Detailed degradation information can help uncover the underlying mechanisms of system degradation and provide essential guidance for accurate prognostics. However, obtaining such information in practice is often costly and challenging, which has motivated the development of prognostics methods that integrate fault diagnosis. Most existing methods, however, primarily focus on degradation magnitude while neglecting trend information, and they also lack sufficient robustness to uncertainties. As a result, they often suffer from low prediction accuracy and limited prediction horizons. To overcome these challenges, this paper proposes a novel diagnosis-integrated prognostics framework in which the remaining useful life prediction task is formulated as an explicit parameter optimization problem. Specifically, the robustness of optimization algorithms contributes to reliable prediction accuracy even under imprecise prior knowledge and low-quality data, while the integration of degradation trend information enhances the ability to capture long-term dynamics, which in turn extends the prediction horizon. To better reflect realistic operating conditions, concurrent actuator and sensor faults are considered. The approach first extracts both magnitude and trend information via fault diagnosis, and then employs parameter optimization for RUL prediction. The effectiveness of the proposed method is demonstrated through simulation studies of actuator and sensor degradation in a grain processing system.

Considering situations such as sensor failures and communication losses, limited data samples are a common challenge in actual industrial processes, making the traditional integrated architecture of fault detection (FD) and fault-tolerant control (FTC) based on subspace identification difficult to be applicable. Regarding this problem, this article proposes a nuclear norm-based subspace identification method for FD and FTC. This method leverages key structural matrix properties in the input and output data model, alleviating reliance on data samples. The parameter matrices required to construct the fault detector and fault-tolerant controller can be directly identified within the nuclear norm optimization framework, enabling the design of an integrated FD and FTC architecture. Two case studies demonstrate that the developed method enhances detection and control performance compared with traditional subspace identification methods, particularly in the case of limited data samples.

With the increasing demand for system reliability, fault prognostics has become an indispensable component of modern engineering systems. In practical applications, system degradation processes are often subject to significant fluctuations caused by operating conditions, human interventions, and environmental noise. These fluctuations pose substantial challenges to fault prognostics, especially in remaining useful life prediction, as they frequently lead to unnecessary warnings and elevated false alarm rates. Most existing studies rely on the first-passage time (FPT) or last-exit time (LET) algorithms. However, their results tend to be overly aggressive or too conservative, which diminishes their practical value for decision-making in engineering applications. To address this issue, a novel fault prognostics algorithm based on the accumulated-passage time (APT) is proposed in this paper. By introducing an additional time-based failure threshold, the proposed algorithm effectively mitigates the adverse effects of fluctuations on prediction accuracy. Furthermore, to jointly predict the remaining useful life and quantify its associated confidence level, an approach for uncertainty quantification grounded in the proposed APT algorithm is developed. Finally, both numerical simulations and real-world bearing degradation datasets are used to validate the effectiveness of the proposed method. The results demonstrate that the APT algorithm outperforms conventional approaches in terms of accuracy, stability, and robustness against uncertainty.

The fault-tolerant tracking control problem is investigated for nonlinear networked stochastic distribution systems (SDSs) under signal quantization, packet loss and system noise. Firstly, a novel logarithmic uniform quantizer is used to quantize the control signal and feedback signal and a data packet loss model is established through Bernoulli random process, which establishes the theoretical foundation for subsequent fault diagnosis (FD). On this basis, a FD observer is designed, which can accurately estimate the fault information under noise and provide reliable fault estimation information for subsequent fault-tolerant control (FTC). Subsequently, to maintain satisfactory tracking performance after fault occurrence, a state feedback fault-tolerant tracking control strategy is proposed based on the parallel distributed compensation technique. Finally, the superiority of the presented FTC method is proven through a numerical example and a practical example.

This paper studies the issue of dynamic event-triggered (ET) resilient control for heterogeneous multi-agent systems (MASs) under DoS attacks. Most of the existing work only considers ideal linear models and undirected graph communications. However, in practice, both disturbance and noise exist in the system model, and directed graph communication is more common. To solve this issue, a fully distributed dynamic ET control strategy with a prediction-based dynamic compensation algorithm is designed to deal with the difficulty of communication jamming. By using the Lyapunov method, it is proved that bounded consensus can be achievable in heterogeneous MASs under DoS attacks, and Zeno behavior is excluded. Furthermore, the tolerable attack intensity is quantified by attack frequency and duration. Finally, a numerical simulation is conducted to validate the efficacy of the proposed method.Note to Practitioners—In real-world scenarios, numerous complex tasks necessitate collaborative endeavors among heterogeneous intelligent agents, such as robots swarms, multiple vehicles and smart grids. Due to physical or geographical limitations, direct communication between all agents and a leader is not always feasible, thereby restricting individual agents to indirectly accessing the leader’s information. Furthermore, the practical constraints on network resources, coupled with the susceptibility to malicious DoS attacks, impede the completion of tasks by multiple agents. This paper presents a resilient dynamic ET control strategy for the consensus task of heterogeneous agents, aiming to improve system resilience while conserving network resources.

Fault is extremely destructive in industrial process, and imbalanced data greatly affect the accuracy of fault diagnosis. Many methods have been proposed to deal with imbalanced data, but the concern for improving the performance of fault diagnostic networks is not enough. Therefore, novel modified conditional generative adversarial network (MCGAN) based on memristive hyperchaotic sequences and mixed-dimensional convolutional neural network (MCNN) is proposed. The 2-D data are obtained by fast Fourier transform and piecewise reconstruction of vibration signals. A novel tanh-input-type memristive hyperchaotic map is utilized to obtain chaos-based random noises. MCGAN can generate synthetic samples for augmenting the fault sample and reducing the imbalanced rate, and chaos-based random noises are used as the noise variable of MCGAN to generate high-quality synthetic samples. By cascading convolution layers with different dimensions, the lightweight MCNN is designed to improve accuracy of motor fault diagnosis. Experiments are implemented using the Case Western Reserve University and practical laboratory platform. The results show that the accuracy of the proposed method is higher than that of some diagnostic networks under imbalanced data.

The fault diagnosis (FD) and fault tolerant control (FTC) problem of the fuzzy stochastic distribution system (SDS) over packet dropout and time delay is studied. Firstly, the static model of linear fuzzy logic system that approximates the output PDF and the dynamic model with the multiplicative fault in a fuzzy system are established. The random packet dropout and time delay caused by the network are described in a unified framework, in which the lost data is compensated with the latest data received by the buffer. On this basis, an adaptive observer is devised to estimate the unknown multiplicative fault. The design of sliding mode predictive fault tolerant controller is discussed to ensure that the system still has good tracking performance after the fault occurs. Numerical simulations on a molecular weight distribution control system are supplied to prove the effectiveness of the presented scheme. Note to Practitioners —The motivation of this paper is to improve the reliability of the fuzzy stochastic distribution system with packet dropout and time delay. This kind of stochastic distribution systems is described by the relationship between the input and the output PDF rather than the normal relationship between the input and the output. When the multiplicative fault occurs in the system, the design of fault diagnosis strategy and fault-tolerant controller becomes an important challenge, especially when the system is subject to packet dropout and time delay. To address the above problems, a new fault diagnosis and fault-tolerant control scheme is proposed, which can accurately estimate the time and size of the fault and obtain the satisfactory fault-tolerant control effect.

A new state estimation method is proposed in this manuscript for a class of linear cyber-physical systems (CPSs) with sparse sensor attacks, unknown input and output disturbances. The sensor attack will make part of the measurement signal received by the remote observer inaccurate. In order to achieve the secure state estimation (SSE) for the investigated linear CPSs, an unknown input observer (UIO) with adaptive switching mechanism is designed. Inspired by the existing results, a set of sub-observers is designed which can exclude the influence of unknown input and output disturbances. To achieve switching between different sub-observers, the adaptive switching mechanism is designed according to the switching adversarial principle. Some parameters of the observer are obtained by designing a set of linear matrix inequalities (LMIs). The sufficient condition for the existence of the observer and the proof of its effectiveness are respectively given by theoretical analysis. Finally, the effectiveness of the proposed method is proved by two Matlab simulation experiments.

In this paper, based on a grain processing device, a bilinear stochastic distribution system (SDS) is established based on its input and output data. The problem of fault diagnosis (FD) and for the bilinear stochastic distribution system when the actuator fault is studied. A new unknown input observer (UIO) is designed to diagnose the fault. A simulation example is given to verify the proposed algorithm.

A fault isolation, estimation, and fault-tolerant control (FTC) scheme for nonlinear time-varying delay stochastic distribution control systems was presented in this paper. The Takagi-Sugeno fuzzy model was adopted to approach the nonlinear dynamics of time-varying delay systems. According to the output equivalence principle and Laplace transformation, an augmented state vector was given to solve the time-varying delay problem. When multiple actuator faults and interference occur simultaneously, fault detection, isolation and fault estimation was designed to obtained the fault information. To decouple faults and obtain the value and location information of the fault, the system was separated into two parts through the designed multiple conversion matrices, in which one subsystem was only affected by one actuator fault. This has simplified the design of fault isolation and estimation. A adaptive observer for fault estimation was given. Then, fault information such as the time, location, and size was determined. The observer gain matrices were calculated using linear matrix inequality (LMI). When a fault was detected and diagnosed, a FTC algorithm was devised using the proportional-integral control scheme to compensate the fault as much as possible. It has been shown that even if multiple faults actuator occurred simultaneously, the FTC controller still ensured the output probability density function of the system traced the desired probability density function when a fault occurred. Finally, the expected results were obtained through the simulation example, which confirmed the effectiveness of the method.

In this article, a fault-tolerant tracking control strategy is investigated for nonlinear probability density function (PDF) control systems with the actuator fault, uncertainties, unknown disturbance, and random packet losses. The control input signal dropout and measurement signal dropouts are described as the independent Bernoulli distribution. An adaptive fault diagnosis (FD) observer based on the Lyapunov function is given to simultaneously estimate the fault, disturbance, and state with packet losses. Different from the traditional robust fault-tolerant control (FTC), a new active fault-tolerant tracking controller is designed based on the model predictive control framework, which has better adaptive fault-tolerant performance. Finally, the validity of the proposed FTC method has been proved by a simulation study of a papermaking process.

The problem of fault diagnosis (FD) and fault tolerant control for a class of Takagi–Sugeno (T–S) fuzzy stochastic distribution control systems subject to sensor and actuator faults is discussed in this article. First, fuzzy logic models are used to approximate the output probability density function (PDF). Next, an adaptive augmented state/FD observer is proposed to estimate the system state, sensor and the actuator faults simultaneously. New expected weights based on the sensor fault estimation information and a PI-type fuzzy feedback fault tolerant controller are designed to compensate the effect of sensor fault and actuator fault simultaneously. When the sensor fault occurs, the expected objective is redesigned to compensate the sensor fault. Meanwhile, the PI controller can compensate the effect of actuator fault, and the output PDF of the system can still track the desired PDF after the fault occurs. Finally, an example of quality distribution control in chemical reaction process is given to confirm the effectiveness of the algorithm.