Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

Abdul, Zrar Kh.; Al-Talabani, Abdulbasit K.

doi:10.1007/s42417-022-00768-6

Cited by 9 publications

(3 citation statements)

References 50 publications

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“…A set of weights were used as the parameters for the linear combination expression. These weights were applied to each layer ( Abdul & Al-Talabani, 2022 ). The last fully-connected layer divided the node outputs into as many nodes as there were classes in the classification task, which, in this case, was 2,214 classes.…”

Section: Methodsmentioning

confidence: 99%

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Asaad

Maghdid

2023

PeerJ Computer Science

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The Internet-of-Things (IoT) has been used with greater frequency to track peoples’ daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

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Section: Methodsmentioning

confidence: 99%

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Asaad

Maghdid

2023

PeerJ Computer Science

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“…However, with the development of machine learning technology, data-driven fault diagnosis methods can be used to adaptively extract fault-related information from the planetary gearbox vibration signal [11] and achieve fault classification efficiently and accurately. That is, the gearbox vibration signals under different fault states are collected by signal acquisition equipment, combined with various signal processing methods, and then machine learning algorithms such as support vector machines [12], artificial neural networks [13], and random forests [14] are used to obtain the mapping relationship between signal characteristics and fault categories. Ding et al [15] established a convolutional depth belief network for planetary gearbox fault diagnosis and introduced a particle swarm algorithm to optimize the hyperparameters of the network.…”

Section: Introductionmentioning

confidence: 99%

Planetary gearbox fault diagnosis based on FDKNN-DGAT with few labeled data

Tao,

Shi,

Qiu

et al. 2023

Meas. Sci. Technol.

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Although data-driven methods have been widely used in planetary gearbox fault diagnosis, the difficulty and high cost of manual labeling leads to little labeled training data, which limits the classification performance of traditional data-driven methods. Therefore, the semi-supervised fault diagnosis method with few labeled samples becomes one of the main research directions. Graph attention network (GAT) is distinguished from traditional classification network by using graph structure for fault node information aggregation and feature extraction, which is an effective semi-supervised learning algorithm. This paper uses fast Fourier transform to process the original vibration signal of gearbox and use it as graph nodes, and propose a KNN graph construction method using pooling for fuzzy distance calculation. In addition, this paper improves the distribution of attention weights by introducing dynamic graph attention networks to correct the problem that classical static GATs cannot clearly distinguish the weights of different categories of nodes. Experiments show that the method proposed in this paper can better extract fault features in complex gearbox vibration signals with an accuracy of more than 99% with very few labeled samples, and has better diagnostic performance compared with other graph neural network architectures and traditional classification networks.

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“…Additionally, in [13], the authors found that deep learning performed better than other machine learning methods to classify normal and diseased cells. In [14], long short-term memory (LSTM) is also used as a classifcation method to gear fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

Omer

Ghafoor

Askar

2022

Mobile Information Systems

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In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms.

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Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

Cited by 9 publications

References 50 publications

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Planetary gearbox fault diagnosis based on FDKNN-DGAT with few labeled data

An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

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