A modified profust-performance-reliability algorithm and its application to dynamic systems

Abstract

In the field of system health monitoring, system performance degradation or fault occurrence will decrease the system reliability to some degree. However, traditional reliability analysis is of limited usefulness in evaluating the reliability of an individual product under dynamic operating and environmental conditions. In this case, research on performance reliability as well as real-time reliability has attracted extensive attentions. Considering the characteristics of fuzzy reliability theory, a performance reliability based on profust reliability theory has its advantage on tracking system's continuous degradation. On the basis of our previous work, this paper proposes a modified profust-performance-reliability (PPR) algorithm as a supplement to the profust reliability based approach to prognostics and health management. In the modified PPR algorithm, the item of transition probability among system's multi-states is replaced with the real-time distribution of system's health status, which achieves an easier implementation of PPR's calculation in practice with higher real-time capability and accuracy. Then, its application in the performance evaluation and prediction of dynamic systems are comprehensively proposed. Finally, a simulation of a quadrotor with partial loss of actuator effectiveness is presented to validate the availability and effectiveness of the proposed method.

Design on Universal Circuit Breaker via Improved Gray Wolf Optimization Algorithm

“…is has effectively improved the efficiency of engineering design [10][11][12]. For example, the particle-based Euclidean distance, improved particle swarm optimization (PSO) algorithm [13] for optimal design of permanent magnet synchronous motors, and improved fuzzy performance reliability algorithm in dynamic systems (PPR) had been employed [14].…”

Failsafe mechanism design of multicopters based on supervisory control theory

“…On the other hand, the management level imports the quantitative health results from the monitoring and prediction levels, and then responds to meet qualitative safety or health requirements. In our previous paper [19], multicopter health is quantitatively evaluated in the face of actuator failures. This paper studies a safety decision-making logic by Supervisory Control Theory (SCT) to guarantee flight safety from a qualitative perspective.…”

“…1, an integrated PHM system generally contains three levels: monitoring, prediction and management [3]. On the one hand, the monitoring and prediction levels assess the quantitative health of the studied system [4][5][6][7]. On the other hand, the management level imports the quantitative health results from the monitoring and prediction levels and then responds to meet qualitative safety or health requirements.…”