Gear Crack Level Classification by Using KNN and Time-Domain Features from Acoustic Emission Signals Under Different Motor Speeds and Loads

Sánchez, René–Vinicio; Lucero, Pablo; Macancela, Jean-Carlo; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

doi:10.1109/sdpc.2018.8664979

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…KNN mainly targets the nearest neighbors’ labels using two instances with p features, x i = { x i 1 , x i 2 ,…, x i,p } and x j = { x j 1 , x j 2 ,…, x j,p } of the training data set, where i = 1,2,…, n , j = 1,2,…, n , and n being the total sample number. A metric d ( x i , xj )) which can be Manhattan, Euclidean, chi-square, or cosine among other distance measures is used to determine the distance between the two instances (Sanchez et al, 2018). Basically, for labeling a new class, KNN algorithm locates k neighbors in the training data set, with the nearest possible distances based on the used metric; thereafter, KNN selects the most dominating class among the KNN.…”

Section: Mathematical Backgroundmentioning

confidence: 99%

“…Liu et al (2018) have also made a review of artificial intelligence-based methods including K-nearest neighbors (KNN), support vector machine (SVM), ANN, Naive Bayes, and deep learning for rotating machines fault diagnosis. KNN has been widely implemented for defect detection and classification in several works (Sanchez et al, 2018;Tayyab et al, 2021aTayyab et al, , 2021b. KNN is computed based on the distances between the sample points and the nearest neighbors of the assigned set of points (Kherif et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms

Afia

Gougam

Rahmoune

et al. 2023

Transactions of the Institute of Measurement and Control

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Due to their complexity and often harsh working environment, air compressors are inevitably exposed to a variety of faults and defects during their operation. Thus, condition monitoring is critically required for early fault recognition and detection to avoid any type industrial failures. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is developed using real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states such as leakage inlet valve (LIV), leakage outlet valve (LOV), non-return valve (NRV), piston ring, flywheel, rider-belt and bearing defects. The proposed algorithm mainly consists of three steps: feature extraction, selection, and classification. For feature extraction, experimental acoustic signals are decomposed using maximal overlap discrete wavelet packet transform (MODWPT) by six levels into 64 wavelet coefficients (nodes). Thereafter, time domain features are calculated for each node to build each air compressor’s health state feature matrix. Each feature matrix dimension is reduced by selecting the most useful features using Harris hawks optimization (HHO) in the feature selection step. Finally, for feature classification, selected features are used as inputs for random forest (RF), ensemble tree (ET) and K-nearest neighbors (KNN) to detect, identify, and classify the compressor health states with high classification accuracy. Comparative studies with several feature extraction and selection methods prove the proposed approach’s efficiency in detecting, identifying, and classifying all air compressor faults.

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Section: Mathematical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms

Afia

Gougam

Rahmoune

et al. 2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Sanchez et al. 20 also proposed fault diagnosis based on the features extracted from vibration and AE signals in gearboxes, 21,22 using kNN algorithm. The proposed model used the relief F ranking over the four and five signal features to achieve the accuracy of 98.11% and 98% respectively.…”

Section: Knn Algorithm In Fault Diagnosismentioning

confidence: 99%

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Manikandan

Duraivelu

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

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“…RF has also been applied for rotating machine defects diagnosis [43] and for air compressors fault diagnosis again based on vibration analysis [44]. Besides RF, ensemble tree (ET) K-nearest neighbors (KNN) have been also widely implemented for defect detection and classification in several works [45][46][47][48][49]. Although considerable research has been carried out on fault diagnosis using machine learning technique, works on reciprocating air compressors using acoustic signals are very restricted due to the variety of faults and failures that occur in these electromechanical devices.…”

Section: Introductionmentioning

confidence: 99%

Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

AFIA

Gougam

Rahmoune

et al. 2023

Preprint

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Air compressors have become critical equipment in different industrial applications such as metallurgy, mining, machinery manufacturing, petrochemical industry, transportation, etc. However, because of their complex structure and often harsh working environment, air compressors inevitably face a variety of faults and failures during their operation. Therefore, intelligent diagnostic techniques are crucially important for early fault recognition and detection to avoid industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is proposed based on several approaches, mainly: Maximal overlap discrete wavelet packet transform (MODWPT) and time domain features for feature extraction, weighted superposition attraction (WSA) for feature selection and random forest (RF), ensemble tree (ET) K-nearest neighbors (KNN) as classifiers. The proposed approach is applied to real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states. According to our approach, the data signals are decomposed by MODWPT into several nodes. Then, the time domain features are calculated for each node to construct the feature matrix for each air compressor health state. After that, WSA is applied to every matrix in the feature selection step. Finally, KNN, ET and RF are used to calculate the classification accuracy and give the confusion matrix. Compared with the robust empirical mode decomposition (REMD), the experimental results prove the effectiveness of the proposed approach to detect, identify and classify all air compressor faults.

show abstract

Gear Crack Level Classification by Using KNN and Time-Domain Features from Acoustic Emission Signals Under Different Motor Speeds and Loads

Cited by 8 publications

References 19 publications

Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms

Intelligent fault classification of air compressors using Harris hawks optimization and machine learning algorithms

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

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