Since industrial control systems are usually integrated with numerous physical devices, the security of control systems plays an important role in safe operation of industrial chemical processes. However, due to the use of a large number of control actuators and measurement sensors and the increasing use of wireless communication, control systems are becoming increasingly vulnerable to cyber-attacks, which may spread rapidly and may cause severe industrial incidents. To mitigate the impact of cyber-attacks in chemical processes, this work integrates a neural network (NN)-based detection method and a Lyapunov-based model predictive controller for a class of nonlinear systems. A chemical process example is used to illustrate the application of the proposed NN-based detection and LMPC methods to handle cyber-attacks.

In this article, the diagnosability problem is investigated for open-loop and closed-loop stochastic systems under sensor and actuator faults. A novel quantitative fault diagnosability analysis method is developed to characterize the detectability and isolability for a given system without any predetermined fault diagnosis algorithm. By exploiting the Bhattacharyya distance, some novel indices are proposed to evaluate the difficulty degree of fault detectability and isolability. Necessary and sufficient conditions are established for the diagnosability of both open- and closed-loop stochastic systems undergoing sensor and actuator faults. In addition, the relationship between the fault detectability of open-loop systems and that of closed-loop ones is discussed.

The ideal air brake system is to provide accurate brake pressures, which ensures the normal braking performance of high-speed trains. For the high-speed trains in China, a univariate monitoring strategy has been adopted to monitor the state of each brake cylinder of the air brake system separately. In practice, this kind of strategy is effective for faults with large amplitude, but it is always ineffective for all kinds of incipient faults with small amplitude. Due to nonstationary and multimode properties inherent in air brake systems, conventional detection methods are difficult to be used. In this article, we develop novel detection strategies for three kinds of incipient faults in the air brake systems, including sensor deviation fault, leakage fault, and brake cylinder component fault. Starting from the characteristics of actual data from air brake systems, some variables with high level of consistency are selected. Then, a linear data transformation is conducted to obtain approximate stationary properties, and all the data in multiple modes are mapped into a compact domain. In addition, a fault-to-signal ratio (FSR) is proposed to reflect the sensitivity of the proposed detection statistic to the fault. In particular, the multistage control limits and sliding window strategies are integrated into the detection statistic, which effectively lowers the control limits and enhances robustness to noise, respectively. Finally, experiments on the braking test platform are provided to show the effectiveness of our proposed strategies.

The moving average (MA)-type scheme, also known as the smoothing method, has been well established within the multivariate statistical process monitoring (MSPM) framework since the 1990s. However, its theoretical basis is still limited to smoothing independent data, and the optimality of its equally or exponentially weighted scheme remains unproven. This paper aims to weaken the independence assumption in the existing MA method, and then extend it to a broader area of dealing with autocorrelated weakly stationary processes. With the discovery of the non-optimality of the equally and exponentially weighted schemes used for fault detection when data have autocorrelation, the essence that they do not effectively utilize the correlation information of samples is revealed, giving birth to an optimally weighted moving average (OWMA) theory. The OWMA method is combined with the Hotelling's $T^2$ statistic to form an OWMA $T^2$ control chart (OWMA-TCC), in order to detect a more challenging type of fault, i.e., intermittent fault (IF). Different from the MA scheme that puts an equal weight on samples within a time window, OWMA-TCC uses correlation (autocorrelation and cross-correlation) information to find an optimal weight vector (OWV) for the purpose of IF detection (IFD). In order to achieve a best IFD performance, the concept of IF detectability is defined and corresponding detectability conditions are provided, which further serve as selection criteria of the OWV. Then, the OWV is given in the form of a solution to nonlinear equations, whose existence is proven with the aid of the Brouwer fixed-point theory. Moreover, symmetrical structure of the OWV is revealed, and the optimality of the MA scheme for any IF directions when data exhibit no autocorrelation is proven.