Applicability of Machine Learning Techniques to Forecast Remaining Useful Life on Excavator Structural Component John Deere

Format:

Book

Conference/Event

Author/Creator:

Velayudhan, Vinod Kumar, author.

Contributor:

Goyal, Rakesh

ISSRANI, MANOJ

Pawar, Sanket

Conference Name:

Off-Highway Technical Conference 2025 (2025-11-06 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Most of the major machines and structural components are designed for fatigue life and at same time it is important to design structural components for no premature fatigue failure. The performance of major machines and structural components are usually tested in controlled environment but in real life components are subjected to fluctuating loads known as fatigue loads which are common causes of failure. Fatigue cracks are common indicators of potential structural failure, and an early stage of crack initiation phase often goes undetected until noticeable performance degradation or failure to the component occurs resulting in a machine downtime. Early detection of Failure and understanding Remaining Useful Life (RUL) of a component is increasingly more important to customers as it helps in preventive maintenance by timely replacement of a component. This would also result in reducing costs by forecasting time to failure. With recent advancement in science, available data can be analyzed easily to build analytical models and developed a machine-based algorithm to learn from data, identify time to failure and make decisions with less human intervention. This paper aims to research the apparatus and methodology used to detect fatigue cracks and forecast RUL on Excavator Structural component

Notes:

Vendor supplied data

Publisher Number:

2025-28-0296

Access Restriction:

Restricted for use by site license