ICA Based Sensors Fault Diagnosis: An Audio Separation Application

Uddin, Zahoor; Qamar, Aamir; Alam, Farooq

doi:10.1007/s11277-021-08184-x

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…Typically, sensor fault detection methods are categorized into three primary classes, statistical-based methods [11][12][13], residual-based methods [14][15][16], and classification-based methods [17][18][19]. Statistical-based methods establish sensor fault detection indices and derive their control limits from data obtained under fault-free conditions.…”

Section: Introductionmentioning

confidence: 99%

Online meta-learning approach for sensor fault diagnosis using limited data

Wang,

Huang,

Huang

et al. 2024

Smart Mater. Struct.

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The accurate and timely diagnosis of sensor faults plays a critical role in ensuring the reliability and performance of structural health monitoring (SHM) systems. However, the challenge is detecting, locating, and estimating sensor faults in an online manner using limited training data. To resolve this problem, a novel approach for online sensor fault diagnosis is proposed for SHM. The proposed approach is based on meta-learning, which enables superior model generalization capabilities using limited data. The detection, localization, and estimation of typical sensor faults in an online manner can be achieved efficiently by the proposed approach. First, a one-dimensional convolutional neural network (1D CNN) is designed to detect and locate faulty sensors. The initial model parameters of the 1D CNN are optimized using a model-agnostic meta-learning training strategy. This strategy allows the acquisition of transferable prior knowledge, which can speed up the learning process on new sensor fault detection and localization tasks. The meta-learning strategy also enables efficient and accurate detection and localization of potential faulty sensors with limited data. After detecting and locating the faulty sensors, an online updating algorithm based on a dual Kalman filter is used to estimate the severity of sensor faults and structural states simultaneously. The proposed approach is demonstrated with simulated sensor faults that cover a numerical example and field measurements from the Canton Tower. The results show that the proposed approach is applicable for online sensor fault diagnosis in SHM.

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Section: Introductionmentioning

confidence: 99%

Online meta-learning approach for sensor fault diagnosis using limited data

Wang,

Huang,

Huang

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

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Image cutting in video media technology application based on detection algorithm

Jiahui

2023

Int J Syst Assur Eng Manag

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Speech activity detection algorithm belongs to a kind of means to judge the audio section and non audio section. Audio information diagnosis is composed of audio and noise section, and special treatment means are used for different signals. In the real model test, we can understand that RBF core has super stable attributes compared with other models, and has good self-renewal ability. In the audio information discrimination attribute diagnosis, we can understand the upgrading calculation process, and understand the large-scale counting audio information discrimination calculation process. The removal of pictograph is the primary obstacle to the solution scope of visual information. In the real use process, the classi ed noises are stored in the image removal with different attributes on a large scale, which plays a great role in the gradual diagnosis of image removal such as image upgrade and image classi cation. Therefore, in order to update the image level, eliminate the noise object and gradually diagnose the image, exploring the image removal method has become the rst in uential step in the rst step of image diagnosis.. In the era of interconnection, we began to further explore the merging means model of interconnection, visualization and information interoperability, focusing on the governance of the merging and upgrading means model on the premise of interconnection, visualization and information interoperability, We can also apply multi-channel upgrading to upgrade our own hardware and software strength, observe and learn the quality level of the mode and class, so that the practical role of the means of mutual transmission of interconnected visual information can go on for a longer time.

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