Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Moosavian, Ashkan; Ahmadi, Hojjat; Tabatabaeefar, A.; Khazaee, Majid

doi:10.1155/2013/360236

Cited by 97 publications

(48 citation statements)

References 39 publications

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“…So, each of those neighbours will regard those particular data as members of their class. Usually, Euclidean distance criterion is used to find the nearest sample K. The value of K is equal to 10 [23,24]. Table 8 shows the accuracy and recognition rate in each algorithm iteration for the KNN classifier.…”

Section: Evaluation Of the Proposed Methods Performancementioning

confidence: 99%

Prediction of stroke probability occurrence based on fuzzy cognitive maps

et al. 2019

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Among neurological patients, stroke is the most common cause of mortality. It is a health problem that is very costly all over the world. Therefore, the mortality due to the disease can be reduced by identifying and modifying the risk factors. Controllable factors which are contributing to stroke including hypertension, diabetes, heart disease, hyperlipidemia, smoking, and obesity. Therefore, by identifying and controlling the risk factors, stroke can be prevented and the effects of this disease could be reduced to a minimum. Therefore, for the quick and timely diagnosis of the disease, we need an intelligent system to predict the stroke risk. In this paper, a method has been proposed for predicting the risk rate of stroke which is based on fuzzy cognitive maps and nonlinear Hebbian learning algorithm. The accuracy of the proposed NHL-FCM model is tested using 15-fold cross-validation, for 90 actual cases, and compared with those of support vector machine and k-nearest neighbours. The proposed method shows superior performance with a total accuracy of (95.4 ± 7.5)%.

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Section: Evaluation Of the Proposed Methods Performancementioning

confidence: 99%

Prediction of stroke probability occurrence based on fuzzy cognitive maps

et al. 2019

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“…Moosavian et al 86 used vibration analysis to detect oil starvation using ANNs while a less common technique, AE, has also proven successful for detecting metal-metal contact. 104 It should be noted that research on wear and fatigue detection of sliding contacts can also be applied to hydrodynamic bearings taking into consideration line contact instead of point contact.…”

Section: Sunnersjo¨5mentioning

confidence: 99%

A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

Cubillo

Perinpanayagam

Esperon-Miguez

2016

Advances in Mechanical Engineering

222

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Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal-metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery.

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“…The Euclidean distance between points of p and q is the length of the line between them. In Cartesian coordinates, if p i and q i are two points in Euclidean n-space, then the distance from p to q is given by (Bagheri et al, 2010;Moosavian et al, 2013):…”

Section: Knnmentioning

confidence: 99%

Support vector machine and K-nearest neighbour for unbalanced fault detection

Moosavian

Ahmadi

Sakhaei

et al. 2014

Journal of Quality in Maintenance Engineering

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Purpose -The purpose of this paper is to develop an appropriate approach for detecting unbalanced fault in rotating machines using KNN and SVM classifiers. Design/methodology/approach -To fulfil this goal, a fault diagnosis approach based on signal processing, feature extraction and fault classification, was used. Vibration signals were acquired from a designed experimental system with three conditions, namely, no load, balanced load and unbalanced load. FFT technique was applied to transform the vibration signals from time-domain into frequency-domain. In total, 29 feature parameters were extracted from FFT amplitude of the signals. SVM and KNN were employed to classify the three different conditions. The performances of the two classifiers were obtained under different values of their parameter. Findings -The experimental results show the potential application of SVM for machine fault diagnosis. Practical implications -The results demonstrate that the proposed approach can be used effectively for detecting unbalanced condition in rotating machines. Originality/value -In this paper, an intelligent approach for unbalanced fault detection was proposed based on supervised learning method. Also, a performance comparison was made between KNN and SVM in fault classification. In addition, this approach gave a high level of classification accuracy. The proposed intelligent approach can be used for other mechanical faults.

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Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Cited by 97 publications

References 39 publications

Prediction of stroke probability occurrence based on fuzzy cognitive maps

Prediction of stroke probability occurrence based on fuzzy cognitive maps

A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

Support vector machine and K-nearest neighbour for unbalanced fault detection

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