A health-adaptive time-scale representation (HTSR) embedded convolutional neural network for gearbox fault diagnostics
TLDR
Wang et al. as discussed by the authors proposed a health-adaptive time-scale representation (HTSR) embedded CNN, which is designed to exploit the concept of TSR, informed by the physics of the time and frequency characteristics induced by the faultrelated signals.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2022-03-15 and is currently open access. It has received 16 citations till now. The article focuses on the topics: Convolutional neural network & Computer science.read more
Citations
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Dual-Weight Consistency-Induced Partial Domain Adaptation Network for Intelligent Fault Diagnosis of Machinery
TL;DR: Wang et al. as mentioned in this paper proposed a dual-weight consistency-induced reweighting (DCPDA) network, which consists of two feature extractors, a classifier, a domain discriminator, and a Wasserstein distance-based-DA module.
Journal ArticleDOI
Infrared Thermography Smart Sensor for the Condition Monitoring of Gearbox and Bearings Faults in Induction Motors
A. I. Alvarado-Hernandez,Israel Zamudio-Ramirez,Arturo Y. Jaen-Cuellar,Roque Alfredo Osornio-Rios,Vicente Donderis-Quiles,Jose A. Antonino-Daviu +5 more
TL;DR: The structure and development of an infrared-thermography-based smart sensor for diagnosing faults in the elements associated with induction motors, such as rolling bearings and the gearbox, is described.
Journal ArticleDOI
Dual-Weight Consistency-Induced Partial Domain Adaptation Network for Intelligent Fault Diagnosis of Machinery
TL;DR: In this article , a dual-weight consistency-induced partial domain adaptation (DCPDA) network is proposed for cross-domain fault diagnosis of machinery, in which the target and source label spaces are identical.
Journal ArticleDOI
Vibration, acoustic, temperature, and motor current dataset of rotating machine under varying operating conditions for fault diagnosis
TL;DR: In this paper , the authors presented the time-series dataset of rotating machines under varying operating conditions, including vibration, acoustic, temperature, and driving current data, which was acquired using four ceramic shear ICP based accelerometers, one microphone, two thermocouples, and three current transformer (CT) based on the ISO standard.
Journal ArticleDOI
A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
TL;DR: In this paper , a one-dimensional deep convolutional neural network (1D-DCNN) was proposed to learn features directly from the vibrational signals and identify the gear fault under different health conditions.
References
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