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Aerospace
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Predictive Maintenance for Complex Systems—from Sensor Measurements to Prognostics to...
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Predictive Maintenance for Complex Systems—from Sensor Measurements to Prognostics to Maintenance Planning

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (16 December 2023) | Viewed by 16659

Special Issue Editors

Dr. Mihaela A. Mitici

E-Mail Website
Guest Editor
Faculty of Science, Utrecht University, Heidelberglaan 8, 3584 CS Utrecht, The Netherlands
Interests: predictive maintenance scheduling; mobility; reliability; AI; optimization; stochastic modeling
Dr. Matteo Davide Lorenzo Dalla Vedova

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10129 Turin, Italy
Interests: aerospace actuators; robots; applied mechanics; modeling and simulation; diagnostics; engineering; flap/slat actuation systems; FBG sensors; flight control systems; hydraulics; matlab simulink; mechatronics; on-board systems; prognostics; systems engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Adam F. Abdin

E-Mail Website
Guest Editor
CentraleSupélec, Paris Saclay University, 3 Rue Joliot Curie 2e ét, 91190 Gif-sur-Yvette, France
Interests: resilience optimization; aerospace systems modeling and optimization
Prof. Dr. Anne Barros

E-Mail Website1 Website2
Guest Editor
CentraleSupélec, Paris Saclay University, Gif-sur-Yvette, France
Interests: predictive maintenance; stochastic modelling; reliability; resilience analysis

Special Issue Information

Dear Colleagues,

Modern aircraft are equipped with multiple sensors that collect up to 2.5 terabytes of measurements every day. The health of wind turbines is continuously monitored by sensors and control systems. In recent years, the increasing availability of monitoring data and advancements in machine learning and AI have incentivized the development of Remaining-Useful-Life prognostics and novel maintenance planning models that integrate these prognostics.

This Special Issue focuses on advancements in predictive maintenance for complex systems where maintenance tasks are planned based on Remaining-Useful-Life (RUL) prognostics, anomaly detection, and/or the availability of spare components. Objectives to be considered are, for example, the minimization of maintenance costs, reliability guarantees, the minimization of asset downtime, and the efficient usage of spare parts. Research on the development of optimization models for predictive maintenance planning and simulations to evaluate the impact of prognostics on maintenance objectives are highly encouraged. Work on the development of probabilistic RUL prognostics and stochastic optimization for maintenance planning is also encouraged. Contributions on the development of Remaining-Useful-Life prognostics and diagnostics (model-based/machine learning/physics-based) are very welcome, together with discussions on the integration of these results into maintenance planning. Applications to be considered are, for example, aircraft systems, wind turbines, engines, and actuators.

Dr. Mihaela A. Mitici
Dr. Matteo Davide Lorenzo Dalla Vedova
Dr. Adam F. Abdin
Prof. Dr. Anne Barros
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • predictive maintenance planning
  • Remaining-Useful-Life prognostics (model-based/machine learning/physics-based)
  • Industry 4.0
  • advanced diagnostics
  • decision making under uncertainty
  • machine learning for predictive maintenance
  • cost analysis
  • reliability
  • management of spare parts
  • simulation and assessment of maintenance planning

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Published Papers (5 papers)

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Research

15 pages, 25726 KiB  
Article
A Method for Constructing Health Indicators of the Engine Bleed Air System Using Multi-Level Feature Extraction
by Zhaobin Duan, Xidan Cao, Fangyu Hu, Peng Wang, Xi Chen and Lei Dong
Aerospace 2023, 10(7), 645; https://doi.org/10.3390/aerospace10070645 - 18 Jul 2023
Cited by 1 | Viewed by 1552
Abstract
Traditional methods are unable to effectively assess the health status of engine bleed air systems. To address the limitation, this paper proposes a methodology for constructing health indicators using multi-level feature extraction. First, this approach involves data-level feature extraction from Quick Access Recorder [...] Read more.
Traditional methods are unable to effectively assess the health status of engine bleed air systems. To address the limitation, this paper proposes a methodology for constructing health indicators using multi-level feature extraction. First, this approach involves data-level feature extraction from Quick Access Recorder (QAR) data and employs a method of significance compensation to process the QAR data. Second, through unsupervised learning, the ResNet Deep Autoencoder (RDAE) is utilized to do the feature-level feature extraction from the processed data. This can solve the problem of lacking annotated data and obtain the health indicators of the engine bleed air system. Third, the method was experimented on one year of QAR data from a specific airline company. The results demonstrate that the RDAE approach achieves the best performance in constructing health indicators for the system. It achieves a miss rate of 0.0523 for the duct pressure of 5th stage bleed, reducing the miss rate by 0.2810 compared to Kernel Principal Component Analysis (KPCA). It also achieves a miss rate of 0 for the pre-cooler outlet temperature, reducing the miss rate by 0.0035 compared to the Deep Autoencoder (DAE). The results indicate that the proposed method provides a more effective assessment of the health status of the engine bleed air system. Full article
(This article belongs to the Special Issue Predictive Maintenance for Complex Systems—from Sensor Measurements to Prognostics to Maintenance Planning)
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17 pages, 3744 KiB  
Article
State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility
by Min Young Yoo, Jung Heon Lee, Joo-Ho Choi, Jae Sung Huh and Woosuk Sung
Aerospace 2023, 10(6), 550; https://doi.org/10.3390/aerospace10060550 - 8 Jun 2023
Cited by 1 | Viewed by 1739
Abstract
This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is [...] Read more.
This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate its state estimator. These profiles incorporate variations in battery power flow, namely, charge-depleting and charge-sustaining modes, during the different phases of the UAM’s flight, including take-off, cruise, and landing. Moreover, the current sensor bias is estimated and corrected concurrently with the SOC. An extended Kalman filter-based bias estimator is developed and experimentally validated using actual current measurements from a Hall sensor, which is prone to noise. With this correction, a SOC estimation error is consistently maintained at 2% or lower, even during transitions between operational modes. Full article
(This article belongs to the Special Issue Predictive Maintenance for Complex Systems—from Sensor Measurements to Prognostics to Maintenance Planning)
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18 pages, 1648 KiB  
Article
A Flexible Code Review Framework for Combining Defect Detection and Review Comments
by Xi Chen, Lei Dong, Hong-Chang Li, Xin-Peng Yao, Peng Wang and Shuang Yao
Aerospace 2023, 10(5), 465; https://doi.org/10.3390/aerospace10050465 - 16 May 2023
Cited by 1 | Viewed by 1860
Abstract
Defects and errors in code are different in that they are not detected by editors or compilers but pose a potential risk to software operation. For safety-critical software such as airborne software, the code review process is necessary to ensure the proper operation [...] Read more.
Defects and errors in code are different in that they are not detected by editors or compilers but pose a potential risk to software operation. For safety-critical software such as airborne software, the code review process is necessary to ensure the proper operation of software applications and even an aircraft. The traditional manual review method can no longer meet the current needs with the dramatic increase in code sizes and variety. To this end, we propose Deep Reviewer, a general and flexible code review framework that automatically detects code defects and correlates the review comments of the defects. The framework first preprocesses the data using several methods, including the proposed D2U flow. Then, features are extracted and matched by the detector, which contains a pair of twin LSTM models, one for code defect type detection and the other for review comment retrieval. Finally, the review comment output function is implemented based on the masks generated by the code defect types. The method is validated using a large public dataset, SARD. For the binary-classification task, the test results of the proposed are 98.68% and 98.67% in terms of precision and F1 score, respectively. For the multi-classification task, the proposed framework shows a significant advantage over other methods. Full article
(This article belongs to the Special Issue Predictive Maintenance for Complex Systems—from Sensor Measurements to Prognostics to Maintenance Planning)
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39 pages, 1810 KiB  
Article
Classification of Systems and Maintenance Models
by Vladimir Ulansky and Ahmed Raza
Aerospace 2023, 10(5), 456; https://doi.org/10.3390/aerospace10050456 - 15 May 2023
Cited by 2 | Viewed by 2471
Abstract
Maintenance is an essential part of long-term overall equipment effectiveness. Therefore, it is essential to evaluate maintenance policies’ effectiveness in addition to planning them. This study provides a classification of technical systems for selecting maintenance effectiveness indicators and a classification of maintenance models [...] Read more.
Maintenance is an essential part of long-term overall equipment effectiveness. Therefore, it is essential to evaluate maintenance policies’ effectiveness in addition to planning them. This study provides a classification of technical systems for selecting maintenance effectiveness indicators and a classification of maintenance models for calculating these indicators. We classified the systems according to signs, such as system maintainability, failure consequences, economic assessment of the failure consequences, and temporary mode of system use. The classification of systems makes it possible to identify 13 subgroups of systems with different indicators of maintenance effectiveness, such as achieved availability, inherent availability, and average maintenance costs per unit of time. When classifying maintenance models, we used signs such as the system structure in terms of reliability, type of inspection, degree of unit restoration, and external manifestations of failure. We identified one hundred and sixty-eight subgroups of maintenance models that differed in their values for specified signs. To illustrate the proposed classification of maintenance models, we derived mathematical equations to calculate all considered effectiveness indicators for one subgroup of models related to condition-based maintenance. Mathematical models have been developed for the case of arbitrary time-to-failure law and imperfect inspection. We show that the use of condition-based maintenance significantly increases availability and reduces the number of inspections by more than half compared with corrective maintenance. Full article
(This article belongs to the Special Issue Predictive Maintenance for Complex Systems—from Sensor Measurements to Prognostics to Maintenance Planning)
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17 pages, 1788 KiB  
Article
Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification
by Juseong Lee, Mihaela Mitici, Henk A. P. Blom, Pierre Bieber and Floris Freeman
Aerospace 2023, 10(2), 186; https://doi.org/10.3390/aerospace10020186 - 15 Feb 2023
Cited by 10 | Viewed by 6902
Abstract
The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into [...] Read more.
The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies. Full article
(This article belongs to the Special Issue Predictive Maintenance for Complex Systems—from Sensor Measurements to Prognostics to Maintenance Planning)
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