scholarly journals Predictive Maintenance in the Automotive Sector: A Literature Review

2021 ◽  
Vol 27 (1) ◽  
pp. 2
Author(s):  
Fabio Arena ◽  
Mario Collotta ◽  
Liliana Luca ◽  
Marianna Ruggieri ◽  
Francesco Gaetano Termine
Keyword(s):  
Literature Review ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Stochastic Methods ◽  
Automotive Sector ◽  
Network Technology ◽  
Challenges And Opportunities ◽  
Notable Increase ◽  
Useful Life ◽  
New Research

With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and artificial intelligence (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. In this scenario, this paper presents a systematic literature review of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance in the automotive sector. It provides a summary on these approaches, their main results, challenges, and opportunities, and it supports new research works for vehicle predictive maintenance.

scholarly journals Deployment of Prognostics to Optimize Aircraft Maintenance - A Literature Review

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jorben Pieter Sprong ◽  
Xiaoli Jiang ◽  
Henk Polinder
Keyword(s):  
Literature Review ◽  
Remaining Useful Life ◽  
Aircraft Maintenance ◽  
Purchase Price ◽  
Operational Costs ◽  
Historic Records ◽  
Challenges And Opportunities ◽  
Useful Life ◽  
The Cost ◽  
State Of Art

Historic records show that the cost of operating and supporting an aircraft may exceed the initial purchase price as much as ten times. Maintenance, repair and overhaul activities represent around 10-15% of an airlines’ operational costs. Therefore, optimization of maintenance operations is extremely important for airlines in order to stay competitive. Prognostics, a process to predict remaining useful life of systems and/ or components suffering from aging or degradation, has been recognized as a revolutionary discipline that can improve efficiency of aircraft operations and optimize the aircraft maintenance. In this paper, the origin and development of prognostics is firstly introduced. Thereafter, the state of art of aircraft maintenance is reviewed. Next, the applicability of prognostics to optimize aircraft maintenance is explained, reviewed, and potential challenges and opportunities are explored. Finally, some recommendations for further research are discussed in perspective of deployment of prognostics to optimize aircraft maintenance.

Overview of Remaining Useful Life Methodologies

2008 ◽  
Author(s):  
Yogesh G. Bagul ◽  
Ibrahim Zeid ◽  
Sagar V. Kamarthi
Keyword(s):  
Literature Review ◽  
Remaining Useful Life ◽  
Estimation Accuracy ◽  
Operational Cost ◽  
Useful Life ◽  
The Individual

Nowadays, it is imperative for products to function properly each time they are used. If a product fails during its use, it may have disastrous consequences. Estimating remaining useful life (RUL) of a product is a key to prevent such disasters, improve its reliability, provide security and reduce maintenance and operational cost. Naturally, estimation of RUL of a product and develop methodologies for the same is proving an interesting domain for researchers. This paper gives an overview of RUL estimation methodologies applied to various products. It also discusses hybrid methodologies which improve RUL estimation accuracy and overcome limitations of the individual methodologies. As this is an emerging area, these methodologies have been applied to only a handful of products. A list of these products is provided with references. A quantitative metric that measures the relative important characteristic differences among different methodologies is given. This paper concludes with few important points observed during literature review.

A Big Data implementation of the MANTIS reference architecture for predictive maintenance

2019 ◽  
Vol 233 (10) ◽  
pp. 1361-1375
Author(s):  
Felix Larrinaga ◽  
Javier Fernandez-Anakabe ◽  
Ekhi Zugasti ◽  
Iñaki Garitano ◽  
Urko Zurutuza ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Reference Architecture ◽  
Implementation Support ◽  
Physical Systems ◽  
Root Cause ◽  
Hadoop Distributed File System ◽  
Useful Life

This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.

scholarly journals Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 932
Author(s):  
Ziqiu Kang ◽  
Cagatay Catal ◽  
Bedir Tekinerdogan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Continuous Production ◽  
Case Study Research ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Production Lines ◽  
Important Concern ◽  
Proposed Model ◽  
Useful Life

Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

scholarly journals Semi-supervised Constrained Hidden Markov Model Using Multiple Sensors for Remaining Useful Life Prediction and Optimal Predictive Maintenance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xinyu Zhao ◽  
Yunyi Kang ◽  
Hao Yan ◽  
Feng Ju
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Multiple Sensors ◽  
Degradation Data ◽  
State Identification ◽  
Markov Decision ◽  
Useful Life

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we develop a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is effective in estimating the RUL, while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.

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