Topic Editors

Dr. Ziquan Yu
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Prof. Dr. Youmin Zhang
Department of Mechanical, Industrial and Aerospace Engineering, Concordia Institute of Aerospace Design and Innovation, Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, QC H3G 1M8, Canada

Perspectives in Fault Diagnosis and Fault Tolerant Control

Abstract submission deadline
closed (31 August 2023)
Manuscript submission deadline
closed (30 November 2023)
Viewed by
24095

Topic Information

Dear Colleagues,

Many highly complex unmanned autonomous systems (e.g., unmanned aerial vehicles, unmanned ground vehicles, unmanned surface vehicles, unmanned underwater vehicles, unmanned airships, unmanned aerial manipulators, satellites, spacecraft systems), intelligent equipment systems, and high-precision components have been developed to improve task execution efficiency. The increased complexity renders these systems and components susceptible to faults, which may significantly degrade the operational performance or even cause catastrophic accidents. Even though fault detection and diagnosis (FDD) and fault-tolerant control (FTC) strategies have been widely investigated to estimate and attenuate the adverse effects caused by faults, FDD and FTC investigations have still faced major challenges with increasing safety requirements. Many new factors now have to be considered that have not yet been addressed. Therefore, the aim of this Topic is to bring together original research articles and review articles in the field of FDD and FTC technology and promote related techniques in the proposed areas.

This Topic provides a platform for worldwide researchers to present their research works related to the theory and practical applications for FDD and FTC. Topics of interest include but are not limited to the following:

  • Model-based FDD design;
  • Data-driven FDD design;
  • Intelligent FDD design;
  • Condition monitoring;
  • Prognostics and health management;
  • FTC design against various constraints;
  • Intelligent fuzzy/neural/neural fuzzy adaptive FTC design;
  • Adaptive reinforcement learning for FTC;
  • Fractional-order FTC;
  • Fault-tolerant guidance and control;
  • Integrated design of FDD and FTC;
  • Safe control of systems against actuator, sensor, communication attacks, etc.

Dr. Ziquan Yu
Prof. Dr. Youmin Zhang
Topic Editors

Keywords

  • fault detection and diagnosis
  • system identification
  • fault detection and isolation
  • condition monitoring
  • prognostics and health management
  • fault-tolerant control
  • safe and reliable control
  • intelligent control
  • consensus control

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Aerospace
aerospace
2.6 3.0 2014 22.3 Days CHF 2400
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400
Drones
drones
4.8 6.1 2017 17.9 Days CHF 2600
Machines
machines
2.6 2.1 2013 15.6 Days CHF 2400
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600
Actuators
actuators
2.6 3.2 2012 16.7 Days CHF 2400
Automation
automation
- - 2020 26.3 Days CHF 1000
Vibration
vibration
2.0 3.5 2018 21.3 Days CHF 1600

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (14 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
18 pages, 3801 KiB  
Article
A Boundary Scan Test Vectors Optimization Method Based on Improved GA-AO* Approach Considering Fault Probability Model
by Yuanzhang Su, Xinfeng Guo, Hang Luo, Jingyuan Wang and Zhen Liu
Appl. Sci. 2024, 14(6), 2410; https://doi.org/10.3390/app14062410 - 13 Mar 2024
Viewed by 301
Abstract
The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors [...] Read more.
The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment. Full article
Show Figures

Figure 1

16 pages, 6007 KiB  
Article
Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
by Yi He, Yunan Yao and Hongsen Ou
Appl. Sci. 2024, 14(4), 1371; https://doi.org/10.3390/app14041371 - 07 Feb 2024
Viewed by 414
Abstract
Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, [...] Read more.
Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions. Full article
Show Figures

Figure 1

16 pages, 7348 KiB  
Article
Improving the Efficiency of the Drive of the Test Bench of Rotary Hydraulic Machines
by Alexander Rybak, Besarion Meskhi, Dmitry Rudoy, Anastasiya Olshevskaya, Yuliya Serdyukova, Svetlana Teplyakova and Alexey Pelipenko
Actuators 2024, 13(2), 63; https://doi.org/10.3390/act13020063 - 06 Feb 2024
Viewed by 1061
Abstract
Volumetric hydraulic drive systems are quite widespread in many industrial sectors. To determine the degree of reliability of hydraulic machines, it is necessary to conduct resource tests. The main requirement for such tests is the compliance of the load level with the operating [...] Read more.
Volumetric hydraulic drive systems are quite widespread in many industrial sectors. To determine the degree of reliability of hydraulic machines, it is necessary to conduct resource tests. The main requirement for such tests is the compliance of the load level with the operating mode of the hydraulic machine. Analysis of existing methods of creating such a load showed a significant drawback of bench tests—the lack of useful work. Therefore, a number of authors suggest the use of stands with a regenerative drive system. One peculiarity of the work of such stands is the possibility of returning part of the spent energy back to the test system. However, such systems are insufficiently studied and have significant drawbacks. The purpose of this work is to increase the efficiency of the regenerative drive system of the test bench of volumetric hydraulic machines of rotational action by improving the theory and methodology of its calculation and design. This article describes the principle of operation of the circuit of the regenerative drive of the test bench of rotary hydraulic machines. A model of the elastic-dissipative state of the sections of the elements of the hydro-mechanical drive system of the stand is also proposed, which allows for the calculation of the structural and energy parameters of the regenerative hydro-mechanical system. The main structural and functional parameters affecting the operational performance of the system as a whole are also identified. A “test efficiency coefficient” is proposed, which allows for evaluation of the energy efficiency of the test process. Full article
Show Figures

Figure 1

16 pages, 12794 KiB  
Article
Disturbance Observer-Enhanced Adaptive Fault-Tolerant Control of a Quadrotor UAV against Actuator Faults and Disturbances
by Xinyue Hu, Ban Wang, Yanyan Shen, Yifang Fu and Ni Li
Drones 2023, 7(8), 541; https://doi.org/10.3390/drones7080541 - 21 Aug 2023
Viewed by 1149
Abstract
For a quadrotor unmanned aerial vehicle (UAV), this paper proposes an adaptive sliding mode control (SMC) strategy enhanced with a disturbance observer to attain precise trajectory and attitude tracking performance while compensating for the detrimental impacts of actuator faults and disturbances. First, an [...] Read more.
For a quadrotor unmanned aerial vehicle (UAV), this paper proposes an adaptive sliding mode control (SMC) strategy enhanced with a disturbance observer to attain precise trajectory and attitude tracking performance while compensating for the detrimental impacts of actuator faults and disturbances. First, an adaptive SMC strategy that utilizes an integral sliding surface is presented to enhance the fault-tolerance capabilities of the studied quadrotor UAV against actuator faults. In addition, a disturbance observer is further created to compensate for the disturbances. By integrating the proposed adaptive SMC strategy with the designed disturbance observer, both actuator faults and disturbances can be effectively accommodated. It was theoretically demonstrated that the system is stable while using the proposed adaptive fault-tolerant control strategy. The effectiveness and benefits of the proposed strategy is verified with comparative simulation results under different faulty scenarios. Full article
Show Figures

Figure 1

22 pages, 3875 KiB  
Article
Fault Detection and Fault-Tolerant Cooperative Control of Multi-UAVs under Actuator Faults, Sensor Faults, and Wind Disturbances
by Zhongyu Yang, Mengna Li, Ziquan Yu, Yuehua Cheng, Guili Xu and Youmin Zhang
Drones 2023, 7(8), 503; https://doi.org/10.3390/drones7080503 - 01 Aug 2023
Viewed by 1075
Abstract
Fault detection (FD) and fault-tolerant cooperative control (FTCC) strategies are proposed in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) under actuator faults, sensor faults, and wind disturbances. Firstly, the faulty model is introduced while the effectiveness loss, deviation of thrust throttle [...] Read more.
Fault detection (FD) and fault-tolerant cooperative control (FTCC) strategies are proposed in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) under actuator faults, sensor faults, and wind disturbances. Firstly, the faulty model is introduced while the effectiveness loss, deviation of thrust throttle setting, and pitot sensor faults are considered. Secondly, the faulty UAV model with wind disturbances is linearized and the system is then converted into two subsystems by using state and output transformations. Further, cooperative unknown input observers (UIOs) are developed to estimate the faults, disturbances, and states. By combining with the observers’ estimations, adaptive thresholds are designed to detect actuator and sensor faults in the system. Then, considering state constraints, a backstepping-based FTCC scheme is proposed for multiple UAVs (multi-UAVs) suffering from actuator faults, sensor faults, and wind disturbances. It is shown by Lyapunov analysis that the tracking errors are fixed-time convergent. Finally, the effectiveness of the FD and FTCC scheme is verified by numerical simulation. Full article
Show Figures

Figure 1

31 pages, 10664 KiB  
Article
A Universal Feature Extractor Based on Self-Supervised Pre-Training for Fault Diagnosis of Rotating Machinery under Limited Data
by Zitong Yan, Hongmei Liu, Laifa Tao, Jian Ma and Yujie Cheng
Aerospace 2023, 10(8), 681; https://doi.org/10.3390/aerospace10080681 - 30 Jul 2023
Viewed by 1191
Abstract
To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully leverage the potential of easily obtainable unlabeled data [...] Read more.
To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully leverage the potential of easily obtainable unlabeled data from other devices. In light of this, this paper proposes a novel network architecture, named Signal Bootstrap Your Own Latent (SBYOL), which utilizes unlabeled vibration signals to address the challenging issues of variable working conditions, strong noise, and limited data in rotating machinery fault diagnosis. The architecture consists of a self-supervised pre-training-based fault feature recognition network and a diagnosis network based on knowledge transfer. The fault feature recognition network uses ResNet-18 as the backbone network for self-supervised pre-training and transfers the trained fault feature extractor to the target diagnostic object. Additionally, a unique vibration signal data augmentation technique, time–frequency signal transformation (TFST), is proposed specifically for rotating machinery fault diagnosis, which addresses the key task of contrastive learning and achieves high-precision fault diagnosis with very few labeled samples. Experimental results demonstrate that the proposed diagnostic model outperforms other methods in both extremely limited sample and strong noise scenarios and can transfer unlabeled data utilization between similar and even different device types. Full article
Show Figures

Figure 1

21 pages, 9353 KiB  
Article
A Vision-Based Autonomous Landing Guidance Strategy for a Micro-UAV by the Modified Camera View
by Lingxia Mu, Qingliang Li, Ban Wang, Youmin Zhang, Nan Feng, Xianghong Xue and Wenzhe Sun
Drones 2023, 7(6), 400; https://doi.org/10.3390/drones7060400 - 16 Jun 2023
Viewed by 2328
Abstract
Autonomous landing is one of the key technologies for unmanned aerial vehicles (UAVs) which can improve task flexibility in various fields. In this paper, a vision-based autonomous landing strategy is proposed for a quadrotor micro-UAV based on a novel camera view angle conversion [...] Read more.
Autonomous landing is one of the key technologies for unmanned aerial vehicles (UAVs) which can improve task flexibility in various fields. In this paper, a vision-based autonomous landing strategy is proposed for a quadrotor micro-UAV based on a novel camera view angle conversion method, fast landing marker detection, and an autonomous guidance approach. The front-view camera of the micro-UAV video is first modified by a new strategy to obtain a top-down view. By this means, the landing marker can be captured by the onboard camera of the micro-UAV and is then detected by the YOLOv5 algorithm in real time. The central coordinate of the landing marker is estimated and used to generate the guidance commands for the flight controller. After that, the guidance commands are sent by the ground station to perform the landing task of the UAV. Finally, the flight experiments using DJI Tello UAV are conducted outdoors and indoors, respectively. The original UAV platform is modified using the proposed camera view angle-changing strategy so that the top-down view can be achieved for performing the landing mission. The experimental results show that the proposed landing marker detection algorithm and landing guidance strategy can complete the autonomous landing task of the micro-UAV efficiently. Full article
Show Figures

Figure 1

20 pages, 5313 KiB  
Review
Review of Launch Vehicle Engine PHM Technology and Analysis Methods Research
by Ruliang Lin, Jialin Yang, Lijing Huang, Zhiwen Liu, Xuehua Zhou and Zhiguo Zhou
Aerospace 2023, 10(6), 517; https://doi.org/10.3390/aerospace10060517 - 30 May 2023
Cited by 2 | Viewed by 4805
Abstract
The reliability and safety of launch vehicle launch missions might be effectively increased thanks to the fault prediction and health management (PHM) technology of engines, which could also improve with problem diagnostics and decrease the cost of operation and maintenance overhaul. This paper [...] Read more.
The reliability and safety of launch vehicle launch missions might be effectively increased thanks to the fault prediction and health management (PHM) technology of engines, which could also improve with problem diagnostics and decrease the cost of operation and maintenance overhaul. This paper combines the equipment characteristics and the current state of safeguarding for large, complex space systems, introduces the intelligent launch vehicle engine PHM technology methods that are being gradually implemented in space systems, and discusses and compares fault detection and health assessment techniques. Subsequently, analysis of the measurement signals from a rocket engine was performed using an example, and it was shown that the established comprehensive health assessment structure, which is based on the fault prediction algorithm method and the fuzzy comprehensive assessment method, could successfully realize the effectiveness of the rocket engine system health assessment, which had an outstanding application value. Full article
Show Figures

Figure 1

18 pages, 3088 KiB  
Article
A Multilayered and Multifactorial Health Assessment Method for Launch Vehicle Engine under Vibration Conditions
by Ruliang Lin, Lijing Huang, Zhiwen Liu, Xuehua Zhou and Zhiguo Zhou
Aerospace 2023, 10(6), 505; https://doi.org/10.3390/aerospace10060505 - 27 May 2023
Cited by 1 | Viewed by 985
Abstract
Sixty percent of the failures of launch vehicles in the ascending phase occur in the propulsion system. Among them, the vibration generated by the engine is an important factor in the occurrence of failure. At present, health assessment methods in the aerospace field [...] Read more.
Sixty percent of the failures of launch vehicles in the ascending phase occur in the propulsion system. Among them, the vibration generated by the engine is an important factor in the occurrence of failure. At present, health assessment methods in the aerospace field are mostly for specific equipment, and scholars mostly assess the real-time health status of launch vehicle engines which can only reflect the current health status of the launch vehicle. Existing methods cannot be applied to different equipment, and there is a lack of research on health assessments of fuzzy and complex mechanical systems. In this article, we propose a multi-layer and multi-factor predictive evaluation method for a fuzzy and complex system and conduct experiments on real vibration data of rockets. First, we divide the health assessment level according to the vibration data that affect the normal operation of the rocket. Secondly, we obtain the future trend of vibration signals based on five data prediction methods and calculate the health status interval of the rocket engine’s working conditions based on the boxplot method. At the same time, we calculate the single health evaluation set of every vibration signal. We obtain the weights of each level and factor for the health value based on an analytic hierarchy process (AHP). The optimization of this step avoids an over-reliance on expert experience. Finally, we complete a fuzzy comprehensive evaluation of the engine system from the bottom up to obtain the final health value. The minimum evaluation error is 0.0193% on the test data of the Long March series launch vehicle engine, which shows that the proposed method can successfully predict and evaluate the launch vehicle engine. Full article
Show Figures

Figure 1

17 pages, 8548 KiB  
Article
A Flexible Dynamic Reliability Simulation Approach for Predicting the Lifetime Consumption of Extravehicular Spacesuits during Uncertain Extravehicular Activities
by Yuehang Sun, Yun-Ze Li and Man Yuan
Aerospace 2023, 10(5), 485; https://doi.org/10.3390/aerospace10050485 - 20 May 2023
Viewed by 1067
Abstract
The special use environment and uncertainty of extravehicular activities (EVAs) make it difficult to predict the lifetime consumption of extravehicular spacesuits in the traditional way. This paper presents a flexible reliability dynamic simulation model to predict the life loss of extravehicular spacesuits. Based [...] Read more.
The special use environment and uncertainty of extravehicular activities (EVAs) make it difficult to predict the lifetime consumption of extravehicular spacesuits in the traditional way. This paper presents a flexible reliability dynamic simulation model to predict the life loss of extravehicular spacesuits. Based on the images of traditional reliability change curves, new life assessment parameters, based on geometric analysis, are proposed as indicators of spacesuit life loss. Multiple influence factors are used to correct the spacesuit failure rate. The results of the study show that mission intensity is the main factor affecting the health status of the spacesuit, and the higher the mission intensity, the higher the failure rate. Additionally, the more frequently the spacesuit is used, the more times it is available, however, the overall service time will decrease. Concentrating on the mission at an early stage would lead to a significant and irreversible loss of life. Reliability is higher when more intense work is scheduled later in the EVA. Therefore, it is important to rationalize the mission duration, frequency, and work intensity of spacesuits. These reliability models predict the health status of the spacesuit and assist in optimizing the scheduling of EVA. Full article
Show Figures

Figure 1

19 pages, 5105 KiB  
Article
A Method for Satellite Component Health Assessment Based on Multiparametric Data Distribution Characteristics
by Yongchao Hui, Yuehua Cheng, Bin Jiang and Lei Yang
Aerospace 2023, 10(4), 356; https://doi.org/10.3390/aerospace10040356 - 04 Apr 2023
Cited by 2 | Viewed by 1373
Abstract
This research presents a novel data-based multi-parameter health assessment method to meet the growing need for the in-orbit health assessment of satellite components. This method analyzed changes in component health status by calculating distribution deviations and variation similarities in real-time operational data. Firstly, [...] Read more.
This research presents a novel data-based multi-parameter health assessment method to meet the growing need for the in-orbit health assessment of satellite components. This method analyzed changes in component health status by calculating distribution deviations and variation similarities in real-time operational data. Firstly, a single-parameter health state description method based on data distribution characteristics was presented. Secondly, the main health characteristic parameters were selected by mechanistic analysis and expert experience. The CRITIC method and the entropy weighting method were fused to assign reasonable weights and establish a multi-parameter component health assessment model. Then, the feasibility of a component health assessment algorithm based on data distribution characteristics was verified using real telemetry data from satellites. Finally, to verify the rationality of the presented health assessment algorithm, the results were compared with the pre-processed original data using empirical mode decomposition. The experimental results show that the method can accurately describe the change trend of the health status of the components. It proves that the method can be effectively used for the real-time health condition assessment and monitoring of satellite components. Full article
Show Figures

Figure 1

15 pages, 4034 KiB  
Article
Predefined-Time Heading Control for a 9-DOF Parafoil Recovery System Subject to Internal Relative Motions
by Yiming Guo, Jianguo Yan, Xiaojun Xing, Xiwei Wu and Lingwei Li
Aerospace 2023, 10(4), 348; https://doi.org/10.3390/aerospace10040348 - 03 Apr 2023
Viewed by 1461
Abstract
This paper addresses the challenging problem of predefined-time heading control of a parafoil recovery system (PRS) with internal relative motions and external disturbance. On the basis of the PRS described by a 9-degree-of-freedom model, a simplification and equivalent model is first derived, which [...] Read more.
This paper addresses the challenging problem of predefined-time heading control of a parafoil recovery system (PRS) with internal relative motions and external disturbance. On the basis of the PRS described by a 9-degree-of-freedom model, a simplification and equivalent model is first derived, which is convenient to design control law. Then, a predefined-time disturbance observer is provided to estimate the lumped disturbance caused by internal relative motions and apparent mass. With the application of the disturbance estimation, a predefined-time heading controller is developed for the PRS. The control system is proven to be predefined-time stable by Lyapunov theory. Simulation results illustrate that the proposed method has better control performance than finite-time and PID controllers. Full article
Show Figures

Figure 1

20 pages, 7106 KiB  
Article
An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features
by Luttfi A. Al-Haddad and Alaa Abdulhady Jaber
Drones 2023, 7(2), 82; https://doi.org/10.3390/drones7020082 - 24 Jan 2023
Cited by 28 | Viewed by 2744
Abstract
As a modern technological trend, unmanned aerial vehicles (UAVs) are extensively employed in various applications. The core purpose of condition monitoring systems, proactive fault diagnosis, is essential in ensuring UAV safety in these applications. In this research, adaptive health monitoring systems perform blade [...] Read more.
As a modern technological trend, unmanned aerial vehicles (UAVs) are extensively employed in various applications. The core purpose of condition monitoring systems, proactive fault diagnosis, is essential in ensuring UAV safety in these applications. In this research, adaptive health monitoring systems perform blade balancing fault diagnosis and classification. There seems to be a bidirectional unpredictability within each, and this paper proposes a hybrid-based transformed discrete wavelet and a multi-hidden-layer deep neural network (DNN) scheme to compensate for it. Wide-scale, high-quality, and comprehensive soft-labeled data are extracted from a selected hovering quad-copter incorporated with an accelerometer sensor via experimental work. A data-driven intelligent diagnostic strategy was investigated. Statistical characteristics of non-stationary six-leveled multi-resolution analysis in three axes are acquired. Two important feature selection methods were adopted to minimize computing time and improve classification accuracy when progressed into an artificial intelligence (AI) model for fault diagnosis. The suggested approach offers exceptional potential: the fault detection system identifies and predicts faults accurately as the resulting 91% classification accuracy exceeds current state-of-the-art fault diagnosis strategies. The proposed model demonstrated operational applicability on any multirotor UAV of choice. Full article
Show Figures

Figure 1

19 pages, 5621 KiB  
Article
Oil Particle-Induced Erosion Wear on the Deflector Jet Servo Valve Prestage
by Na Liang, Zhaohui Yuan and Fuli Zhang
Aerospace 2023, 10(1), 67; https://doi.org/10.3390/aerospace10010067 - 09 Jan 2023
Cited by 3 | Viewed by 1532
Abstract
Severe oil particle-induced erosion to the prestage component progressively degrades the overall performance of the deflector jet servo valve (DJSV), even leading to valve failure. Herein, we present an approach for evaluating degradation in performance and predicting the erosion lifespan of the DJSV [...] Read more.
Severe oil particle-induced erosion to the prestage component progressively degrades the overall performance of the deflector jet servo valve (DJSV), even leading to valve failure. Herein, we present an approach for evaluating degradation in performance and predicting the erosion lifespan of the DJSV on different levels of oil pollution. Specifically, a mathematical model of the whole valve was built based on a previously established working principle and physical mechanism. In addition, considering the horizontal and rotational particle motions, combined with impact of particle size distributions under different oil contamination degrees, an erosion model was constructed. Then, after simulating and analyzing the pressure characteristics before and after the erosion of prestage, the performance degradation of the whole valve was examined, thereby predicting the erosion life of the valve. Investigations revealed that the maximum erosion rate occurred at the shunt wedge of the receiving holes, which increased with the contamination degree and accelerated after level 7. After erosion, however, the control pressure difference decreased significantly, and erosion life followed exponential distribution corresponding to the distribution of particles under different pollution levels. The aforementioned investigation can thus help diagnose faults and optimize the design of the servo valves in service. Full article
Show Figures

Figure 1

Back to TopTop