Feature Extraction Using EMD and Classifier Through Artificial Neural Networks for Gearbox Fault Diagnosis

Malik, Hasmat; Pandya, Yogesh; Parashar, Aakash; Sharma, Rajneesh

doi:10.1007/978-981-13-1822-1_28

Cited by 24 publications

(14 citation statements)

References 12 publications

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“…A publicly available dataset already presented in the paper [ 56 ] was used in this work. The dataset includes vibration measurements from healthy and broken gearboxes under various loads and a constant rotating speed at 30 Hz.…”

Section: Materials and Methodsmentioning

confidence: 99%

A Machine Learning Approach for Gearbox System Fault Diagnosis

Vrba

Cejnek

Steinbach

et al. 2021

Entropy

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This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings.

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Section: Materials and Methodsmentioning

confidence: 99%

A Machine Learning Approach for Gearbox System Fault Diagnosis

Vrba

Cejnek

Steinbach

et al. 2021

Entropy

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“…Dataset 2 is a gear fault diagnosis dataset disclosed in [ 34 ]. The dataset includes vibration measurements on healthy and damaged gearboxes under various loads (0–90%, in 10% steps) and a constant rotational speed of 30 Hz.…”

Section: Datasetmentioning

confidence: 99%

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Huo

Kang

Wang

et al. 2022

Entropy

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The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

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“…This research is partially supported by the National Natural Science Foundation of China (52005103, 71801046, 51775112, 51975121), the Guangdong Basic and Applied Basic Research Foundation (2019B1515120095), and the MoST International Cooperation Program (6)(7)(8)(9)(10)(11)(12)(13)(14).…”

Section: Fundingmentioning

confidence: 99%

“…Chine et al [6] proposed a fault diagnostics approach for the photovoltaic system based on Artificial Neural Networks (ANNs). Malik et al [7] used Empirical Mode Decomposition (EMD) for feature extraction, and an ANN was trained using the extracted features for gearbox fault diagnostics. He et al [8] extracted statistical features from monitoring signals and Support Vector Machines (SVMs) were developed for fault diagnostics for the 3D printer.…”

Section: Introductionmentioning

confidence: 99%

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

Yang¹,

Gjorgjevikj²,

Long³

et al. 2020

Preprint

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Novelty detection is a challenging task for the machinery fault diagnosis. A novel fault diagnostic method is developed for dealing with not only diagnosing the known type of defect, but also detecting novelties, i.e. the occurrence of new types of defects which have never been recorded. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that it is able to accurately diagnose known types of defects, as well as to detect unknown defects, outperforming other state-of-the-art methods.

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Feature Extraction Using EMD and Classifier Through Artificial Neural Networks for Gearbox Fault Diagnosis

Cited by 24 publications

References 12 publications

A Machine Learning Approach for Gearbox System Fault Diagnosis

A Machine Learning Approach for Gearbox System Fault Diagnosis

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

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