Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

This article presents a hybrid model for predicting the temperature of molten steel in a ladle furnace (LF). Unique to the proposed hybrid prediction model is that its neural network-based empirical part is trained in an indirect way since the target outputs of this part are unavailable. A modified cuckoo search (CS) algorithm is used to optimize the parameters in the empirical part. The search of each individual in the traditional CS is normally performed independently, which may limit the algorithm’s search capability. To address this, a modified CS, information interaction-enhanced CS (IICS), is proposed in this article to enhance the interaction of search information between individuals and thereby the search capability of the algorithm. The performance of the proposed IICS algorithm is first verified by testing on two benchmark sets (including 16 classical benchmark functions and 29 CEC 2017 benchmark functions) and then used in optimizing the parameters in the empirical part of the proposed hybrid prediction model. The proposed hybrid model is applied to actual production data from a 300 t LF at Baoshan Iron & Steel Co. Ltd, one of China's most famous integrated iron and steel enterprises, and the results show that the proposed hybrid prediction model is effective with comparatively high accuracy

Nonexistence of the Asymptotic Flocking in the Cucker−Smale Model With Short Range Communication Weights

For the long range communicated Cucker-Smale model, the asymptotic flocking exists for any initialcondition. It is noted that, for the short range communicated Cucker-Smale model, the asymptotic flocking only holds for very restricted initial conditions. In this case, the nonexistence of the asymptotic flocking has been frequently observed in numerical simulations, however, the theoretical results are far from perfect. In this note, we first point out that the nonexistence of the asymptotic flocking is equivalent to the unboundedness of the second order space moment, i.e., t|x i(t)-x j(t)|2=. Furthermore, by taking the second derivative and then integrating, we establish a new and key equality about this moment. At last, we use this equality and relevant technical lemmas to deduce a general sufficient condition to the nonexistence of the asymptotic flocking

Observer-Based Event-Triggered Predictive Control for Networked Control Systems under DoS Attacks

This paper studies the problem of DoS attack defense based on static observer-based event-triggered predictive control in networked control systems (NCSs). First, under the conditions of limited network bandwidth resources and the incomplete observability of the state of the system, we introduce the event-triggered function to provide a discrete event-triggered transmission scheme for the observer. Then, we analyze denial-of-service (DoS) attacks that occur on the network transmission channel. Using the above-mentioned event-triggered scheme, a novel class of predictive control algorithms is designed on the control node to proactively save network bandwidth and compensate for DoS attacks, which ensures the stability of NCSs. Meanwhile, a closed-loop system with an observer-based event-triggered predictive control scheme for analysis is created. Through linear matrix inequality (LMI) and the Lyapunov function method, the design of the controller, observer and event-triggered matrices is established, and the stability of the scheme is analyzed. The results show that the proposed solution can effectively compensate DoS attacks and save network bandwidth resources by combining event-triggered mechanisms. Finally, a smart grid simulation example is employed to verify the feasibility and effectiveness of the scheme’s defense against DoS attacks

Data-Driven Fault Classification for Non-Inverting Buck–Boost DC–DC Power Converters Based on Expectation Maximisation Principal Component Analysis and Support Vector Machine Approaches

Data-driven fault classification for power converter systems has been taking more into considerations in power electronics, machine drives, and electric vehicles. It is challenging to classify the different topologies of faults in the real time monitoring control systems. In this paper, a data-driven and supervised machine learning-based fault classification technique is adopted by combining and consolidating with Expectation Maximisation Principal Component Analysis (EMPCA) and Support Vector Machine (SVM) to substantiate the availability of fault classification. The proposed methodology is applied to the non-inverting Buck–Boost DC–DC power converter systems subjected to the incipient fault and serious fault, respectively. Finally, the feasibility of the approach is validated by intensive simulations and comparison studies

Genomic characterization of tigecycline-resistant Escherichia coli and Klebsiella pneumoniae isolates from hospital sewage

IntroductionThe tigecycline-resistant Enterobacterales have emerged as a great public concern, and the mobile tet(X) variants and tmexCD-toprJ efflux pump are mainly responsible for the spread of tigecycline resistance. Hospital sewage is considered as an important reservoir of antimicrobial resistance, while tigecycline resistance in this niche is under-researched.MethodsIn this study, five Escherichia coli and six Klebsiella pneumoniae strains were selected from a collection of tigecycline-resistant Enterobacterales for further investigation by antimicrobial susceptibility testing, conjugation, whole-genome sequencing, and bioinformatics analysis.ResultsAll five E. coli strains harbored tet(X4), which was located on different plasmids, including a novel IncC/IncFIA(HI1)/IncHI1A/IncHI1B(R27) hybrid structure. In addition, tet(X4)-bearing plasmids were able to transfer by conjugation and be stabilized in the recipient in the absence of antibiotics. tmexCD1-toprJ1 was identified in two K. pneumoniae (LZSFT39 and LZSRT3) and it was carried by a novel multidrug-resistance transposon, designated Tn7368, on a novel IncR/IncU hybrid plasmid. In addition, we found that two K. pneumoniae (LZSFZT3 and LZSRT3) showed overexpression of efflux genes acrB and oqxB, respectively, which was most likely to be caused by mutations in ramR and oqxR.DiscussionIn conclusion, the findings in this study expand our knowledge of the genetic elements that carry tigecycline resistance genes, which establishes a baseline for investigating the structure diversity and evolutionary trajectories of human, animal, and environmental tigecycline resistomes

Grain refinement of magnesium alloys: a review of recent research, theoretical developments and their application

This paper builds on the ‘‘Grain Refinement of Mg Alloys’’ published in 2005 and reviews the grain refinement research onMg alloys that has been undertaken since then with an emphasis on the theoretical and analytical methods that have been developed. Consideration of recent research results and current theoretical knowledge has highlighted two important factors that affect an alloy’s as-cast grain size. The first factor applies to commercial Mg-Al alloys where it is concluded that impurity and minor elements such as Fe and Mn have a substantially negative impact on grain size because, in combination with Al, intermetallic phases can be formed that tend to poison the more potent native or deliberately added nucleant particles present in the melt. This factor appears to explain the contradictory experimental outcomes reported in the literature and suggests that the search for a more potent and reliable grain refining technology may need to take a different approach. The second factor applies to all alloys and is related to the role of constitutional supercooling which, on the one hand, promotes grain nucleation and, on the other hand, forms a nucleation-free zone preventing further nucleation within this zone, consequently limiting the grain refinement achievable, particularly in low solute-containing alloys. Strategies to reduce the negative impact of these two factors are discussed. Further, the Interdependence model has been shown to apply to a broad range of casting methods from slow cooling gravity die casting to fast cooling high pressure die casting and dynamic methods such as ultrasonic treatment

Chattering-free discrete-time sliding mode control with event-trigger strategy

In this paper, a novel discrete-time sliding mode control (DSMC) method based on event-trigger strategy is proposed. The DSMC method here used is a chattering-free method. With the introduction of event-trigger strategy, the system performance is improved in terms of the control updating times. Hence, less resource is required in control execution. It is shown that the proposed control techniques ensure the reachability of the sliding surface with a small band. Finally, simulations are presented to verify the effectiveness of the proposed methods

Convergence of velocities for the short range communicated discrete-time Cucker–Smale model

Most existing literature about the discrete-time Cucker−Smale model focus on the asymptotic flocking behavior. When the communication weight has a long range, asymptotic flocking holds for any initial data. Actually, the velocity of every agent will exponentially converge to the same limit in this case. However, when the communication weight has a short range, asymptotic flocking does not exist for general initial data. In this note, we will prove the convergence of velocities for any initial data in the short range communication case. We first propose a new strategy about the convergence of velocities, and then show an important inequality about the velocity–position moment, according to which we will successfully prove the convergence of velocities and obtain the convergence rates for two kinds of communication weights. Besides, for some special initial data we show that the limits of velocities can be different from each other. Simulation results are given to validate the theoretical results