Rolling BearingFaultDiagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network. Abstract: Due to the advantage of automatically extracting features from raw da…
Unlike traditional machine learning techniques, convolutional neural networks (CNNs), one of deep learning methods, automate the feature extraction process required for an effective classification…
This paper proposes an adaptive deep convolutionalneural network (ADCNN) that utilizes cyclic spectrum maps (CSM) of raw vibration signal as bearing health states to automate feature extra…
In this paper, we propose a new bearingfault diagnosis method without the feature extraction, based on ConvolutionalNeural Network (CNN). The 1-D vibration signal is converted to 2-D dat…
The convolutionalneural network (CNN), due to its advanced feature extraction ability of vibrational signals, has achieved promising results in bearingfault diagnosis. However, the worki…
Convolutionalneural networks. Load domain adaptation. Anti-noise. End-to-end. 1. Introduction. Machine health monitoring is of great importance in modern industry. Failure of these machines …
These methods contain support vector classifier (SVC), k-nearest neighbor (kNN), famous neural networks such as AlexNet, ResNet, and some of the latest methods used in the field of bearingf…
In order to analyze the effect of different vibration signal input modes for bearingfault identification using convolutionalneural network, the analysis procedure is divided into the following four parts…
Abstract. Fault diagnosis plays a vital role in the modern industry. In this research, a joint vibration signal analysis and deep learning method for fault diagnosis is proposed. The vibration signal an…
Abstract. ConvolutionalNeural Network (CNN) has been widely used in bearingfault diagnosis and many satisfying results have been achieved. However, nearly all the published researches...