A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

Zhang, Guokai; Xiao, Haoping; Jiang, Jingwen; Liu, Qinyuan; Liu, Yimo; Wang, Liying

doi:10.1155/2020/5831632

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…Once completed, financial data will be used to train intelligent strategies. e last section is the results and report section, which uses a plan to analyze financial information, draw up erroneous data, report, and provide financial advice to the financial statements, so it creates a closed loop [19].…”

Section: Design and Implementation Of Abnormalmentioning

confidence: 99%

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Jiang

2022

Computational Intelligence and Neuroscience

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In order to better solve the problems of low efficiency, large consumption of human resources, and relatively low degree of intelligence in the abnormal data monitoring system of financial accounting in colleges and universities under the background of current accounting computerization, this article takes data mining and neural network algorithm as the technical basis to build the abnormal data monitoring system of financial accounting in colleges and universities. This article uses data mining algorithm and neural network analysis technology to process the original accounting information of colleges and universities, effectively eliminate invalid data, retain valuable data, and improve the detection efficiency of abnormal accounting data. The system test results show that the accuracy of identifying 50 abnormal situations in the original accounting data of colleges and universities is more than 90% by using data mining and neural network model.

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Section: Design and Implementation Of Abnormalmentioning

confidence: 99%

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Jiang

2022

Computational Intelligence and Neuroscience

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“…We think these approaches are applicable for anomaly detection with audio data, as our concern is to measure the difference between normal and anomalous. Zhang et al [56] proposed a multi-index generative adversarial network (MI-GAN) to detect tool wear from imbalanced sensor signal data.…”

Section: Generative Adversarial Network-based Methodsmentioning

confidence: 99%

Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments

2021

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Anomaly detection without employing dedicated sensors for each industrial machine is recognized as one of the essential techniques for preventive maintenance and is especially important for factories with low automatization levels, a number of which remain much larger than autonomous manufacturing lines. We have based our research on the hypothesis that real-life sound data from working industrial machines can be used for machine diagnostics. However, the sound data can be contaminated and drowned out by typical factory environmental sound, making the application of sound data-based anomaly detection an overly complicated process and, thus, the main problem we are solving with our approach. In this paper, we present a noise-tolerant deep learning-based methodology for real-life sound-data-based anomaly detection within real-world industrial machinery sound data. The main element of the proposed methodology is a generative adversarial network (GAN) used for the reconstruction of sound signal reconstruction and the detection of anomalies. The experimental results obtained in the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) show the superiority of the proposed methodology over baseline approaches based on the One-Class Support Vector Machine (OC-SVM) and the Autoencoder–Decoder neural network. The proposed schematics using the unscented Kalman Filter (UKF) and the mean square error (MSE) loss function with the L2 regularization term showed an improvement of the Area Under Curve (AUC) for the noisy pump data of the pump.

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An imbalanced data learning approach for tool wear monitoring based on data augmentation

Zhang,

Liu,

Yue

et al. 2023

J Intell Manuf

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A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

Cited by 6 publications

References 33 publications

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments

An imbalanced data learning approach for tool wear monitoring based on data augmentation

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