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.

Analytical Redundancy of Variable Cycle Engine Based on Proper Net considering Multiple Input Variables and the Whole Engine’s Degradation

In this paper, Proper net is proposed to construct variable cycle engine’s analytical redundancy, when all control variables and environmental variables change simultaneously, also accompanied with the whole engine’s degradation. In another word, Proper net is proposed to solve a multivariable, strongly nonlinear, dynamic, and time-varying problem. In order to make the topological structure of Proper net physically explainable, Proper net’s topological structure is designed according to physical relationship between variables, by which means analytical redundancy based on Proper net achieves higher accuracy with less calculation time. Experiments were compared with performance of analytical redundancy based on Proper net, seven convolutional neural network topological structures, and five shallow learning methods. Results demonstrate that under condition of average relative error less than 1.5%, Proper net is the most accurate and the least time-consuming one, which proves not only the effectiveness of Proper net but also the feasibility of topological structures’ design method based on physical relationship.

Reconfiguration Strategy for Fault Tolerance in a Cascaded Multilevel Inverter Using a Z-Source Converter

The cascade multilevel inverters are widely used in industrial manufacturing processes for DC-AC conversion. Therefore, the reliability and efficiency improvement, optimized control, and fault-tolerant strategies are areas of interest for researchers. The fault tolerance strategies applied to cascade multilevel inverters are classified as material redundancy and analytical redundancy. This paper presents the use of the Z-source converter as a fault reconfiguration method applied to a cascade multilevel inverter. On the one hand, the proposed approach has the characteristic of combining the use of material redundancy (modifying the output voltage by changing the Z-source operation), and on the other hand, it has the use of analytical redundancy (modifying the switching sequence of the multilevel inverter, changing from symmetrical to asymmetrical operation mode). This approach has been validated by experimental results of the system under fault-free conditions and employing the Z-source converter as the main fault reconfiguration element. The proposed fault reconfiguration strategy allows the cascaded multilevel inverter to continue to operate even in the presence of a fault by having continuous operation.