Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

Hasan, Junayed; Kim, Jaeyoung; Kim, Cheol Hong; Kim, Jong‐Myon

doi:10.3390/app10072525

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…Many research findings support the machine learning approach in machinery fault diagnosis as the ML methods are more competitive than signal-based methods [12][13][14][15]. Machine learning characteristics collected from data are more objective than signal-based methods.…”

Section: Introductionmentioning

confidence: 99%

Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Jamil¹,

Khan²,

Khanam³

2021

Vib. proced.

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Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring and fault diagnostics of these bearings. The development of machine learning (ML) brings a new way of diagnosing the fault of rolling element bearings. In the current work, ML models, namely, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are used to classify the faults associated with different ball bearing elements. Using open-source Case Western Reserve University (CWRU) bearing data, machine learning classifiers are trained with extracted time-domain and frequency-domain features. The results show that frequency-domain features are more convincing for the training of ML models, and the KNN classifier has a high level of accuracy compared to SVM.

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Section: Introductionmentioning

confidence: 99%

Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Jamil¹,

Khan²,

Khanam³

2021

Vib. proced.

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“…The degree to which vibration signals deviates from normal distribution is reflected by skewness and kurtosis.The waveform index is a sensitive and stable parameter, which can well represent the slight damage of slippers with different wear degrees. With the aggravation of wear, the impulsion index and tolerance index increase obviously [43][44][45]. Table 2.…”

Section: Multi-domain Feature Selectionmentioning

confidence: 99%

Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost

et al. 2020

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Under different degradation conditions, the complexity of natural oscillation of the piston pump will change. Given the difference of the characteristic values of the vibration signal under different degradation states, this paper presents a degradation state recognition method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and eXtreme gradient boosting (XGBoost) to improve the accuracy of state recognition. Firstly, ICEEMDAN is proposed to alleviate the mode mixing phenomenon, which decomposes the vibration signal and obtain the intrinsic mode functions (IMFs) with less noise and more physical meaning, and subsequently the optimal IMF is found by using the correlation coefficient method. Then, the time domain, frequency domain, and entropy of the effective IMF are calculated, and the new characteristic values which can represent the degradation state are selected by principal component analysis (PCA) that it realizes dimension reduction. Finally, the above-mentioned characteristic indexes are used as the input of the XGBoost algorithm to achieve the recognition of the degradation state. In this paper, the vibration signals of four different degradation states are generated and analyzed through the piston pump slipper degradation experiment. By comparing the proposed method with different state recognition algorithms, it can be seen that the method based on ICEEMDAN and XGBoost is accurate and efficient, the average accuracy rate can reach more than 99%. Therefore, this method can more accurately describe the degradation state of the piston pump and has a highly practical application value.

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“…The Boruta algorithm is a wrapper-based feature selection algorithm built using the random forest classification algorithm [38]. It has been successfully used in different fields of fault diagnosis [39,40]. It collects outputs from an ensemble of randomized samples through which it is possible to reduce the misleading effects of random fluctuations and correlations.…”

Section: Boruta-mahalanobis Systemmentioning

confidence: 99%

A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

Habib

Kim

2020

Applied Sciences

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This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.

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Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

Cited by 12 publications

References 15 publications

Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost

A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

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