Fault Detection and State Estimation in Automatic Control

1. Introduction

Fault detection and state estimation play pivotal roles in ensuring the reliability, safety, and performance of automatic control systems. Recently, the integration of advanced methodologies with cutting-edge technologies has significantly impacted the fields of fault detection and state estimation. Particularly with advancements in artificial intelligence, the fusion of deep learning and ensemble methods, such as K -nearest neighbors, random forest regressors, and support vector regression, has garnered considerable attention. These robust, artificial-intelligence-driven approaches have been developed for intricate action recognition, predictive models for fault identification, and fault-tolerant control in complex, multi-sensor systems.

Amidst the challenges presented by packet drops, delays, and the complexities of large-scale networked systems, the pursuit of fault detection and state estimation has ventured into innovative domains. From empowering swarm robots with multitarget search capabilities to developing resilient prediction models capable of navigating uncertainties, this field stands at the forefront of innovation. The integration of these methodologies not only enhances the system’s resilience but also ensures its adaptability to unforeseen disturbances.

These studies transcend traditional boundaries, immersing themselves in the domain of Swarm Robots and Multitarget Searches within intricate, interconnected environments. Furthermore, they unveil the pivotal role of fault detection and state estimation in guaranteeing the functionality of automated systems across various industries and sectors.

Fault detection and state estimation stand as imperative tasks for ensuring the reliability, safety, and performance of automatic control systems. Nevertheless, these endeavors encounter numerous challenges, including nonlinear dynamics, uncertain disturbances, incomplete information, sensor faults, and computational complexity. Hence, there is a pressing requirement for novel methods and algorithms capable of surmounting these obstacles and delivering precise, robust solutions.

One promising avenue involves delving into the potential of machine learning and artificial intelligence techniques for fault detection and state estimation [1,2], with a specific emphasis on reinforcement learning (RL) [3]. These techniques possess the capacity to learn from data and adapt to dynamic environments, thereby enhancing the fault diagnosis and state estimation capabilities of automatic control systems [4]. For instance, RL can be employed to craft intelligent fault detection and diagnosis methods that optimize the delicate balance between detection accuracy and timeliness [5]. Neural networks are valuable tools for approximating nonlinear functions and estimating unknown states and parameters [6]. Deep learning, on the other hand, proves instrumental in extracting features and patterns from high-dimensional data, thereby improving fault classification [7].

Another prospective direction involves the integration of diverse methods and models for fault detection and state estimation. This integration enhances the robustness and reliability of solutions by leveraging the strengths of different approaches. For instance, an interval analysis can adeptly handle uncertain state perturbations and measurement noise by computing guaranteed bounds on state estimates [8]. Kalman filtering facilitates the fusion of multiple information sources and continuously updates state estimates based on measurement data [9]. Hybrid systems provide a modeling framework for capturing interactions between continuous and discrete dynamics and events. This facilitates control, verification, state estimation, and fault detection in complex systems [10].

Nevertheless, numerous challenges still require attention, including scalability, computational efficiency, online implementation, fault isolation, fault recovery, and fault-tolerant control. Hence, additional research efforts are imperative to propel this critical field forward, both in theory and practice.

2. An Overview of Published Articles

With the current state of science and technology, the modern industrial production scale and the complexity of automation in control systems have greatly improved. Additionally, state estimation and fault detection are particularly important in the production process of these systems before a fault causes any damage; further, testing and maintenance can reduce the risk of accidents, improve the system’s 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 focuses on intelligent control, intelligent modeling, computational intelligence, artificial intelligence, machine learning, and fault detection. This fits within the scope of Applied Sciences, as the practical applications of fault detection and machine learning are incredibly extensive and important. The research areas of this Special Issue include (but are not limited to) the design and application of fault detection algorithms, state estimation methods, machine learning algorithms, intelligent control systems, and analyzing the characteristics of automatic control systems.

An analysis of the papers published in this Special Issue is shown in

Table 1. Analysis of the published contributions in the Special Issue.

No.	DOI	Research Area	Focus	Type of Research	Industry	Country
S1	10.3390/ app132111878	Automation Engineering	riserless mud recovery technology, mud pump, ANSYS software, SVR algorithm	Mathematical Modeling	Geological Exploration	China
S2	10.3390/ app13169384	Aircraft Control	deep neural network, convolutional LSTM, action recognition, body pose keypoints; aerial surveillance	Mathematical Modeling	Automation	Pakistan
S3	10.3390/ app13169320	Automation Engineering	riserless mud-recovery technology, ABAQUS software, SVR-DSWA algorithm	Mathematical Modeling	Geological Exploration	China
S4	10.3390/ app13158778	Sensor Control	multi-sensor systems, robust fusion estimation, event-triggered, random packet drops; d-step state delay, deterministic control inputs	Simulation	Manufacturing	China
S5	10.3390/ app13127212	Automation Engineering	crack propagation, machine learning, dynamic load, random forest regressor, support vector regression, gradient boosting regressor, ridge, lasso, k-nearest neighbors	Comparisons	Materials	UK
S6	10.3390/ app13042675	Robot Control	fault-tolerant control, FTC, robot manipulators, artificial intelligence	Literature Review	Manufacturing	Poland
S7	10.3390/ app13042554	Sensor Control	fault identification, fisher discriminant analysis, dissolved oxygen sensor, energy costs assessment, GHG emissions assessment	Simulation	Chemical Engineering	Romania
S8	10.3390/ app13021214	Aircraft Control	dissipativity, large-scale system, linear matrix inequality, networked system, sparseness	Numerical Simulations	Aerospace	China
S9	10.3390/ app13021099	Power System Control	diesel engine heat load, intelligent detection, long short-term memory network, prediction model, evaluation index	Mathematical Modeling	Transportation	China
S10	10.3390/ app122312111	Aircraft Control	persistent monitoring, privacy protection, path planning, monitoring frequency, overdue time	Mathematical Modeling + Simulation	Aerospace	China
S11	10.3390/ app122312069	Aircraft Control	powered parafoil, altitude control, sliding mode backstepping, fractional calculus, LESO	Mathematical Modeling + Simulation	Manufacturing	China
S12	10.3390/ app13031969	Robot Control	swarm robot, unknown complex environment, multitarget cooperative search, simplified virtual force model, particle swarm optimization	Mathematical Modeling + Simulation	Geological Exploration	China
S13	10.3390/ app12178511	Robot Control	flexible-joint manipulators, reduced-order extended state observer, backstepping, command filter, error compensation	Numerical Simulations	Manufacturing	China

. Many studies have been conducted by scholars on fault detection and state estimation in the context of automatic control, covering many related research areas. Studies of contribution 1, 3 and 5 are related to automation engineering research; studies of contribution 2, 8, 10, and 11 are related to aircraft control; studies of contribution 4, and 5 are related to sensor control; studies of contribution 6, 12, and 13 are related to robot control; and other studies are more related to system control research.

Interestingly, studies of contribution 1, and 9 are mostly focused on the prediction of working conditions, where contribution 1 uses a support vector regression algorithm to predict vibrational amplitudes, and contribution 9 uses a long short-term memory network to predict the exhaust temperature of a diesel engine. Studies contribution 3 and contribution 10 both focus on monitoring specific working conditions in real-time as well as analyzing and optimizing the system stability. The authors of contribution 3 analyze the stability of a system under different working conditions based on several factors. The authors of contribution 10 establish a continuous trajectory planning model combined with the ant colony optimization algorithm to monitor the optimal trajectory of an unmanned aerial vehicle.

Furthermore, in contribution 2, abnormal or faulty behavior is detected by efficiently encoding information about a target pose to recognize various human actions more accurately. The authors of contribution 7 focus on fault identification under specific working conditions and use Fisher’s discriminant analysis to diagnose the faults of dissolved oxygen sensors in wastewater treatment plants while evaluating both environmental and economic factors. Meanwhile, the authors of contribution 12 deal with obstacle detection in automatic control applications to achieve real-time obstacle avoidance during a multi-target search by swarm robots.

Overall, these studies cover a wide range of industries, including manufacturing, transportation, aerospace, materials, chemicals and geological exploration. The authors are mainly from China, but there are also scholars from Pakistan, the UK, Poland and Romania who have contributed to our Special Issue.

3. Conclusions

This editorial letter describes the roles of fault detection and state estimation in automatic control systems and highlights the applications of advanced methods and cutting-edge technologies in recent years. In particular, with the development of artificial intelligence, the convergence of deep learning and integration methods has attracted significant attention. These powerful AI-driven methods are designed for fine-grained action recognition, predictive models for fault identification, and fault-tolerant control in complex multi-sensor systems. Other research efforts in the Special Issue advance fault detection and state estimation in both theory and practice. These studies show that fault detection and state estimation have become particularly important in industrial production processes as the level of technology and the scale, complexity, and automation of modern industrial production increase.