Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process

In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations.

Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections

The behavior of beam-to-column connections significantly influences the stability, strength, and stiffness of steel structures. This is particularly important in extreme non-elastic responses, i.e., earthquakes, and sudden column removal, as the fluctuation in strength and stiffness affects both supply and demand. Accordingly, it is essential to accurately estimate the strength and stiffness of connections in the analysis of and design procedures for steel structures. Beginning with the state-of-the-art, the capacity of three available component-based mechanical models to estimate the complex mechanical properties of top- and seat-angle connections with double-web angles (TSACWs), with variable parameters, were investigated. Subsequently, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental dataset. Using several statistical metrics, including the corresponding coefficient of variation (CoV), correlation coefficient (R), and the correlation coefficient provided by the Taylor diagram, this study revealed that the krill herd-ANN model achieved the most reliable predictive accuracy for the strength and stiffness of top- and seat-angle connections with double web angles.

A novel krill herd algorithm with orthogonality and its application to data clustering

Krill herd algorithm (KHA) is an emerging nature-inspired approach that has been successfully applied to optimization. However, KHA may get stuck into local optima owing to its poor exploitation. In this paper, the orthogonal learning (OL) mechanism is incorporated to enhance the performance of KHA for the first time, then an improved method named orthogonal krill herd algorithm (OKHA) is obtained. Compared with the existing hybridizations of KHA, OKHA could discover more useful information from historical data and construct a more promising solution. The proposed algorithm is applied to solve CEC2017 numerical problems, and its robustness is verified based on the simulation results. Moreover, OKHA is applied to tackle data clustering problems selected from the UCI Machine Learning Repository. The experimental results illustrate that OKHA is superior to or at least competitive with other representative clustering techniques.