Aircraft Fault Detection

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 19430

Special Issue Editor


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Guest Editor
Department of Mechanical, Automotive and Aerospace Engineering, Munich University of Applied Sciences, Lothstraße 34, 80335 München, Germany
Interests: fault detection and isolation; robust control; flight control; wind turbine control
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Special Issue Information

Dear Colleagues,

In recent decades, we have been pushing the field of fault detection for aircraft systems through outstanding practical and theoretical research with the aim being to bridge the gap between academia and industry to bring our approaches from the labs into real applications. To name a few achievements, this research has enabled the automation of flight systems in case of faults and a greener footprint of aircraft through highly optimized operation during fault scenarios and, ultimately, made manned and unmanned aerial vehicles more reliable and safer. However, we are well aware that this job is far from done.

The ongoing progress in the field of aviation, such as new urban mobility concepts, unmanned drones for reconnaissance purposes, the development of more electric aircraft, the usage of advanced materials in aircraft, the integration of smart subcomponents, enhanced flight control algorithms making use of novel actuation and sensing concepts, etc., calls for further research and development on sophisticated fault detection methods and algorithms to monitor airborne aviation systems in real time.

This Special Issue on Aircraft Fault Detection aims at collecting the newest research and developments trends in the field of aircraft fault detection, which may include:

  • The development of advanced linear and nonlinear model-based fault detection algorithms;
  • The use of signal and knowledge-based methods based on, e.g., machine learning techniques;
  • Active fault detection methods;
  • The combination of fault detection together with fault-tolerant control in aviation systems;
  • The validation of aircraft fault detection approaches in hardware-in-the-loop simulations or flight tests;
  • The development of nonlinear simulators including realistic fault models,

Submissions combining classical methods from fault detection and diagnosis with new methods from artificial intelligence are strongly encouraged. The fusion of both ideas has the great potential to further improve the performance and reliability of detection algorithms and make flying safer than ever before.

Prof. Dr. Daniel Ossmann
Guest Editor

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Published Papers (6 papers)

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Research

40 pages, 15738 KiB  
Article
Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades
by Jonas Aust, Dirk Pons and Antonija Mitrovic
Aerospace 2022, 9(1), 18; https://doi.org/10.3390/aerospace9010018 - 29 Dec 2021
Cited by 10 | Viewed by 3402
Abstract
Background—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry [...] Read more.
Background—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry practitioners was measured for 137 blade images, leading to N = 6850 observations. The data were statistically analysed to identify the significant factors. Subsequent evaluation of the eye tracking data provided additional insights into the inspection process. Results—Inspection accuracies in borescope inspections were significantly lower compared to piece-part inspection at 63.8% and 82.6%, respectively. Airfoil dents (19.0%), cracks (11.0%), and blockage (8.0%) were the most difficult defects to detect, while nicks (100.0%), tears (95.5%), and tip curls (89.0%) had the highest detection rates. The classification accuracy was lowest for airfoil dents (5.3%), burns (38.4%), and tears (44.9%), while coating loss (98.1%), nicks (90.0%), and blockage (87.5%) were most accurately classified. Defects of severity level S1 (72.0%) were more difficult to detect than increased severity levels S2 (92.8%) and S3 (99.0%). Moreover, visual perspectives perpendicular to the airfoil led to better inspection rates (up to 87.5%) than edge perspectives (51.0% to 66.5%). Background colour was not a significant factor. The eye tracking results of novices showed an unstructured search path, characterised by numerous fixations, leading to longer inspection times. Experts in contrast applied a systematic search strategy with focus on the edges, and showed a better defect discrimination ability. This observation was consistent across all stimuli, thus independent of the influence factors. Conclusions—Eye tracking identified the challenges of the inspection process and errors made. A revised inspection framework was proposed based on insights gained, and support the idea of an underlying mental model. Full article
(This article belongs to the Special Issue Aircraft Fault Detection)
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14 pages, 2617 KiB  
Article
Aircraft Assembly Snags: Human Errors or Lack of Production Design?
by Ageel Abdulaziz Alogla and Mansoor Alruqi
Aerospace 2021, 8(12), 391; https://doi.org/10.3390/aerospace8120391 - 10 Dec 2021
Cited by 5 | Viewed by 3405
Abstract
To err is an intrinsic human trait, which means that human errors, at some point, are inevitable. Business improvement tools and practices neglect to deal with the root causes of human error; hence, they ignore certain design considerations that could possibly prevent or [...] Read more.
To err is an intrinsic human trait, which means that human errors, at some point, are inevitable. Business improvement tools and practices neglect to deal with the root causes of human error; hence, they ignore certain design considerations that could possibly prevent or minimise such errors from occurring. Recognising this gap, this paper seeks to conceptualise a model that incorporates cognitive science literature based on a mistake-proofing concept, thereby offering a deeper, more profound level of human error analysis. An exploratory case study involving an aerospace assembly line was conducted to gain insights into the model developed. The findings of the case study revealed four different causes of human errors, as follows: (i) description similarity error, (ii) capture errors, (iii) memory lapse errors, and (iv) interruptions. Based on this analysis, error-proofing measures have been proposed accordingly. This paper lays the foundation for future work on the psychology behind human errors in the aerospace industry and highlights the importance of understanding human errors to avoid quality issues and rework in production settings, where labour input is of paramount importance. Full article
(This article belongs to the Special Issue Aircraft Fault Detection)
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14 pages, 3458 KiB  
Article
A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning
by Shenghan Zhou, Chaofan Wei, Pan Li, Anying Liu, Wenbing Chang and Yiyong Xiao
Aerospace 2021, 8(12), 357; https://doi.org/10.3390/aerospace8120357 - 23 Nov 2021
Cited by 5 | Viewed by 1613
Abstract
Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide [...] Read more.
Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide decision support for aircraft maintenance support work. Therefore, a text-based fault diagnosis model is proposed in this paper. The proposed method uses Word2vec to map text words into vector space, and the extracted text feature vectors are then input into the classifier based on a stacking ensemble learning scheme. Its performance has been validated using a real aircraft fault text dataset. The results show that the fault diagnosis accuracy of the proposed method is 97.35%, which is about 2% higher than that of the suboptimal method. Full article
(This article belongs to the Special Issue Aircraft Fault Detection)
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16 pages, 3040 KiB  
Article
An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record
by Linchao Yang, Guozhu Jia, Ke Zheng, Fajie Wei, Xing Pan, Wenbing Chang and Shenghan Zhou
Aerospace 2021, 8(11), 347; https://doi.org/10.3390/aerospace8110347 - 15 Nov 2021
Cited by 1 | Viewed by 1401
Abstract
At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults [...] Read more.
At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV. Full article
(This article belongs to the Special Issue Aircraft Fault Detection)
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23 pages, 4386 KiB  
Article
Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades
by Jonas Aust, Antonija Mitrovic and Dirk Pons
Aerospace 2021, 8(11), 313; https://doi.org/10.3390/aerospace8110313 - 22 Oct 2021
Cited by 8 | Viewed by 4393
Abstract
Background—In aircraft engine maintenance, the majority of parts, including engine blades, are inspected visually for any damage to ensure a safe operation. While this process is called visual inspection, there are other human senses encompassed in this process such as tactile perception. Thus, [...] Read more.
Background—In aircraft engine maintenance, the majority of parts, including engine blades, are inspected visually for any damage to ensure a safe operation. While this process is called visual inspection, there are other human senses encompassed in this process such as tactile perception. Thus, there is a need to better understand the effect of the tactile component on visual inspection performance and whether this effect is consistent for different defect types and expertise groups. Method—This study comprised three experiments, each designed to test different levels of visual and tactile abilities. In each experiment, six industry practitioners of three expertise groups inspected the same sample of N = 26 blades. A two-week interval was allowed between the experiments. Inspection performance was measured in terms of inspection accuracy, inspection time, and defect classification accuracy. Results—The results showed that unrestrained vision and the addition of tactile perception led to higher inspection accuracies of 76.9% and 84.0%, respectively, compared to screen-based inspection with 70.5% accuracy. An improvement was also noted in classification accuracy, as 39.1%, 67.5%, and 79.4% of defects were correctly classified in screen-based, full vision and visual–tactile inspection, respectively. The shortest inspection time was measured for screen-based inspection (18.134 s) followed by visual–tactile (22.140 s) and full vision (25.064 s). Dents benefited the most from the tactile sense, while the false positive rate remained unchanged across all experiments. Nicks and dents were the most difficult to detect and classify and were often confused by operators. Conclusions—Visual inspection in combination with tactile perception led to better performance in inspecting engine blades than visual inspection alone. This has implications for industrial training programmes for fault detection. Full article
(This article belongs to the Special Issue Aircraft Fault Detection)
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26 pages, 7369 KiB  
Article
Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach
by Juan Luis Pérez-Ruiz, Yu Tang and Igor Loboda
Aerospace 2021, 8(8), 232; https://doi.org/10.3390/aerospace8080232 - 22 Aug 2021
Cited by 15 | Viewed by 2872
Abstract
Considering the importance of continually improving the algorithms in aircraft engine diagnostic systems, the present paper proposes and benchmarks a gas-path monitoring and diagnostics framework through the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) software developed by NASA. The algorithm uses fleet-average and individual [...] Read more.
Considering the importance of continually improving the algorithms in aircraft engine diagnostic systems, the present paper proposes and benchmarks a gas-path monitoring and diagnostics framework through the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) software developed by NASA. The algorithm uses fleet-average and individual engine baseline models to compute feature vectors that form a fault classification with healthy and faulty engine classes. Using this classification, a hybrid fault-recognition technique based on regularized extreme learning machines and sparse representation classification was trained and validated to perform both fault detection and fault identification as a common process. The performance of the system was analyzed along with the results of other diagnostic frameworks through four stages of comparison based on different conditions, such as operating regimes, testing data, and metrics (detection, classification, and detection latency). The first three stages were devoted to the independent algorithm development and self-evaluation, while the final stage was related to a blind test case evaluated by NASA. The comparative analysis at all stages shows that the proposed algorithm outperforms all other diagnostic solutions published so far. Considering the advantages and the results obtained, the framework is a promising tool for aircraft engine monitoring and diagnostic systems. Full article
(This article belongs to the Special Issue Aircraft Fault Detection)
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