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Fault Detection and State Estimation in Automatic Control

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 18919

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Special Issue Editors

Prof. Dr. Sheng Du

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Guest Editor

School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Wei Wang

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Guest Editor

School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
Interests: machine learning; text mining; intelligent control

Dr. Hao Fu

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Guest Editor

School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: adaptive control; machine learning; nonlinear system

Prof. Dr. Xiongbo Wan

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Guest Editor

1. School of Automation, China University of Geosciences, Wuhan 430074, China
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
Interests: artificial intelligence; robust control of time-delay systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With level science and technology, the modern industrial production scale degree, modern scale, complexity, and degree of automation of the control system are greatly improved. Additionally, state estimation and fault detection are particularly important in the process of production if they can be achieved before the fault causes damage to the system; further, testing and maintenance can reduce the risk of accidents, improve system security, and reduce the economic loss of production. Therefore, the purpose of this Special Issue is to introduce the latest fault detection algorithms and state estimation methods.

This Special Issue aims to focus on intelligent control, intelligent modeling, computational intelligence, artificial intelligence, machine learning, and fault detection. This fits the scope of Applied Sciences as the practical applications of fault detection and machine learning are incredibly extensive and important.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Design and application of fault detection algorithms;
Design and application of state estimation methods;
Design and application of machine learning algorithms;
Automatic control system characteristics analysis;
Design and application of intelligent control systems.

We look forward to receiving your contributions.

Prof. Dr. Sheng Du
Dr. Wei Wang
Dr. Hao Fu
Prof. Dr. Xiongbo Wan
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

intelligent control
intelligent modeling
computational intelligence
artificial intelligence
machine learning
fault detection

Published Papers (14 papers)

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Editorial

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5 pages, 198 KiB

Open AccessEditorial

Fault Detection and State Estimation in Automatic Control

by Sheng Du, Wei Wang, Hao Fu and Xiongbo Wan

Appl. Sci. 2023, 13(23), 12936; https://doi.org/10.3390/app132312936 - 4 Dec 2023

Viewed by 746

Abstract

Fault detection and state estimation play pivotal roles in ensuring the reliability, safety, and performance of automatic control systems [...] Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

Research

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20 pages, 5175 KiB

Open AccessArticle

Study of the Relationships among the Reverse Torque, Vibration, and Input Parameters of Mud Pumps in Riserless Mud Recovery Drilling

by Guolei He, Benchong Xu, Haowen Chen, Rulei Qin, Changping Li and Guoyue Yin

Appl. Sci. 2023, 13(21), 11878; https://doi.org/10.3390/app132111878 - 30 Oct 2023

Viewed by 695

Abstract

Compared with traditional deepwater drilling, riserless mud recovery (RMR) drilling technology has the advantages of improving drilling efficiency, reducing risks, and minimizing environmental effects. Therefore, RMR drilling technology has been widely applied in recent years. This study primarily investigates the relationships among reverse torque, vibration, and input parameters of mud pumps in riserless mud recovery drilling. Firstly, the operating principle and the structure of the mud pump module are analyzed, and an analytical model for the reverse torque and the vibration of the mud pump is established. Secondly, relevant data are derived from theoretical calculations and experiments, and the relationships among the reverse torque, vibration, and input parameters of the mud pump are analyzed using ANSYS (Version 2020 R1) software. Furthermore, the SVR (support vector regression) algorithm is employed to predict and analyze the amplitude of the mud pump’s vibration. Finally, the conclusions are drawn based on the findings of the relationships among the reverse torque, vibration, and input parameters of the mud pump. The findings show that the reverse torque of the mud pump increases approximately linearly with an increase in rotational speed, and the vibration of the mud pump increases and then decreases with an increase in rotational speed. The predicted values obtained through the prediction algorithm closely match the actual values. The findings provide a valuable reference for the application of RMR technology. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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13 pages, 3062 KiB

Open AccessArticle

Body-Pose-Guided Action Recognition with Convolutional Long Short-Term Memory (LSTM) in Aerial Videos

by Sohaib Mustafa Saeed, Hassan Akbar, Tahir Nawaz, Hassan Elahi and Umar Shahbaz Khan

Appl. Sci. 2023, 13(16), 9384; https://doi.org/10.3390/app13169384 - 18 Aug 2023

Cited by 2 | Viewed by 1443

Abstract

The accurate detection and recognition of human actions play a pivotal role in aerial surveillance, enabling the identification of potential threats and suspicious behavior. Several approaches have been presented to address this problem, but the limitation still remains in devising an accurate and robust solution. To this end, this paper presents an effective action recognition framework for aerial surveillance, employing the YOLOv8-Pose keypoints extraction algorithm and a customized sequential ConvLSTM (Convolutional Long Short-Term Memory) model for classifying the action. We performed a detailed experimental evaluation and comparison on the publicly available Drone Action dataset. The evaluation and comparison of the proposed framework with several existing approaches on the publicly available Drone Action dataset demonstrate its effectiveness, achieving a very encouraging performance. The overall accuracy of the framework on three provided dataset splits is 74%, 80%, and 70%, with a mean accuracy of 74.67%. Indeed, the proposed system effectively captures the spatial and temporal dynamics of human actions, providing a robust solution for aerial action recognition. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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23 pages, 7642 KiB

Open AccessArticle

Quantitative Analysis of the Stability of a Mud-Return Circulation System in a Riserless Mud-Recovery Drilling System

by Rulei Qin, Qiuping Lu, Guolei He, Benchong Xu, Haowen Chen, Changping Li, Guoyue Yin, Jiarui Wang and Linqing Wang

Appl. Sci. 2023, 13(16), 9320; https://doi.org/10.3390/app13169320 - 16 Aug 2023

Viewed by 1133

Abstract

Riserless mud-recovery (RMR) drilling technology was widely applied in recent years. Compared with traditional deepwater drilling, RMR drilling can improve drilling efficiency, reduce risks, and minimize environmental effects. This paper focuses primarily on the stability of a mud-return circulation system in an RMR system. First, various factors that affect the stability of a mud-return circulation system are analyzed. An analytical model for the skid-and-mud-return line is established. Second, relevant data are derived from theoretical calculations and experiments. ABAQUS software is used to analyze the effects of each factor on the stability of the mud-return circulation system. The influencing patterns of each factor on the stability of the mud-return circulation system are summarized. Furthermore, the stability of the system under different operating conditions is analyzed based on the coupling of multiple factors. The support vector regression with derivative significance weight analysis (SVR-DWSA) algorithm is employed to perform a weight analysis of the effect on the system’s stability. Finally, based on the research findings on the stability of the mud-return circulation system, relevant conclusions and recommendations are drawn. The results of this study provide valuable references for the application of RMR technology. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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20 pages, 581 KiB

Open AccessArticle

Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops

by Xiaolei Du, Huabo Liu and Haisheng Yu

Appl. Sci. 2023, 13(15), 8778; https://doi.org/10.3390/app13158778 - 29 Jul 2023

Viewed by 882

Abstract

This paper investigates the robust fusion estimation problem for multi-sensor systems with communication constraints, parameter uncertainty, d-step state delays, and deterministic control inputs. The multi-sensor system consists of a fusion center and some sensor nodes with computational capabilities, between which there are random packet drops. The state augmentation method is utilized to transform a time-delay system into a non-time-delay one. The robust state estimation algorithm is derived based on the sensitivity penalty for each sensor node to reduce the impact of modelling errors, and modelling errors here are not limited to a unique form, which implies that the fusion estimator applies to a wide range of situations. An event-triggered transmission strategy has been adopted to effectively alleviate the communication burden from the sensor node to the fusion center. Moreover, the fusion estimator handles packet drops arising from unreliable channels, and the corresponding pseudo-cross-covariance matrix is provided. Some conditions are given to ensure that the estimation error of the robust fusion estimator is uniformly bounded. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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15 pages, 4071 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Models for Predicting Crack Propagation under Coupled Load and Temperature

by Intisar Omar, Muhammad Khan and Andrew Starr

Appl. Sci. 2023, 13(12), 7212; https://doi.org/10.3390/app13127212 - 16 Jun 2023

Cited by 3 | Viewed by 1492

Abstract

Crack propagation in materials is a complex phenomenon that is influenced by various factors, including dynamic load and temperature. In this study, we investigated the performance of different machine learning models for predicting crack propagation in three types of materials: composite, metal, and polymer. For composite materials, we used Random Forest Regressor, Support Vector Regression, and Gradient Boosting Regressor models, while for polymer and metal materials, we used Ridge, Lasso, and K-Nearest Neighbors models. We trained and tested these models using experimental data obtained from crack propagation tests performed under varying load and temperature conditions. We evaluated the performance of each model using the mean squared error (MSE) metric. Our results showed that the best-performing model for composite materials was Gradient Boosting Regressor, while for polymer and metal materials, Ridge and K-Nearest Neighbors models outperformed the other models. We also validated the models using additional experimental data and found that they could accurately predict crack propagation in all three materials with high accuracy. The study’s findings provide valuable insights into crack propagation behavior in different materials and offer practical applications in the design, construction, maintenance, and inspection of structures. By leveraging this knowledge, engineers and designers can make informed decisions to enhance the strength, reliability, and durability of structures, ensuring their long-term performance and safety. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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22 pages, 4665 KiB

Open AccessArticle

Fault Type Diagnosis of the WWTP Dissolved Oxygen Sensor Based on Fisher Discriminant Analysis and Assessment of Associated Environmental and Economic Impact

by Alexandra-Veronica Luca, Melinda Simon-Várhelyi, Norbert-Botond Mihály and Vasile-Mircea Cristea

Appl. Sci. 2023, 13(4), 2554; https://doi.org/10.3390/app13042554 - 16 Feb 2023

Cited by 3 | Viewed by 1588

Abstract

Sensor failures are common events in wastewater treatment plant (WWTP) operations, resulting in ineffective monitoring and inappropriate plant management. Efficient aeration control is typically achieved by the dissolved oxygen (DO) control, and its associated sensor becomes critical to the whole WWTP’s reliable and economical operation. This study presents the Fisher discriminant analysis (FDA) used for fault diagnosis of the DO sensor of a currently operating municipal WWTP. Identification of the bias, drift, wrong gain, loss of accuracy, fixed value, complete failure minimum and maximum types of DO sensor fault was investigated. The FDA-proposed methodology proved efficiency and promptitude in obtaining the diagnosis decision. The consolidated fault identification showed an accuracy of 87.5% correct identification of the seven faulty and normal considered classes. Depending on the fault type, the results of the diagnosing time varied from 2.5 h to 16.5 h during the very first day of the fault appearance and were only based on observation data not included in the training data set. The latter aspect reveals the potential of the methodology to learn from incomplete data describing the faults. The rank of the fault type detection promptitude was: bias, fixed value, complete failure minimum, complete failure maximum, drift, wrong gain and loss of accuracy. Greenhouse gases (GHGs) such as nitrous oxide (N₂O) and carbon dioxide (CO₂) emitted during wastewater treatment, electrical energy quantity in association with costs spent in the WWTP water line and clean water effluent quality were ranked and assessed for the normal operation and for each of the DO sensor faulty regimes. Both for CO₂ and N₂O, the on-site emissions showed the most significant GHG contribution, accounting for about three-quarters of the total emissions. The complete failure maximum, fixed value and loss of accuracy were the DO sensor faults with the highest detrimental impact on GHG-released emissions. The environmental and economic study reveals the incentives of the proposed DO sensor faults identification for the WWTP efficient and environmentally friendly operation. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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18 pages, 3807 KiB

Open AccessArticle

Multitarget Search Algorithm Using Swarm Robots in an Unknown 3D Mountain Environment

by You Zhou, Shaowu Zhou, Mao Wang and Anhua Chen

Appl. Sci. 2023, 13(3), 1969; https://doi.org/10.3390/app13031969 - 2 Feb 2023

Cited by 1 | Viewed by 1210

Abstract

A multitarget search algorithm for swarm robot in an unknown 3D mountain environment is proposed. Most existing 3D environment obstacle avoidance algorithms are potential field methods, which need to consider the location information of all obstacles around the robot, and they easily fall into local optima, and their calculation is complex. Furthermore, they cannot well meet the requirements of real-time obstacle avoidance characteristics of swarm robots in multiobject searches. This paper first focuses on solving the obstacle avoidance problem of swarm robots in mountain environments. A new 3D curved obstacle tracking algorithm (3D-COTA) is designed by discretizing the mountains within the detection range of robot obstacles. Then, a task assignment model and virtual force model in 2D space are extended to 3D, and a particle swarm search model with kinematic constraints is constructed, which considers the kinematic constraints and the limitations of the communication ability of the robots. Finally, a new multitarget search algorithm for swarm robot in an unknown 3D mountain environment is proposed by means of the designed 3D surface obstacle tracking algorithm. Numerical simulation results demonstrate the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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12 pages, 276 KiB

Open AccessArticle

Dissipativity Analysis of Large-Scale Networked Systems

by Yuanfei Sun, Jirong Wang and Huabo Liu

Appl. Sci. 2023, 13(2), 1214; https://doi.org/10.3390/app13021214 - 16 Jan 2023

Viewed by 1175

Abstract

This paper investigates the dissipativity analysis of large-scale networked systems with linear time-invariant dynamics. The networked system is composed of a large number of subsystems whose connections are arbitrary, and each subsystem can have different dynamics. A sufficient and necessary condition for the strict dissipativity analysis of the networked system is derived, which takes advantage of the block-diagonal structure of the system parameter matrix and the sparseness characteristics of the subsystem interconnections. Then, a necessary condition and a sufficient condition that depend only on a single subsystem parameter are given separately. Numerical simulations illustrate that compared with the existing results, the conditions suggested in this paper have higher computational efficiency in the dissipative analysis of large-scale networked systems. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

15 pages, 5072 KiB

Open AccessArticle

Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network

by Rui Zhou, Jiyin Cao, Gang Zhang, Xia Yang and Xinyu Wang

Appl. Sci. 2023, 13(2), 1099; https://doi.org/10.3390/app13021099 - 13 Jan 2023

Cited by 3 | Viewed by 1412

Abstract

High heat load on diesel engines is a main cause of ship failure, which can lead to ship downtime and pose a risk to personal safety and the environment. As such, predictive detection and maintenance measures are highly important. During the operation of marine diesel engines, operating data present strong dynamic, time lag, and nonlinear characteristics, and traditional models and prediction methods cause difficulties in accurately predicting the heat load. Therefore, the prediction of its heat load is a challenging and significant task. The continuously developing machine learning technology provides methods and ideas for intelligent detection and diagnosis maintenance. The prediction of diesel engine exhaust temperature using long short-term memory network (LSTM) is analyzed in this study to determine the diesel engine heat load and introduce an effective method. Spearman correlation coefficient method with the addition of artificial experience is utilized for feature selection to obtain the optimal input for the LSTM model. The model is applied to validate the ship data of the Shanghai Fuhai ship, and results show that the mean absolute percentage error (MAPE) of the model is lowest at 0.089. Compared with other models, the constructed prediction model presents higher accuracy and stability, as well as an optimal evaluation index. A new idea is thus provided for combining artificial knowledge experience with data-driven applications in engineering practice. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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16 pages, 4707 KiB

Open AccessArticle

Multi-UAV Cooperative Path Planning with Monitoring Privacy Preservation

by Yang Chen, Yifei Shu, Mian Hu and Xingang Zhao

Appl. Sci. 2022, 12(23), 12111; https://doi.org/10.3390/app122312111 - 26 Nov 2022

Cited by 4 | Viewed by 1033

Abstract

UAVs have shown great potential application in persistent monitoring, but still have problems such as difficulty in ensuring monitoring frequency and easy leakage of monitoring path information. Therefore, under the premise of covering all monitoring targets by UAVs, it is necessary to improve the monitoring frequency of the target and the privacy protection of the monitoring intention as much as possible. In response to the above problems, this research proposes monitoring overdue time to evaluate the monitoring frequency and monitoring period entropy in order to evaluate the ability to ensure monitoring privacy protection. It then establishes a multi-UAV cooperative persistent monitoring path planning model. In addition, the multi-group ant colony optimization algorithm, called overdue-aware multiple ant colony optimization (OMACO), is improved based on the monitoring overdue time. Finally, an optimal flight path for multi-UAV monitoring with high monitoring frequency and strong privacy preservation of monitoring intention is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node and the privacy preservation of the UAV monitoring path and has great significance for enhancing security monitoring and preventing intrusion. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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16 pages, 1444 KiB

Open AccessArticle

Altitude Control of Powered Parafoil Using Fractional Sliding-Mode Backstepping Control Combined with Extended State Observer

by Erlin Zhu, Youwu Du, Wei Song and Haitao Gao

Appl. Sci. 2022, 12(23), 12069; https://doi.org/10.3390/app122312069 - 25 Nov 2022

Cited by 1 | Viewed by 998

Abstract

This paper presents a method of altitude control of the powered parafoil with uncertainties and disturbances based on sliding-mode backstepping control combined with a linear extended state observer (LESO). First, the dynamics of a powered parafoil is derived in the longitudinal plane using its inclination angle. The problem of altitude control is converted to the issue of angle control. Next, uncertainties and disturbances are considered as a total disturbance. An LESO is used to estimate the total disturbance and form an inner-loop compensation. Backstepping control is employed to regulate the inclination angle to follow the desired value. A fractional sliding surface is introduced to the backstepping control. This ensures the transient performance of altitude control of the powered parafoil. Then, stability analysis shows that the observation errors of the LESO are bounded and the control system is uniformly ultimately bounded. Simulation results of an 8 degree-of-freedom powered parafoil illustrate that the LESO can effectively estimate the states of the system and demonstrate the validity and the superiority of the presented method. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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19 pages, 1816 KiB

Open AccessArticle

Model-Assisted Reduced-Order ESO Based Command Filtered Tracking Control of Flexible-Joint Manipulators with Matched and Mismatched Disturbances

by Changzhong Pan, Xiangyin Fei, Jinsen Xiao, Peiyin Xiong, Zhijing Li and Hao Huang

Appl. Sci. 2022, 12(17), 8511; https://doi.org/10.3390/app12178511 - 25 Aug 2022

Cited by 4 | Viewed by 1170

Abstract

Flexible-joint manipulators (FJMs) have been widely used in the fields of industry, agriculture, medical service, aerospace, etc. However, the FJMs in practical applications inevitably encounter various uncertainties including matched and mismatched disturbances. In this paper, we consider the high precision tracking control problem of FJMs in the presence of unknown lumped matched and mismatched disturbances. An efficient model-assisted composite control approach is proposed by integrating two reduced-order extended state observers (RESOs), a second-order command filtered backstepping (SCFB) technique and an error compensation dynamic system. Unlike some existing methods, the RESOs constructed with partial known model information are capable of estimating and compensating the matched and mismatched disturbances simultaneously. In addition, by employing the SCFB with an error compensation system, the proposed approach can not only overcome the problem of “explosion of complexity” inherent in backstepping, but also reduce the filtering errors arising from the command filters. The stability of the resulting control system and the convergence of error signals are guaranteed by Lyapunov stability theory. Comparative simulations are conducted for a single-link FJM with both matched and mismatched disturbances, and the results show that the proposed approach achieves a better tracking performance, i.e., compared with conventional backstepping method and adaptive fuzzy command filtered control method, the tracking accuracy is improved by 99.5% and 99.2%, respectively. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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Review

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18 pages, 2121 KiB

Open AccessReview

Review of Fault-Tolerant Control Systems Used in Robotic Manipulators

by Andrzej Milecki and Patryk Nowak

Appl. Sci. 2023, 13(4), 2675; https://doi.org/10.3390/app13042675 - 19 Feb 2023

Cited by 5 | Viewed by 2433

Abstract

Control systems that ensure robot operation during failures are necessary, particularly when manipulators are operating in hazardous or hard-to-reach environments. In such applications, fault-tolerant robot controllers should detect failures and, using fault-tolerant control methods, be able to continue operation without human intervention. Fault-tolerant control (FTC) is becoming increasingly important in all industries, including production lines in which modern robotic manipulators are used. The use of fault-tolerant systems in robotics can prevent the production line from being immobilized due to minor faults. In this paper, an overview of the current state-of-the-art methods of fault-tolerant control in robotic manipulators is provided. This review covers publications from 2003 to 2022. The article pays special attention to the use of artificial intelligence (AI), i.e., fuzzy logic and artificial neural networks, as well as sliding mode and other control methods, in the FTC of robotic manipulators. The cited and described publications were mostly found using Google Scholar. Full article

(This article belongs to the Special Issue Fault Detection and State Estimation in Automatic Control)

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