An Improved Convolutional Neural Network for Recognition of Incipient Faults

Xing, Jiaqi; Xu, Jinxue

doi:10.1109/jsen.2022.3189484

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…There are works involving applications of process monitoring using several network architectures, such as recurrent networks [351][352][353][354][355][356][357][358][359][360][361][362][363][364][365][366][367], convolutional networks [299,349,[368][369][370][371][372][373][374][375][376][377][378][379][380][381][382], autoencoders [282,365,379,, generative adversarial networks [406][407][408][409][410][411][412][413][414][415][416], Transformers [417]…”

Section: Applications In Process Monitoringmentioning

confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.

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Section: Applications In Process Monitoringmentioning

confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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“…The CNN model performance was evaluated by the R 2 , RMSE and MAE. These indices are defined as follows [28,29]:…”

Section: Cnn Modelmentioning

confidence: 99%

Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network

Li,

Zhang,

et al. 2023

Photonics

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The variation of shock-wave velocity with time induced by a millisecond-nanosecond combined pulse laser (CPL) on silicon is investigated. The convolution neural network (CNN) is used to predict the shock-wave velocity induced by a single ns laser and CPL with a ns laser energy density of 6, 12 and 24 J/cm2, ms laser energy density of 0 and 226.13 J/cm2, and pulse delay of 0, 0.4 and 0.8 ms. The four-layer CNN model was applied, ns laser energy density, ms laser energy density, pulse delay and time were set as the input parameter, while the shock-wave velocity was set as the output parameter. The correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) of the CNN model on the test data set was 0.9865, 3.54 and 3.01, respectively. This indicated that the CNN model shows a high reliability in the prediction of CPL-induced shock-wave velocity with limited experimental data.

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“…The UWB technology is widely utilized due to its fast transmission speed, strong anti-attenuation capability, and high-precision localization results, surpassing other indoor localization technologies including Bluetooth and WIFI [5]. However, in complex indoor environments, UWB signals may experience attenuation, reflection, and penetration due to obstacles, resulting in Non-Line-of-Sight (NLOS) error and severely degraded positioning accuracy [6]. Therefore, NLOS error identification and mitigation have become crucial research priorities in the area of UWB-assisted localization.…”

Section: Introductionmentioning

confidence: 99%

Study on the indoor mobile robot localization based on multi-sensor fusion

He,

2024

J. Phys.: Conf. Ser.

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In recent years, mobile robots have found extensive application across diverse sectors including industry, agriculture, healthcare, and defense. However, relying solely on a single sensor for mobile robot localization presents several challenges, such as limited accuracy, divergence of localization errors over time, and susceptibility to obstruction from obstacles. This paper proposes an indoor mobile robot localization algorithm assisted by Ultra-Wideband (UWB). The algorithm begins by calculating the credibility of the Line-of-Sight (LOS) environment using UWB ranging measurements and predicted distances, enabling the identification of the Non-Line-of-Sight (NLOS) environment. Subsequently, ranging measurements affected by NLOS errors are compensated by using a complementary filter. Finally, these measurements are utilized for Extended Kalman Filter updates to achieve the best estimation of the mobile robot’s position. Field tests are conducted on a wheeled robot to validate the effectiveness and performance of the developed approach. Results show that the localization approach reduces the maximum localization error from 41 cm to 19 cm compared to UWB trilateration, achieving a 53.7% improvement. Even under a prolonged NLOS environment, the algorithm ensures that the localization error remains below 25 cm. The proposed method is of significant merit in solving the challenge of indoor mobile robot localization.

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An Improved Convolutional Neural Network for Recognition of Incipient Faults

Cited by 10 publications

References 30 publications

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network

Study on the indoor mobile robot localization based on multi-sensor fusion

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