... maintenance activities. By integrating statistical modeling and machine learning techniques, the proposed framework aims to minimize unplanned downtimes, reduce maintenance costs and enhance operational efficiency. It addresses critical gaps in existing methods by providing real-time condition monitoring...

... types of shell-and-tube heat exchangers are being determined based on machine learning (ML) by results of more than 45 years inspection data at Lavan oil refinery. This study reviewed that the major factors consist of exchangers’ operating conditions with different media, process condition and exchanger...

..., categorising the enablers into four groups: organisation-related enablers, digital infrastructure and system support–related enablers, data-related enablers and people-related enablers. A machine learning-based BN technique was implemented to investigate the impact of the identified enablers on the adoption...

... rights only Digital twin Machine learning Remaining useful life Maintenance In this study, a RUL model for a turbofan engine was developed using the NASA PHM08 dataset. Preprocessing was completed, and RUL was determined for each turbofan engine based on its time cycle, as shown...

.... Originality/value This work contributes to the field by applying and validating state-of-the-art machine learning models for predictive maintenance in a real industrial setting. It offers a data-driven approach that aligns with Industry 4.0 practices and supports decision-making processes aimed at improving...

... seeks to demonstrate how machine learning techniques can be used to automatically identify crack-related faults based on vibration signals, contributing to improved reliability, early fault detection, and maintenance decision-making in engineering applications. Design/methodology/approach...

... in the semiconductor manufacturing industry. This system includes ultrasonic sensors and machine learning. Design/methodology/approach Employing ultrasonic sensors, physical and data-driven models are established. The time- or frequency-domain data acquired by the monitoring system are converted into cepstrums...

... are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines. Findings Based on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy...

...Imane Mjimer; Es-Saadia Aoula; E.L. Hassan Achouyab Purpose The aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed...

...Laura Isabel Alvarez Quiñones; Carlos Arturo Lozano-Moncada; Diego Alberto Bravo Montenegro Purpose The purpose of this paper is to describe a methodology that has been set up to schedule predictive maintenance of distribution transformers at Cauca Department (Colombia) using machine learning...

.... The first issue has been addressed applying data cleansing operations and creating ad hoc methodology to enlarge the training data. The second issue has been handled developing and comparing an empirical model with a machine learning (ML)-based model; the comparison has been performed assessing capabilities...

... use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance...

...Ravikumar KN; Hemantha Kumar; Kumar GN; Gangadharan KV Purpose The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques. Design/methodology/approach Vibration signals...

... work that involves engineering judgement and intuition, causing the output to have high variability. The purpose of this paper is to reduce the amount of time and output variability of corrosion loop development process by utilizing machine learning and group technology method. Design/methodology...