Submit your Manuscript Submit your Abstract Edit a Topic

Topic Menu

Topic Editors

Dr. Ziquan Yu

E-Mail Website

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Prof. Dr. Youmin Zhang

E-Mail Website

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

31 August 2023

Manuscript submission deadline

30 November 2023

Viewed by

4847

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	2.660	3.4	2014	21.8 Days	1800 CHF	Submit
Applied Sciences applsci	2.838	3.7	2011	14.9 Days	2300 CHF	Submit
Drones drones	5.532	7.2	2017	13.6 Days	2000 CHF	Submit
Machines machines	2.899	3.1	2013	16.2 Days	2000 CHF	Submit
Sensors sensors	3.847	6.4	2001	15 Days	2400 CHF	Submit

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (6 papers)

Download All Papers

Order results

Result details

Journals

Show export options Show export options

Select all

Export citation of selected articles as:

Open AccessArticle

A Multilayered and Multifactorial Health Assessment Method for Launch Vehicle Engine under Vibration Conditions

and

Aerospace 2023, 10(6), 505; https://doi.org/10.3390/aerospace10060505 - 27 May 2023

Viewed by 192

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 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

(This article belongs to the Topic Perspectives in Fault Diagnosis and Fault Tolerant Control)

► Show Figures

Figure 1

Open AccessArticle

A Flexible Dynamic Reliability Simulation Approach for Predicting the Lifetime Consumption of Extravehicular Spacesuits during Uncertain Extravehicular Activities

Yuehang Sun

Yun-Ze Li

and

Man Yuan

Aerospace 2023, 10(5), 485; https://doi.org/10.3390/aerospace10050485 - 20 May 2023

Viewed by 359

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 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

(This article belongs to the Topic Perspectives in Fault Diagnosis and Fault Tolerant Control)

► Show Figures

Figure 1

Open AccessArticle

A Method for Satellite Component Health Assessment Based on Multiparametric Data Distribution Characteristics

and

Aerospace 2023, 10(4), 356; https://doi.org/10.3390/aerospace10040356 - 04 Apr 2023

Viewed by 522

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, 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

(This article belongs to the Topic Perspectives in Fault Diagnosis and Fault Tolerant Control)

► Show Figures

Figure 1

Open AccessArticle

Predefined-Time Heading Control for a 9-DOF Parafoil Recovery System Subject to Internal Relative Motions

and

Aerospace 2023, 10(4), 348; https://doi.org/10.3390/aerospace10040348 - 03 Apr 2023

Viewed by 734

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 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

(This article belongs to the Topic Perspectives in Fault Diagnosis and Fault Tolerant Control)

► Show Figures

Figure 1

Open AccessArticle

An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features

Luttfi A. Al-Haddad

and

Alaa Abdulhady Jaber

Drones 2023, 7(2), 82; https://doi.org/10.3390/drones7020082 - 24 Jan 2023

Cited by 1 | Viewed by 1204

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 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

(This article belongs to the Topic Perspectives in Fault Diagnosis and Fault Tolerant Control)

► Show Figures

Figure 1

Open AccessArticle

Oil Particle-Induced Erosion Wear on the Deflector Jet Servo Valve Prestage

Na Liang

Zhaohui Yuan

and

Fuli Zhang

Aerospace 2023, 10(1), 67; https://doi.org/10.3390/aerospace10010067 - 09 Jan 2023

Viewed by 833

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 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

(This article belongs to the Topic Perspectives in Fault Diagnosis and Fault Tolerant Control)

► Show Figures

Figure 1

Show export options Show export options

Select all

Export citation of selected articles as:

Displaying articles 1-6

Submit your Abstract

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.660	3.4	2014	21.8 Days	1800 CHF	Submit
Applied Sciences applsci	2.838	3.7	2011	14.9 Days	2300 CHF	Submit
Drones drones	5.532	7.2	2017	13.6 Days	2000 CHF	Submit
Machines machines	2.899	3.1	2013	16.2 Days	2000 CHF	Submit
Sensors sensors	3.847	6.4	2001	15 Days	2400 CHF	Submit

Topic Menu

Topic Editors

Perspectives in Fault Diagnosis and Fault Tolerant Control

Topic Information

Keywords

Participating Journals

Published Papers (6 papers)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI