Condition Monitoring of an All-Terrain Vehicle Gear Train Assembly Using Deep Learning Algorithms with Vibration Signals

Sakthivel, G.; Jakkamputi, Lakshmipathi; Mohanraj, T.; Marikkannan, Senthil Kumar; Jegadeeshwaran, R.; Thangavelu, Kannan; Gangadhar, K.

doi:10.3390/app122110917

Cited by 5 publications

(5 citation statements)

References 31 publications

(35 reference statements)

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“…Condition monitoring of the transmission assembly was performed using vibration signals collected from an all-terrain vehicle (ATV) transmission. From the results of finite element analysis, they identified the defective parts of the gear [24][25][26] .…”

Section: Discussionmentioning

confidence: 99%

Finite Element Analysis for Improved All-Terrain Vehicle Component Design

Lalit N. Patil,

Atul A. Patil,

Kishor B. Waghulde

et al. 2023

Evergreen

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All-terrain vehicle (ATV) is an off-road vehicle that can be easily driven over any terrain. However, because to the severe stresses imposed by the gravel tracks and the weight limits, one critical component such as the Reduction Gearbox is prone to failure. The gearbox is an expensive component, and if it fails, the vehicle becomes immobilized. Therefore, the aim of the present work is to design, develop and analyze the ATV components for enhancement in life. These analytical designs have been validated using CAD models with FEA tools. Various Analyses like Structural, Repeated loads and Finite Element Analysis (FEA) has been performed to study the behaviour of components and oil flow during operations. The results shows that all the components are not prone to fail within the specified operating limits based on the analytical calculations and Finite Element Analysis.

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Section: Discussionmentioning

confidence: 99%

Finite Element Analysis for Improved All-Terrain Vehicle Component Design

Lalit N. Patil,

Atul A. Patil,

Kishor B. Waghulde

et al. 2023

Evergreen

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“…By averaging many decision trees applied to various subsets of the available data, RF is a classifier that raises the expected accuracy of the dataset. It employs predictions from each tree rather than just one, forecasting the outcome based on the votes of the majority of projections [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Natarajan

Mohanraj

Sakthivel

et al. 2023

Sensors

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Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

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“…Vibration sensors are usually preferred over others because they allow for the early detection of faults [23]. Furthermore, various machine learning techniques, often designated as artificial intelligence methods, have been applied to condition-based maintenance via vibration analysis [24][25][26][27]. For fault classification, these approaches prove highly valuable when sufficient data, including faulty data, are available.…”

Section: Classification Detectionmentioning

confidence: 99%

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial

Matania,

Bachar,

Bechhoefer

et al. 2024

Sensors

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One of the common methods for implementing the condition-based maintenance of rotating machinery is vibration analysis. This tutorial describes some of the important signal processing methods existing in the field, which are based on a profound understanding of the component’s physical behavior. Furthermore, this tutorial provides Python and MATLAB code examples to demonstrate these methods alongside explanatory videos. The goal of this article is to serve as a practical tutorial, enabling interested individuals with a background in signal processing to quickly learn the important principles of condition-based maintenance of rotating machinery using vibration analysis.

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Condition Monitoring of an All-Terrain Vehicle Gear Train Assembly Using Deep Learning Algorithms with Vibration Signals

Cited by 5 publications

References 31 publications

Finite Element Analysis for Improved All-Terrain Vehicle Component Design

Finite Element Analysis for Improved All-Terrain Vehicle Component Design

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial

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