Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection

Abid, Anam; Khan, Muhammad Tahir

doi:10.1109/tsmc.2017.2746762

Cited by 55 publications

(21 citation statements)

References 49 publications

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“…After one complete iteration of GA, we obtain one mature optimal detector, which is stored in the optimized detector set. The implementation process of GA is repeated to obtain and store the required optimum number of the detector set (Abid et al, 2017 ). The pseudo code for GA is given in Table 4 .…”

Section: Methodsmentioning

confidence: 99%

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Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Rashid

Iqbal

Mahmood

et al. 2018

Front. Hum. Neurosci.

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Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV–Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight.

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Section: Methodsmentioning

confidence: 99%

“…Abid et al ( 2017 ) suggested the optimization of detectors using Equations 17–19. Let the set of training sample data vectors be Ω k ( k = 1, 2, ……… K ) with self radius R k = R self , the location of the optimal set of detectors is given by d j ( j = 1, 2, ……… N d ).…”

Section: Methodsmentioning

confidence: 99%

Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Rashid

Iqbal

Mahmood

et al. 2018

Front. Hum. Neurosci.

Self Cite

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“…The work [15] presented a similar comparative study to evaluate the performance of the autoencoder to the original feature set, PCA reduction and real-valued negative selection. The dimensional reduction of the autoencoder, once again, have performed a highly improved anomaly detection compared to the others.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms

Figueirêdo¹,

Guarieiro²,

Nascimento³

2022

Artificial Intelligence

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The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data.

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“…The high failure rate of rolling bearings also increases the operation and maintenance costs. Moreover, in cases in which the potential faults of rolling bearings are not detected, there would be a high risk exists of the breakdown of the entire equipment [5]- [10].Therefore, it is always desirable and necessary to diagnose potential rolling bearing faults in time.…”

Section: Introductionmentioning

confidence: 99%

Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis

Liu

Wei

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this paper, a deep adversarial domain adaptation model (called DADA) is proposed for rolling bearing fault diagnosis. This model constructs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack auto-encoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack auto-encoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Thirdly, comprehensive analysis is performed on real data to validate the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.

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Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection

Cited by 55 publications

References 49 publications

Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms

Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis

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