Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine

Vashishtha, Govind; Kumar, Rajesh

doi:10.1088/1361-6501/ac2cf2

Cited by 36 publications

(13 citation statements)

References 32 publications

(66 reference statements)

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“…Another example is the minimization of passband, stopband, and transition band errors for the design of a two-channel quadrature mirror filter bank [27]. Interested readers can refer to [28,29,30] to see more engineering applications of such optimization methods. ■ distance between the normal node and the cluster head minimizing the energy consumption of sensors 2021 [11] In summary, the most important differences between this study and the above research are as follows:…”

Section: -Related Workmentioning

confidence: 99%

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Abbasi-khazaei

Rezvani

2022

Soft Comput

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One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the subproblems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as stateof-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

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Section: -Related Workmentioning

confidence: 99%

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Abbasi-khazaei

Rezvani

2022

Soft Comput

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“…Traditional intelligent diagnosis methods mainly include two processes: feature extraction and pattern classification. Feature extraction usually uses a variety of signal processing methods such as mode decomposition (Vashishtha et al, 2022a(Vashishtha et al, , 2022b(Vashishtha et al, , and 2022cVashishtha and Kumar, 2021), spectral analysis (Vashishtha and Kumar, 2022), wavelet transform and statistical features (Meng et al, 2019;Zadkarami et al, 2017) to extract time-domain, frequency-domain and time-frequency domain features from original data. The extracted features are then input into support vector machine (SVM), particle swarm optimization (PSO), and artificial neural network (ANN), and Bayesian network algorithms are used to classify the faults.…”

Section: Introductionmentioning

confidence: 99%

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

Yan

Liu

et al. 2023

Transactions of the Institute of Measurement and Control

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The leakage of natural gas pipelines may cause significant safety accidents and cause severe economic losses to society. Therefore, timely detection and location of pipeline leak is an effective measure to reduce losses. Traditional methods need to extract the leak features from signals and then input them into convolutional neural networks to realize leakage diagnosis, which is a tedious process. This paper proposes a pipeline leak detection method based on residual connections and one-dimensional separable convolutional neural network (1D-RC-SCNN) and optimizes the structure and parameters of the model. The model uses one-dimensional separable convolution to replace the traditional convolutional neural network, which effectively reduces the number of model parameters. The improved residual connection can prevent the training data from overfitting and speed up the model convergence. The leakage signal after segmentation and preprocessing is directly sent to the 1D-RC-SCNN model, the leakage features are adaptively extracted, and then, the results are output through the Softmax classifier. The method directly performs end-to-end processing on the leaked signal, which improves the data processing speed. Finally, the cross-correlation method is used to locate the pipeline leak. The effectiveness of the proposed method is verified by building an experimental platform and compared with related models. The experimental results show that the method has high accuracy for the detection and localization of pipeline leak.

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“…Since the mechanical equipment is becoming increasingly complicated, the traditional condition monitoring methods based on physical models and signal processing techniques have been less effective in TCM. With the great promotion of big data technology, data-driven methods have shown remarkable superiority in processing complex signals [14,15], which have also been introduced in TCM. For example, Yu et al developed a novel approach based on the weighted hidden Markov model for tool remaining life prediction and tool wear monitoring [16].…”

Section: Introductionmentioning

confidence: 99%

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

et al. 2022

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Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.

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Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine

Cited by 36 publications

References 32 publications

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

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