Ecological Predictive Maintenance of Diesel Engines

Abstract

The ecological predictive maintenance (EPM) of diesel engines is a great contribution to improve the environment and to stimulate good practices with good impact in the human health. The ecology is a rapidly developing scientific discipline with great relevance to a sustainable world, whose development is not complete as a mature theory. There are, however, general principles emerging that may facilitate the development of such theory. In the meantime, these principles can serve as useful guides for EPM. According to the state of the art, it can be stated that through prediction algorithms, the equipment's performance can be improved. To support this approach, it is necessary to implement a good condition monitoring maintenance. The result permits to maximise the time spacing between interventions and to increase the reliability levels. The condition variables of each equipment can be monitored according to their specificity, such as temperature, humidity, pollutant emissions (NO x ,CO 2 , HC and PM), emitted noise, etc. The environment where the equipment is inserted also must be considered. The assessment of the equipment's condition can be done by Hidden Markov Models (HMM), namely diesel engines. This chapter presents two algorithms-Viterbi and Baum-Welch algorithms-that, through the prediction of the equipment's condition, help to increase the efficiency of the maintenance planning.

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

“…Refs. [70][71][72][73][74][75][76][77][78][79][80][81][82][83][84] have shown various works applying statistical and probabilistic modeling approaches such as hidden Markov models (HMMs), Bayesian networks (BNs), Gaussian mixture models (GMMs), extreme gradient boosting (XGBoost), Density-based spatial clustering (DBSC), principal component analysis (PCA), and K-means to PdM tasks. Moreover, they introduced different DNN models, such as LSTM and autoencoders, for the tasks.…”