”

Section: Machine Learningmentioning

confidence: 99%

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Activity Cliffs As Protein-Related Phenomenon: Investigation Using Machine Learning Against Numerous Protein Kinases

Daoud

Taha

2022

Preprint

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Activity cliffs (ACs) are analogous compounds of significant affinity discrepancies against certain biotarget. We propose that the ACs phenomenon is protein-related and that the propensity of certain target to have ACs can be predicted by some intrinsic protein properties. We pursued this assumption by collecting the crystallographic structures of 84 protein kinases, each of which has numerous reported inhibitors (hundreds). Following data augmentation using synthetic minority oversampling technique (SMOTE), we attempted to correlate the presence/absence of ACs within the ligand pools of collected protein kinases with their corresponding protein properties using genetic algorithm (GA) coupled with variety of machine learners (MLs). Very good GA-ML models were achieved with accuracies of around 75% against external testing set. The models were further validated by Y-scrambling. Shapely additive explanations highlighted the significance of protein rotatable bonds, hydrophobic and acidic residues in relation to the presence of ACs. These results support the hypothesis that ACs are protein-related.

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Section: Screening Machine Learners (Mls)mentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Activity Cliffs As Protein-Related Phenomenon: Investigation Using Machine Learning Against Numerous Protein Kinases

Daoud

Taha

2022

Preprint

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“…Even different theories can absorb the advantages of other ways to improve their algorithms and innovate. Ravikumar et al [13] applied the decision tree method to find the significant features for classification of self-aligning troughing rollers' faults, and the k-star algorithm was applied for fault diagnosis to raise the fault classification accuracy. Arup et al [14] presented an automated tool called the Manufacturing Process Failure Diagnosis Tool (MPFDT), which can detect and isolate the faults and anomalies in the Programmable Logic Controller (PLC) controlled manufacturing systems effectively.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Belt Conveyor Based on Support Vector Machine and Grey Wolf Optimization

Liu

Zhang

et al. 2020

Mathematical Problems in Engineering

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Belt conveyor is widely used for material transportation over both short and long distances nowadays while the failure of a single component may cause fateful consequences. Accordingly, the use of machine learning in timely fault diagnosis is an efficient way to ensure the safe operation of belt conveyors. The support vector machine is a powerful supervised machine learning algorithm for classification in fault diagnosis. Before the classification, the principal component analysis is used for data reduction according to the varieties of features. To optimize the parameters of the support vector machine, this paper presents a grey wolf optimizer approach. The diagnostic model is applied to an underground mine belt conveyor transportation system fault diagnosis on the basis of monitoring data collected by sensors of mine internet of things. The results show that the recognition accuracy of the fault is up to 97.22% according to the mine site dataset. It is proved that the combined classification model has a better performance in fault intelligent diagnosis.

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“…(Ravikumar et al, 2019; Joshuva and Sugumaran 2020;Gao et al, 2019). The KNN algorithm adopts the Euclidean distance as a measure to determine the k-nearest neighbours, while the KStar uses an entropic distance measure based on probability(Cleary and Trigg 1995).…”

mentioning

confidence: 99%

“…It was demonstrated that the used entropy as a distance measure leads to a high regression and classi cation accuracy and can help in handling symbolic and real-valued data(Gao et al, 2019; Joshuva and Sugumaran 2020). The KStar algorithm proceeds by successive summation of the probabilities of the new attribute to all the remaining members of the category and the nal selection should be based upon the highest probability(Ravikumar et al, 2019).…”

mentioning

confidence: 99%

Multi-step-ahead soil temperature forecasting at multiple-depth based on meteorological data: Integrating resampling algorithms and machine learning models

Khosravi

Golkarian

Barzegar

2022

Preprint

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Direct soil temperature (ST) measurement is time-consuming and costly; thus, the use of a simple and cost-effective machine learning (ML) tool is helpful. In this study, ML approaches, including KStar, instance-based K-nearest learner (IBK) and locally weighted learner (LWL) coupled with resampling algorithms of bagging (BA) and dagging (DA) were developed and tested for multi-step ahead (3, 6 and 9 days ahead) ST forecasting. In addition, a linear regression model (LR) was used as a benchmark to compare the results. A dataset with daily ST time-series (as models’ output) along with meteorological data (mean (TMean), minimum (TMin) and maximum (TMax) air temperature, evaporation (Eva), sunshine hours (SSH) and solar radiation (SR); as models’ input) were collected at Isfahan synoptic station (Iran), in a farmland, during 13 years (1992–2005) at 5 and 50 cm soil depths. Six different input combination scenarios were proposed to the models based on Pearson’s correlation coefficients between inputs and outputs. For the model building, we used 70% of the data and the remaining 30% was considered for model evaluation through different visual and quantitative metrics. Our findings showed that variable TMean is the most effective input variable for ST forecasting in most of the developed algorithms, while in some cases the combination of several variables including TMean, TMax and as well as the integration of TMean, TMax, TMin, Eva and SSH proved to be the best input combinations. Among the evaluated models, KStar showed more compatibility with the BA algorithm, while, in most cases and depending on soil depth, IBK and LWL obtained more accurate results when they were hybridized with DA. For soil depth of 5 cm, BA-KStar has superior performance (i.e. Nash-Sutcliffe Efficiency (NSE) = 0.90, 0.87 and 0.85 for 3, 6 and 9 months ahead forecasting, respectively) while for soil depth of 50 cm, DA-KStar outperforms other algorithms (i.e. NSE = 0.88, 0.89 and 0.89 for 3, 6 and 9 months ahead forecasting, respectively). Also, results confirmed that all hybrid models had higher prediction capability than the LR model.

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Fault diagnosis of self-aligning troughing rollers in belt conveyor system using k-star algorithm

Cited by 42 publications

References 21 publications

Activity Cliffs As Protein-Related Phenomenon: Investigation Using Machine Learning Against Numerous Protein Kinases

Activity Cliffs As Protein-Related Phenomenon: Investigation Using Machine Learning Against Numerous Protein Kinases

Fault Diagnosis of Belt Conveyor Based on Support Vector Machine and Grey Wolf Optimization

Multi-step-ahead soil temperature forecasting at multiple-depth based on meteorological data: Integrating resampling algorithms and machine learning models

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