This paper presents a case study and a model to predict maintenance interventions based on condition monitoring of diesel engine oil in urban buses by accompanying the evolution of its degradation. Many times, under normal functioning conditions, the properties of the lubricants, based on the intervals that manufacturers recommend for its change, are within normal and safety conditions. Then, if the lubricants’ oil condition is adequately accompanied, until reaching the degradation limits, the intervals of oil replacement can be enlarged, meaning that the buses’ availability increases, as well as their corresponding production time. Based on this assumption, a mathematical model to follow and to manage the oil condition is presented, in order to predict the next intervention with the maximum time between them, which means the maximum availability.
Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.
The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.
An efficient management of physical assets in general and the urban transportation buses in particular, has as objective the optimization of its Life Cycle Cost (LCC), being the optimization of its maintenance and the determination of the right moment of its withdrawal or its renewal, essential elements for optimization of the capital investments. These aspects are related to the determination of the optimal dimension of the fleet reserve, aiming the maximization of its availability and the investment minimization. This can be reached through several algorithms that ought to be in consideration aspects like the maintenance costs, reseller cost, and inflation, among others. This is reached trough the adequate maintenance policies, being these scheduled and or on-condition predictive or any kind of planned ones. This type of decisions are determinant to maximize the LCC and, by consequence, the dimension of reserve fleet. It is through this perspective that the identification of the right moment of replacement or total renewal of an asset can be a strategic element in the competitiveness of the organizations by reducing the costs that can come from there. The validation of the management models to be implemented will be done in the road transports sector of passengers' transportation and the models and the results have the potential to help this strategic sector in the global economy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.