Automated Gearbox Fault Diagnosis Using Entropy-Based Features in Flexible Analytic Wavelet Transform (FAWT) Domain

Ramteke, Dada Saheb; Pachori, Ram Bilas; Parey, Anand

doi:10.1007/s42417-021-00322-w

Cited by 21 publications

(9 citation statements)

References 34 publications

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“…It can obtain data from external or instrument drive module and transmit data to signal processing module for processing. When completion of the test, the test results are edited and printed through the affiliated function modules [10].…”

Section: System Software Operation Flowmentioning

confidence: 99%

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Mao,

Pang,

Jin

et al. 2024

J. Phys.: Conf. Ser.

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Traditional manual testing methods are no longer able to meet the maintenance needs of modern shipboard fire control system board-level circuits. Therefore, the development of intelligent and universal automatic test systems has become an important issue for shipboard fire control system fault detection. This article analyzes the performance and functional requirements of the automatic test system, develops the automatic test system based on PXI bus instruments, and designs an overall solution with signal time-frequency analysis capabilities that can carry out intelligent fault diagnosis for a variety of board-level circuits. On this basis, the design of hardware circuit and software solution is completed. The automatic test system was analyzed on hardware indicators and software functions. On the hardware side, the hardware resource selection was completed based on the hardware indicator requirements. On the software side, the overall software architecture design and the design of each functional module were completed based on the system software functional requirements. Finally, the composition and functions of each module of the system software and the overall operation process are introduced.

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Section: System Software Operation Flowmentioning

confidence: 99%

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Mao,

Pang,

Jin

et al. 2024

J. Phys.: Conf. Ser.

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“…The Flexible Analytic Wavelet Transform (FAWT) [39] is particularly effective for EEG feature extraction compared to other statistical methods due to its adaptive time-frequency analysis capabilities. Unlike the Fourier Transform, which only provides frequency information, or standard wavelet transforms with fixed time-frequency resolution, FAWT dynamically adjusts its analysis to match the local characteristics of EEG signals.…”

Section: Eeg Feature Extractionmentioning

confidence: 99%

“…The functional connectivity graph has been formed by using graph metrics like degree, betweenness centrality, etc., to characterize the network, where nodes represent ROIs and edges represent the strength of connectivity (e.g., correlation). In the end, the General Linear Model (GLM) [39] models the BOLD signal in each voxel as a linear combination of explanatory variables (like task conditions) and confounds: 𝑇𝑖(𝑡) = 𝛽0 + 𝛽1𝑋1(𝑡)+. .…”

Section: Eeg Feature Extractionmentioning

confidence: 99%

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Alrawis,

Al-Ahmadi,

Mohammad

2024

Applied Sciences

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Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

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“…There is a wide variety of EFs such as the energy entropy, the permutation entropy, the sample entropy, the approximate entropy, and the fuzzy entropy, just to mention a few; each one of these parameters provides different information that can be useful for the detection of a variety of faults in gearboxes such as wear, broken teeth, eccentricity, and imbalance, among others [ 28 , 29 ]. Moreover, it has been demonstrated that EFs are suitable for automatically detecting different faults in gearboxes if they are considered along with ML techniques and classification algorithms, such as support vector machines (SVMs) [ 30 ] and k-nearest neighbors (KNNs) [ 31 ]; and more recently with deep learning (DL) techniques, such as convolutional neural networks (CNNs) [ 32 ] and autoencoders [ 33 ]. However, despite DL techniques leading to achieving advantageous results, their implementation is associated with a high computational burden because of the processing of images (i.e., CNNs), and a priori knowledge is required to set specific hyperparameters (i.e., autoencoders).…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

Elvira-Ortiz

Saucedo-Dorantes

Osornio-Ríos

et al. 2023

Entropy

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Gears are reliable and robust elements that are found in any power transmission system. However, gears are prone to present incipient faults, such as wear, since they are constantly subjected to contact forces. Due to gears playing a key role in many industrial processes, it is important to develop condition monitoring strategies that ensure the proper functioning of the related power transmission system and the overall components. In this regard, the data on entropy provide relevant information that allow us to identify and quantify the effect of different wear levels in gears. Therefore, in this work, we proposed the use of seven entropy-related features to perform the identification of different wear severities in a gearbox. The novelty of this proposal lies in the use of the entropy features to carry out a high-performance characterization of the available vibration signals that are acquired from experimental tests. The novelty of this proposal lies in the fusion of three different techniques: entropy features, linear discriminant analysis, and artificial neural networks to obtain a machine learning approach for improving the detection of different wear severities in gears compared to other reported methodologies. This situation is achieved due to the high-performance characterization of the available vibration signals that are acquired from experimental tests. Additionally, the entropy features are subjected to a feature space transformation by means of linear discriminant analysis to obtain a 2D representation and, finally, the set of features extracted by linear discriminant analysis are used as inputs of a neural network-based classifier to determine the severity of wear that is present in the gears. The proposed methodology is validated and compared with a conventional statistical approach to show the improvement in the classification.

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Automated Gearbox Fault Diagnosis Using Entropy-Based Features in Flexible Analytic Wavelet Transform (FAWT) Domain

Cited by 21 publications

References 34 publications

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

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