Faults diagnosis of a centrifugal pump using multilayer perceptron genetic algorithm back propagation and support vector machine with discrete wavelet transform‐based feature extraction

Tobi, Maamar Al; Bevan, Geraint; Wallace, Peter G.; Harrison, David K.; Okedu, Kenneth E.

doi:10.1111/coin.12390

Cited by 21 publications

(9 citation statements)

References 40 publications

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“…WPT is similar to DWT except WPT provides higher and finer decomposition tree, where both approximation (A) and detail (D) can produce pairs of packets (second level of approximation and detail), but DWT does not have such ability (ie, the next or second level of approximation and detail can be split by the approximation (A) only). 52 WPT has been applied for other types of rotating machinery.. 46,[51][52][53][54][55] In this work, WPT using two mother wavelets (db4 and rbio1.5) is applied for the preprocessing and feature extraction. Three cases are considered, and they are as follows:…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

“…WPT has been applied for other types of rotating machinery. 46,51‐55 . In this work, WPT using two mother wavelets (db4 and rbio1.5) is applied for the preprocessing and feature extraction.…”

Section: Feature Extractionmentioning

confidence: 99%

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Using MLP‐GABP and SVM with wavelet packet transform‐based feature extraction for fault diagnosis of a centrifugal pump

Tobi

Bevan

Wallace

et al. 2021

Energy Science & Engineering

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This paper explores artificial intelligent training schemes based on multilayer perceptron, considering back propagation and genetic algorithm (GA). The hybrid scheme is compared with the traditional support vector machine approach in the literature to analyze both fault and normal scenarios of a centrifugal pump. A comparative analysis of the performance of the variables was carried out using both schemes. The study used features extracted for three decomposition levels based on wavelet packet transform. In order to investigate the effectiveness of the extracted features, two mother wavelets were investigated. The salient part of this work is the optimization of the hidden layers numbers using GA. Furthermore, this optimization process was extended to the multilayer perceptron neurons. The evaluation of the model system performance used for the study shows better response of the extracted features, and hidden layers variables including the selected neurons. Moreover, the applied training algorithm used in the work was able to enhance the classifications obtained considering the hybrid artificial intelligent scheme been proposed. This work has achieved a number of contributions like GA‐based selection of hidden layers and neuron, applied in neural network of centrifugal pump condition classification. Furthermore, a hybrid training method combining GA and back propagation (BP) algorithms has been applied for condition classification of a centrifugal pump. The obtained results have shown the good ability of the proposed methods and algorithms.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

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Using MLP‐GABP and SVM with wavelet packet transform‐based feature extraction for fault diagnosis of a centrifugal pump

Tobi

Bevan

Wallace

et al. 2021

Energy Science & Engineering

Self Cite

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show abstract

“…3) The network shows high connectivity [18], which is decided by the weight of the ANN. For a feed-forward neural network with multiple layers, it can gain its computing power by integrating these features with its capability to learn from training experience.…”

Section: The Steady-state Model Training Algorithm Based On Neural Ne...mentioning

confidence: 99%

A Steady-State Modeling Method for Direct Expansion Air Conditioning Systems

Gao¹,

Xia²,

Zhang³

et al. 2021

IJHT

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To figure out the operating features of Direct-Expansion Air-Conditioning (DX-AC) system, this paper employed the BP training algorithm and built a two-in/two-out steady-state model based on artificial neural network (ANN) and connect the DX-AC system’s Totally output Cooling Capacity (TCC) and Sensible Heat Ratio (SHR) under different combinations of rotational velocities of compressor and blower; then, the paper verified the proposed model by comparing the prediction results of the proposed model with experimental data, and the numerical analysis of the steady-state model and bilinear interpolation indicated that the ANN-based model proposed in the paper showed a high prediction accuracy and can well exhibit its operating performance.

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“…The model will be biased toward the majority of cases if it is trained directly on unbalanced data. Numerous real-world applications, such as intrusion detection, 4 fault diagnosis, 5,6 computer vision, 7 malware detection, 8 and text categorization, 9 frequently encounter class-imbalance issues.…”

Section: Introductionmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

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One of the most widely used approaches to the class‐imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi‐armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi‐armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out‐of‐bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi‐armed bandit heterogeneous ensemble model is then assessed using 30 real‐world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state‐of‐the‐art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post‐hoc test.

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Faults diagnosis of a centrifugal pump using multilayer perceptron genetic algorithm back propagation and support vector machine with discrete wavelet transform‐based feature extraction

Cited by 21 publications

References 40 publications

Using MLP‐GABP and SVM with wavelet packet transform‐based feature extraction for fault diagnosis of a centrifugal pump

Using MLP‐GABP and SVM with wavelet packet transform‐based feature extraction for fault diagnosis of a centrifugal pump

A Steady-State Modeling Method for Direct Expansion Air Conditioning Systems

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

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