Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/8827
Title: Prediction and Assessment of LHD Machine Breakdowns Using Failure Mode Effect Analysis (FMEA)
Authors: Balaraju, J.
Govinda, Raj, M.
Murthy, C.S.N.
Issue Date: 2020
Citation: Lecture Notes in Mechanical Engineering, 2020, Vol., , pp.833-850
Abstract: Across the world, production industries are always searching for enhancement of productivity by producing the targeted level of production. In the mining industry, Load Haul Dumper (LHD) is one of the major production equipments generally utilized as an intermediate level technology-based transportation system. LHDs are prone to uneven modes of multiple failures/breakdowns due to harsh operating environmental conditions. This leads to a decrease in the performance of the equipment and increases the maintenance cost, the number of unplanned outages (downtime), as well as loss of production levels. This can be controlled by adequate prediction of machine failures through root cause analysis (RCA). In the present investigation, a well-known fault prediction technique, i.e., failure mode effect analysis (FMEA) was utilized to identify the modes of potential failure, causative factors and recognize the effects of these failures on performance and safety. The risk-based numerical assessment was made by prioritizing the failure modes through the risk priority number (RPN) model. The criticality of failure was estimated using RPN values. They are calculated by the product of risk indexed/ruled parameters [severity (S), occurrence (O) and detection (D)]. Further, an attempt has been made to suggest suitable remedial actions to reduce or eliminate the various potential failures. � 2020, Springer Nature Singapore Pte Ltd.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/8827
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.