Yash Maurya - Academia.edu (original) (raw)

Yash Maurya

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Papers by Yash Maurya

Research paper thumbnail of Condition Monitoring and Diagnosis of an IC Engine using Vibration Recognition

Journal of emerging technologies and innovative research, Oct 1, 2021

This paper presents a model-based approach for the condition monitoring and fault diagnosis of an... more This paper presents a model-based approach for the condition monitoring and fault diagnosis of an IC engine using the MATLAB software. The vibration signatures of normally aspirated engine contain valuable information about the health of the engine. So, to extract these features a Simulink model consisting of an engine and gearbox is constructed and then the vibrational data has been recorded using an accelerometer connection. Healthy and fault induced data are extracted and are further processed to match the input requirements of the MATLAB Software libraries.21 significant features in both the time and spectral domain have been extracted using the diagnostic feature designer. Which are then used to train the classifier which results in a function, which can be used to predict the nature of new untested data signals.

Research paper thumbnail of Condition Monitoring and Diagnosis of an IC Engine using Vibration Recognition

Journal of emerging technologies and innovative research, Oct 1, 2021

This paper presents a model-based approach for the condition monitoring and fault diagnosis of an... more This paper presents a model-based approach for the condition monitoring and fault diagnosis of an IC engine using the MATLAB software. The vibration signatures of normally aspirated engine contain valuable information about the health of the engine. So, to extract these features a Simulink model consisting of an engine and gearbox is constructed and then the vibrational data has been recorded using an accelerometer connection. Healthy and fault induced data are extracted and are further processed to match the input requirements of the MATLAB Software libraries.21 significant features in both the time and spectral domain have been extracted using the diagnostic feature designer. Which are then used to train the classifier which results in a function, which can be used to predict the nature of new untested data signals.

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