Ensemble trees learning based improved predictive maintenance using IIoT for turbofan engines

Behera, Sourajit; Choubey, Anurag; Kanani, Chandresh S.; Patel, Yashwant Singh; Misra, Rajiv; Sillitti, Alberto

doi:10.1145/3297280.3297363

Cited by 24 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A predictive maintenance model was proposed to identify the most crucial attributes and the critical relationship among the attributes for fault detection of individual equipment(s) in the turbofan aircraft, which uses the data-driven prognostic method for RUL estimation with multiple operating conditions. Predictive maintenance is a prominent strategy that can achieve increased reliability and safety of CPS (cyber-physical systems) while attaining reduced maintenance cost by estimating the current health status and the remaining useful life (RUL) (Behera et al, 2019) Predictive maintenance is the method of scheduling maintenance based on the prediction about the failure time of any equipment. The prediction can be made by analyzing the data measurements from the equipment using Machine learning, a technology by which the outcomes can be predicted based on a model prepared by training it on past input data and its output behaviour.…”

Section: Literature Reviewmentioning

confidence: 99%

Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Azyus

2022

JCSTS

View full text Add to dashboard Cite

Predictive maintenance (PdM) is indicated state of the machine to perform a schedule of maintenance based on historical data, integrity factors, statistical inference methods, and engineering approaches that are currently often applied to aircraft maintenance. The Predictive maintenance on aircraft to avoid the worse event (failure) and get information about the status of aircraft machines by applied on Machine Learning (ML) to get high accuracy and precision. The research aims to look for the method and technique of ML, which is the best applied on PdM for aircraft in accuracy indicators. The techniques of ML have been divided by classification and regression, which are compared on three ML methods: Random Forest (RF), Support Vector Machine (SVM), and simple LSTM. The result of the study for classification technique are LSTM 98,7%, SVM 95,6%, and RF 900,3%. On other hand, Regression technique for ML result on MAE and RMSE are LSTM 13,55 and 22,13, SVM 15,77 and 20,51, RF 15,06 and 19,98. Classify technique is better and faster than regression when calculating the PdM on an aircraft engine. The LSTM method of ML is the best applied to it because of the accuracy higher and time process faster than other methods in this study. Finally, the LSTM method is highly recommended while using with classify technique on ML to determine the PdM on an aircraft engine.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Azyus

2022

JCSTS

View full text Add to dashboard Cite

show abstract

“…Notably, health status prediction research has been found to grow with the introduction of machine learning (ML) (Carvalho et al, 2019). Examples can be found in studies by Calabrese et al (2019), Behera et al (2019) and Selak et al (2014), which used artificial neural networks and decision trees, gradient-boosted trees and random forests, and support vector machine, respectively. Similarly, simulation is used to analyze faults and estimate the RUL (Guizzi et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Data-based decision-making in maintenance service delivery: the D3M framework

Sala

Bertoni

Pirola

et al. 2021

JMTM

View full text Add to dashboard Cite

PurposeThis paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.Design/methodology/approachThe Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.FindingsThe interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.Originality/valueThe paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

show abstract

“…PdM tasks have been fulfilled via another AI technique called Gradient Boosted Trees (GBT) in [13], [14]. In [13], the authors conducted a classification task on scheduling maintenance for railway systems.…”

Section: Related Workmentioning

confidence: 99%

“…In [13], the authors conducted a classification task on scheduling maintenance for railway systems. The authors of the study [14] performed RUL prediction for airline maintenance using GBT, based on internet of things with an RF algorithm as a baseline. Both [13] and [14] suggest GBTs as an alternative method to the classical RF algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…By conducting such a comparison, the study also aims to provide basics for the stakeholders in the production industries planning to adapt the rapidly growing PdM approach. The study first implements the five selected AI algorithms, namely Random Forest (RF) [11], [12], Gradient Boosted Tree (GBT) [13], [14], Light Gradient Boosting Machine (LightGBM) [15], [16], K-Nearest Neighbors (KNN) [17], [18], Multilayer Perceptron neural networks (MLP) [19], [20] and then compares different attributes of those algorithms. The study determines the RUL and detects anomalies for a specific machinery provided in the Microsoft AMLWorkshop dataset 1 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of State-of-the-Art Machine Learning Algorithms on Fault Indication and Remaining Useful Life Determination by Telemetry Data

Unal

Kaleli

Ummak³

et al. 2021

2021 8th International Conference on Future Internet of Things and Cloud (FiCloud)

View full text Add to dashboard Cite

Contemporary trends in the diffusion of artificial intelligence technologies has increased the number of studies on predictive maintenance, a recent focus of interest in many industrial domains. Despite the increased interest in the use of machine learning for predictive maintenance, few studies involve thorough comparisons of machine learning algorithms' performance on predictive maintenance applications. This work aims to predict the remaining useful life and machine failures and compares five different algorithms: Random Forest, Gradient Boosted Tree, K-Nearest Neighbors, Multilayer Perceptron and LightGBM. Our results suggest better performances for binary classification using Random Forest, and for regression using LightGBM compared to other selected algorithms.

show abstract

Ensemble trees learning based improved predictive maintenance using IIoT for turbofan engines

Cited by 24 publications

References 20 publications

Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Data-based decision-making in maintenance service delivery: the D3M framework

A Comparison of State-of-the-Art Machine Learning Algorithms on Fault Indication and Remaining Useful Life Determination by Telemetry Data

Contact Info

Product

Resources

About