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Fault Diagnosis and Fault-Tolerant Control with Applications to Robotics and Automation Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 11111

Special Issue Editors

Prof. Dr. Alessandro Freddi

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: fault diagnosis; fault-tolerant control; unmanned vehicles; robotics; assistive technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Felicetti

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: fault detection and diagnosis; fault-tolerant control; aerial robotics; optimization
Dr. Alessandro Baldini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: disturbance observers; fault detection and diagnosis; fault-tolerant control; unmanned vehicles

Special Issue Information

Dear Colleagues,

The research and development of robotic systems have experienced exponential growth in the last 50 years, which has led to the application domain of robots to be extended from industry to new areas such as medicine, exploration, service, and assistance.

The safety and reliability requirements for modern robotic and automation systems are increasing, and despite the presence of faults and failures, they must reach their goals without representing a danger to humans and plants.

In order to improve both the reliability and safety of such systems, it becomes of utmost importance to deal with fault and failures during the operational phase, by means of fault detection and isolation (FDI), fault diagnosis (FD), fault-tolerant control (FTC), fault prognosis (FP), and health-aware control (HAC), both model-based and data-driven. Thus, the aim of this SI is to gather contributions, from both the academic and professional worlds, that address the abovementioned challenges in robotic and automation systems.

Prof. Dr. Alessandro Freddi
Dr. Riccardo Felicetti
Dr. Alessandro Baldini
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • fault detection and isolation (FDI)
  • fault diagnosis (FD)
  • fault-tolerant control (FTC)
  • fault prognosis (FP)
  • health-aware control (HAC)
  • robotics
  • automation

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Published Papers (4 papers)

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Research

27 pages, 44996 KiB  
Article
A Novel Hierarchical Vision Transformer and Wavelet Time–Frequency Based on Multi-Source Information Fusion for Intelligent Fault Diagnosis
by Changfen Gong and Rongrong Peng
Sensors 2024, 24(6), 1799; https://doi.org/10.3390/s24061799 - 11 Mar 2024
Cited by 8 | Viewed by 1627
Abstract
Deep learning (DL) has been widely used to promote the development of intelligent fault diagnosis, bringing significant performance improvement. However, most of the existing methods cannot capture the temporal information and global features of mechanical equipment to collect sufficient fault information, resulting in [...] Read more.
Deep learning (DL) has been widely used to promote the development of intelligent fault diagnosis, bringing significant performance improvement. However, most of the existing methods cannot capture the temporal information and global features of mechanical equipment to collect sufficient fault information, resulting in performance collapse. Meanwhile, due to the complex and harsh operating environment, it is difficult to extract fault features stably and extensively using single-source fault diagnosis methods. Therefore, a novel hierarchical vision transformer (NHVT) and wavelet time–frequency architecture combined with a multi-source information fusion (MSIF) strategy has been suggested in this paper to boost stable performance by extracting and integrating rich features. The goal is to improve the end-to-end fault diagnosis performance of mechanical components. First, multi-source signals are transformed into two-dimensional time and frequency diagrams. Then, a novel hierarchical vision transformer is introduced to improve the nonlinear representation of feature maps to enrich fault features. Next, multi-source information diagrams are fused into the proposed NHVT to produce more comprehensive presentations. Finally, we employed two different multi-source datasets to verify the superiority of the proposed NHVT. Then, NHVT outperformed the state-of-the-art approach (SOTA) on the multi-source dataset of mechanical components, and the experimental results show that it is able to extract useful features from multi-source information. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control with Applications to Robotics and Automation Systems)
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23 pages, 4804 KiB  
Article
Fault-Tolerant Control of a Variable-Pitch Quadrotor under Actuator Loss of Effectiveness and Wind Perturbations
by Alessandro Baldini, Riccardo Felicetti, Alessandro Freddi and Andrea Monteriù
Sensors 2023, 23(10), 4907; https://doi.org/10.3390/s23104907 - 19 May 2023
Cited by 4 | Viewed by 1643
Abstract
The actuator fault-tolerant control problem for a variable-pitch quadrotor is addressed under uncertain conditions. Following a model-based approach, the plant nonlinear dynamics are faced with a disturbance observer-based control and a sequential quadratic programming control allocation, where only kinematic data of the onboard [...] Read more.
The actuator fault-tolerant control problem for a variable-pitch quadrotor is addressed under uncertain conditions. Following a model-based approach, the plant nonlinear dynamics are faced with a disturbance observer-based control and a sequential quadratic programming control allocation, where only kinematic data of the onboard inertial measurement unit are required for the fault-tolerant control, i.e., it does not require the measurement of the motor speed nor the current drawn by the actuators. In the case of almost horizontal wind, a single observer handles both faults and the external disturbance. The estimation of the wind is fed forward by the controller, while the actuator fault estimation is exploited in the control allocation layer, which copes with the variable-pitch nonlinear dynamics, thrust saturation, and rate limits. Numerical simulations in the presence of measurement noise show the capability of the scheme to handle multiple actuator faults in a windy environment. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control with Applications to Robotics and Automation Systems)
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16 pages, 7851 KiB  
Article
A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation
by Mengyang Li, Xinhong Hei, Wenjiang Ji, Lei Zhu, Yichuan Wang and Yuan Qiu
Sensors 2022, 22(23), 9438; https://doi.org/10.3390/s22239438 - 2 Dec 2022
Cited by 12 | Viewed by 2724
Abstract
In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, [...] Read more.
In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control with Applications to Robotics and Automation Systems)
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21 pages, 7379 KiB  
Article
Bearing Fault Diagnosis of Hot-Rolling Mill Utilizing Intelligent Optimized Self-Adaptive Deep Belief Network with Limited Samples
by Rongrong Peng, Xingzhong Zhang and Peiming Shi
Sensors 2022, 22(20), 7815; https://doi.org/10.3390/s22207815 - 14 Oct 2022
Cited by 6 | Viewed by 4207
Abstract
Given the complexity of the operating conditions of rolling bearings in the actual rolling process of a hot mill and the difficulty in collecting data pertinent to fault bearings comprehensively, this paper proposes an approach that diagnoses the faults of a rolling mill [...] Read more.
Given the complexity of the operating conditions of rolling bearings in the actual rolling process of a hot mill and the difficulty in collecting data pertinent to fault bearings comprehensively, this paper proposes an approach that diagnoses the faults of a rolling mill bearing by employing the improved sparrow search algorithm deep belief network (ISAA-DBN) with limited data samples. First, the fast spectral kurtosis approach is adopted to convert the non-stationary original vibration signals collected by the acceleration sensors installed at the axial and radial ends of the rolling mill bearings into two-dimensional (2D) spectral kurtosis time–frequency images with higher feature recognition, and the principal component analysis (PCA) technique is used to decrease the dimension of the data in order to achieve a high diagnosis rate with a limited number of samples. Subsequently, the sparrow search algorithm (SSA) is used to realize the intelligent optimized self-adaptive function of a deep belief network (DBN). Furthermore, the firefly disturbance algorithm is employed to improve the spatial search capability and robustness of SSA-DBN in order to achieve better performance of the ISSA-DBN method. Finally, the proposed approach is experimentally compared to other approaches used for diagnosis. The results show that the proposed approach not only retains the useful features of the data through dimension reduction but also improves the efficiency of the diagnosis and achieves the highest diagnosis accuracy with limited data samples. In addition, the optimal position of the sensor for diagnosing rolling mill roll faults is identified. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control with Applications to Robotics and Automation Systems)
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