Year: 2024
Type: article
Abstract: ABSTRACT - Maintenance has always been an important function in any manufacturing operation.The main role of maintenance is to ensure that the manufacturing activities run at optimum conditionwithout any interruption. Several maintenance strategies have been introduced such as breakdownmaintenance, preventive maintenance, and condition-based maintenance. Condition-basedmaintenance (CBM) has become one of the popular maintenance strategies in manufacturingindustries. CBM is a maintenance program that is triggered by the machine deterioration condition.CBM serves two distinct purposes: diagnostic and prediction. The predictive segment largely focuses onforecasting the remaining useful life (RUL) of components. Presently, the focus of study is primarily onthe prediction of Remaining Useful Life (RUL). A point often overlooked, the effectiveness of thismaintenance strategy depends on the quality and integrity of observation data. There have been a fewreview papers on the broad scope of RUL prediction particularly in diagnostic techniques, but they lackfocus on the quality and integrity of the maintenance data. This paper provides a review on thesignificance of data integrity in RUL. This review covers publications from 2005 to 2024. It offers acategorisation of research fields or topics concerning data integrity and methods for data processing.Deep learning neural network models especially LSTM and CNN have been used extensively to treatdata integrity issues, particularly in have also gained popularity, especially in newer publications. Thisreview provides evidence on the importance of data integrity in RUL prediction, and it can be useful fornew researchers in the area.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7219925643