Search

Batch process can exhibit significantly different characteristics across different phases, hence it is significant to partition it reasonably and set up corresponding subphase models for online monitoring. Unlike traditional phase-partition algorithms that customarily exploit the result of PCA algorithm for advanced research, an innovative algorithm which directly extracts effective information from the covariance matrix is presented in this paper, which is called multiway beacon exception analysis for maintenance (MBEAM). Its theoretics and statistical characteristics are demonstrated adequately. Based on the accurate capture of the change in variable correlation caused by characteristic variance of the process, the algorithm can separate the process into major phases and transition patterns automatically. The time-varying characteristics will then remain relatively stable in each independent subphase and will be supervised by homologous monitoring model that reflects the inherent phase feature. Due to its simple and intuitive format, MBEAM has superior performance in computation efficiency and fault interpretation, which is illuminated later in this paper. Synthetical illustrations are given concerning the influences of major parameters on the monitoring performance. Comparison with the step-wise sequential phase partition algorithm is conducted for a clearer insight. Experiments are carried out to further confirm the validation of the proposed method. [ABSTRACT FROM PUBLISHER]

By means of virtualization, computing and storage resources are effectively multiplexed by different applications in cloud data centers. However, there lacks useful approaches to share the internal network resource of cloud data centers. Invalid network sharing not only degrade the performance of applications, but also affect the efficiency of data center operation. To guarantee network performance of applications and provide fine-grained service differentiation, in this paper, we propose a differentiated bandwidth guarantee scheme for data center networks. Utility functions are constructed according to the throughput and delay sensitive characteristics of different applications. Aiming to maximize the utility of all applications, the problem is formulated as a multi-objective optimization problem. We solve this problem using a heuristic algorithm: the elitist Non-Dominated Sorted Genetic Algorithm-II(NSGA-II), and we make a multi-attribute decision to refine the solutions. Extensive simulations are conducted to show that our scheme provides minimum band-width guarantees and achieves more fine-grained service differentiation than existing approaches. The simulation also verifies that the proposed mechanism is suitable for arbitrary data center architectures. [ABSTRACT FROM AUTHOR]

The batch process is a batch-repeated production process, which shows a multiple modal switching within the batch. This makes it difficult to use a single-mode analysis method to achieve accurate modeling and fault diagnosis. Therefore, a novel two-step phase partition idea is proposed based on improved affinity propagation (AP) clustering and sub-phase similarity diminishing scan (PSDS) method. In order to capture the dynamic characteristics of the modes switching, the improved AP clustering is used for phase preliminary partition, in which an effective method that is more suitable for complex batch process is proposed to calculate the similarity. For sub-phases generated by the phase preliminary partition, the internal process of each sub-phase also varies obviously with the development of duration, so an innovative method PSDS is proposed to implement phase fine partition. Then each sub-phase scanned by the PSDS method is identified and divided into stable parts and transition parts, which further reflects the change trend within the sub-phase. For the outliers and misclassification points that may arise during the process of phase partition, the solutions are put forward, respectively. Thus, the partition results with different characteristics are modeled and monitored separately by using the method of principal component analysis (PCA). A practical application on batch process, aircraft steering engine platform fault diagnosis experiment, is given to conform the feasibility and performance of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect. [ABSTRACT FROM AUTHOR]