In the past decades, with the rapid development of modern technologies, a special kind of fault, the intermittent fault (IF) has become a serious threat to system securities. Although fruitful research results have been obtained for fault diagnosis, most of them are concerned with permanent faults. Furthermore, up to now, there has been no overview on the diagnosis techniques for IFs in dynamic systems. In fact, IFs are common in a variety of fields, whereas their research is far from mature and many open issues still remain to be solved. Therefore, in this overview, our main objective is to attract direct attentions from more researchers and engineers into the fascinating area of IF diagnosis. In this paper, the concept, research background, challenges and research motivations of IFs are introduced. Then, current research into diagnostics of IFs is reviewed, along with discussions on their strengths, limitations, and future directions.

This paper is concerned with the distributed state estimation problem over wireless sensor networks. The communication links are unreliable that are subject to random link failures modeled as a set of independent Bernoulli processes. To estimate the plant state collaboratively, a Kalman-consensus filtering approach is developed where the sensors spread the local information obtained from the Kalman filtering algorithm by performing a consensus of the inverse covariance matrices at each time instant. Sufficient conditions for the stochastic boundedness of the Kalman-consensus filter are established. It is shown that the filtering performance is directly influenced by the network connectivity and the collective observability. A numerical example is illustrated to verify the proposed results.

In this article, the fault-tolerant consensus control problem is investigated for multiagent systems with sensor faults. A first-order difference equation is utilized to describe the sensor fault, and an observer is designed to estimate the state and the fault simultaneously. For security enhancement and/or congestion mitigation purposes, the estimated state is first encrypted into a series of finite-level codewords by an encryption algorithm and, then, transmitted to other agents through a directed topology. After being received, the codewords are then decrypted by the corresponding decryption algorithm and subsequently utilized to design the consensus controller. By constructing a novel matrix norm along with its compatible vector norm, we obtain a necessary and sufficient condition, which serves as an index in the observer and the controller design. In the end, a simulation example is given to demonstrate the validity of the results in this article.

Due to the inner deteriorating mechanism or the mutant environmental stress, the degradation systems with multi-phase features have frequently been encountered in engineering practice. The key issue for prognostics of such systems is to account for the impact of the changing-point variability and the associated degradation state at this point on the progression of the degradation process. However, current studies usually treat the degradation state at the change point as a fixed value rather a random variable. Thus, it is still challenging to predict the lifetime of such multi-phase degrading systems. To this end, we first formulate a general degradation modeling framework based on a two-phase Wiener process. In prognostics, we take into full account the uncertainty of the degradation state at the changing point and then derive the analytical expressions of the lifetime and remaining useful life under the concept of the first passage time. The derived results are distinguished from existing results limited to the fixed state at the changing point. Furthermore, we extend our approach and results to cases with unit-to-unit variability and multiple phases. To facilitate the model implementation, we propose both offline and online methods for parameter identification, which make full use of the historical data and the in-service data. Finally, a numerical simulation and a practical case study are provided for illustration.

The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal of the gearbox is an effective indicator of its operational status and fault information. However, gearboxes in real industrial settings often operate under variable working conditions, such as varying speeds and loads. It is a significant and challenging research area to complete the gearbox fault diagnosis procedure under varying operating conditions using vibration signals. This data article presents vibration datasets collected from a gearbox exhibiting various fault degrees of severity and fault types, operating under diverse speed and load conditions. These faults are manually implanted into the gears or bearings through precise machining processes, which include health, missing teeth, wear, pitting, root cracks, and broken teeth. Several kinds of actual compound faults are also encompassed. The development of these datasets facilitates testing the effectiveness and reliability of newly developed fault diagnosis methods.

The consensus control problem of multi-agent systems (MASs) has received an increasing research interest with ever-developing consensus theory. Before it can be widely applied to actual engineering, the consensus theory needs to be improved by addressing some practical problems. One of the key problems is how to ensure the desirable performance of each agent as well as the entire MAS when confronted with faults, which is referred to as the fault-tolerant consensus control (FTCC) issue. In this survey, we review the classic and recent results in the context of FTCC from two aspects, namely, physical security and logical security. In addition, we provide the latest results on FTCC where the physical and logical security are considered simultaneously in a framework. Finally, we outline several potential future research topics on the FTCC issue.