An enhanced non-local weakly supervised fault diagnosis method for rotating machinery

Ruan, Hulin; Wang, Yi; Li, Xiaomeng; Qin, Yi; Tang, Baoping

doi:10.1016/j.measurement.2021.110433

Cited by 15 publications

(3 citation statements)

References 24 publications

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“…After convolution layer processing, the activation layer is used to perform a nonlinear transformation on its output convolution result to accelerate the convergence process of CNN and obtain the feature map of this convolution layer. The activation function selected is ReLU, whose main effect is to prevent gradient vanish in convolution layers [20].…”

Section: 2mentioning

confidence: 99%

Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

Liang¹,

Liu²,

Liu³

et al. 2024

MFC

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<p style='text-indent:20px;'>Satellite networking, as the future development direction of aero-space, requires high-precision autonomous fault diagnosis capability for a single satellite. In this paper, aiming at the characteristics of closed-loop fault propagation and high data dimensionality of spacecraft control system, neural network algorithms are conducted to study the fault diagnosis of spacecraft high-dimensional coupled data. Based on the ground test data of a certain spacecraft, this paper converts the high-dimensional sequence data into grayscale images, and then uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to diagnose them respectively. The effectiveness of the methods in this paper is illustrated by comparing and validating them with three non-image-based machine learning algorithms, namely, K-NearestNeighbor, Bayesian classifier, and K-NearestNeighbor based on Principal Component Analysis.</p>

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Section: 2mentioning

confidence: 99%

Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

Liang¹,

Liu²,

Liu³

et al. 2024

MFC

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“…According to the results of the trial, the accuracy was 94.05%. Ruan et al (2022) conducted two fault diagnosis experiments on ball bearings and bevel gears with 97.23% and 99.76% accuracy. Another example is a work presented by Kim et al (2021) with segmentation for an autonomous combine harvester.…”

Section: Weakly Supervised Learningmentioning

confidence: 99%

Ten deep learning techniques to address small data problems with remote sensing

Safonova¹,

Ghazaryan²,

Stiller³

et al. 2023

Preprint

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Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states. Such cases, also known as small data problems, pose significant methodological challenges. This review summarises these challenges in the RS domain and the possibility of using emerging DL techniques to overcome them. We show that the small data problem is a common challenge across disciplines and scales that results in poor model generalisability and transferability, yet this has not been investigated in a structured way. We first introduce ten emerging DL techniques: transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning; we also include a validation technique known as spatial k-fold cross validation. These techniques have shown promising potential in other scientific disciplines, but have been rarely applied in the RS domain. We also provide guidance on which learning technique to use in various cases, which helps to create a more methodologically robust DL application (and a greater number of them) that can be used to tackle socially important problems with limited data.

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“…Following this augmentation, they constructed a Residual Attention Convolutional Network (RA-CNN) for fault recognition. Ruan et al [20] proposed an improved fault diagnosis method using local weak supervision and non-local operations. This addresses low accuracy in sparse training datasets by enhancing convolutional neural networks to capture long-term dependencies during feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault Diagnosis of Rotating Machinery

Wang,

Dai,

Shi

et al. 2024

IEEE Access

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In practical engineering scenarios, machines are seldom in a faulty operating state, so it is difficult to get enough available sample data to train the fault diagnosis model, leading to the problem of the small and unbalanced number of rotating machinery fault samples and low fault diagnosis accuracy. To solve this problem, this paper introduces a novel approach to machinery fault diagnosis. This approach involves the integration of a Convolutional Attention Residual Network (CBAM-ResNet) with a Graph Convolutional Neural Network (GCN). Firstly, to comprehensively exploit time-domain information from one-dimensional vibration signals, this study utilize Gram Angular Field (GAF) coding to transform traits of vibration signals into two-dimensional image characteristics. The resultant two-dimensional image is then expanded by applying the Wasserstein Distance Gradient Penalty Generation Adversarial Network (WGAN-GP) to produce a representative sample image. Secondly, the image is input to CBAM-ResNet to perform focused feature extraction and construct the feature matrix. Lastly, the adjacency matrix is derived through Graph Generation Layer (GGL); subsequently, the feature matrix and adjacency matrix are utilized as inputs for the GCN. After deep feature extraction, fault feature classification is executed via Softmax. Performance tests were conducted using the Case Western Reserve University bearing dataset and the planetary gearbox dataset. The method demonstrated remarkable results, achieving an accuracy of over 99% on the unbalanced dataset and surpassing 98% in 0dB noise compared to various other models. This illustrates the effectiveness and feasibility of the proposed method.

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An enhanced non-local weakly supervised fault diagnosis method for rotating machinery

Cited by 15 publications

References 24 publications

Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

Ten deep learning techniques to address small data problems with remote sensing

Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault Diagnosis of Rotating Machinery

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