This brief proposes a robust subspace-aided fault detection approach for rolling mill processes with roll eccentricity. The novelty of this brief relies on the closed-loop identification of the so-called data-driven realization of the stable kernel representation (SKR) of the rolling mill process. In order to ensure an accurate and robust closed-loop identification, the mappings among the closed-loop process data and the unknown disturbance are analyzed analytically based on the process model, which play essential roles in the data-driven realizations and designs. By determining the kernel subspace of the rolling mill process, a robust data-driven fault detection approach is derived and a disturbance-decoupled residual signal can be obtained. The effectiveness of the proposed approach in comparison to conventional data-driven designs is demonstrated through case studies on a rolling mill benchmark process.

Sensors are ubiquitous in automatized industrial systems. To ensure the safety of the process control, the fault diagnosis and fault-tolerant control of sensors is necessary. This article proposes subspace-aided sensor fault diagnosis and compensation control approaches based on the data-driven stable kernel representation (SKR) and stable image representation (SIR) identified by the process data decompositions. First, this article obtains data-driven SKR and SIR through the mapping relationship of the subspaces of signals and proposes a series of fault diagnosis and compensation approaches. Furthermore, considering the accuracy and timeliness, this article presents an accurate online fault diagnosis and compensation approach by the online updating $LQ$ decomposition. These approaches can perform fault diagnosis, fault estimation, and fault compensation for the multiple and different types of additive sensor faults. The effectiveness of the strategies has been verified by the numerical study and the three-tank experimental system, which has a specific engineering significance.

A novel closed-loop numerical algorithm for subspace state space system identification (CN4SID) is proposed in this paper. Different from standard schemes, CN4SID algorithm can extend the standard LQ decomposition to the closed-loop cases by incorporating the controller information. Moreover, the proposed method can deliver unbiased pole estimation in the closed-loop framework. To this end, CN4SID algorithm shows superior pole estimation performance compared with a class of subspace identification method via principal component analysis algorithms. The effectiveness of the proposed CN4SID algorithm is finally verified through a practical dc motor system.

This article is concerned with a novel data-driven bias-eliminated subspace identification approach for closed-loop systems. Compared with the existing methods, the proposed method first proposes to utilize the coprime factorization of the controller to construct an instrumental variable uncorrelated with noise under closed-loop conditions. Furthermore, it can reliably eliminate the pole estimation bias due to the correlation between inputs and noise under feedback control. More importantly, the proposed method establishes a general framework for both open-loop and closed-loop system identification. Performance comparisons with two other closed-loop methods are made from many different aspects. Finally, the performance of the identified system is again demonstrated in the vehicle lateral dynamic system.

With the development of Cyber-Physical Systems (CPSs), many industrial facilities have realized remote control and monitoring. However, the widespread of CPSs has brought new issues and challenges in terms of security. Attackers can exploit vulnerabilities induced by network communication, tamper with transmitted data, and cause serious accidents through carefully designed covert attacks. This paper proposes a residual-driven comprehensive defense scheme based on the coprime factorization technique to address the threat posed by concealed CPS attacks. The novel scheme protects CPS from stealth cyber-physical attacks through secure transmission and attack detection. In particular, a secure transmission method is first introduced to prevent information leakage from the source. The pivotal idea is to convert confidential transmission control and measurement signals into non-essential filtered residual signals. It contributes to the reduction of information leakage and helps reduce the risks of stealth attacks. Then, under the same residual-driven framework, a stealth attack detection approach is put forward. It can eliminate false alarms caused by system faults, and therefore, achieve superior efficacy in detection accuracy under stealth attacks. Finally, simulation research is conducted on the F-404 engine to verify the effectiveness and performance of the proposed scheme and approach.

Aiming at the problem of colored noise in the signal, this article proposes a subspace frequency estimation approach under colored noise with application to fault diagnosis of motor rolling bearings. First, a nonlinear discrete-time system is described to generate colored noise. An extended I/O model with parameters of a nonlinear discrete-time system is given by the subspace method. Then, the gap metric-aided system order determination approach is developed for extended observability matrix identification. Then, the data-driven diagnostic observer parameter identification approach and the fast approximate power iterative subspace method are adopted to realize online monitoring for frequency change detection. Eventually, a data-driven design scheme of residual generator is proposed for the implementation of fault detection. The effectiveness of the proposed methods is verified for fault diagnosis performance through numerical simulations and the experimental measurements from the dynamic motor rolling bearing experiment rig.