Intelligent Diagnosis for Fuel Line Fault of Diesel Engine Based on Vibration Signatures John Deere India Pvt. Limited

Format:

Book

Conference/Event

Author/Creator:

Chaudhari, Parag, author.

Contributor:

Dharmadhikari, Nitin

Gangsar, Purushottam

Mandke, Devendra

Pawar, Sachin

Conference Name:

Symposium on International Automotive Technology (2024-01-23 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Early fault detection is vital in maintaining system stability and to decrease the cost associated with maintenance. This paper presents an approach to identify the fuel line failure for a diesel engine based on vibration signals and machine learning. Vibration measurements are performed on the fuel line of the engine for both normal and faulty conditions for engine ramp up condition. After acquiring the time domain vibration signals, various features were extracted and have been analyzed in time and time-frequency domains. Based on the most effective feature, a machine learning model (id est, support vector machine (SVM)) for fault diagnosis is developed. Results showed that the proposed SVM based model can detect the fuel line fault correctly. This study can be useful for early detection of this critical fault in diesel engine and take useful decision before any catastrophic failure happens because of this fault

Notes:

Vendor supplied data

Publisher Number:

2024-26-0221

Access Restriction:

Restricted for use by site license