Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment

Abstract

Resilience is an intrinsic characteristic of systems. Through it, the capacity of a system to react to the existence of disruptive events is expressed. A series of metrics to represent systems’ resilience have been proposed, however, only one indicator relates the availability of the system to this characteristic. With such a metric, it is possible to relate the topological aspects of a system and the resources available in order to be able to promptly respond to the loss of performance as a result of unexpected events. This work proposes the adaptation and application of such a resilience index to assess the influence of different maintenance strategies and topologies in fleets’ resilience. In addition, an application study considering an actual mining fleet is provided. A set of critical assets was identified and represented using reliability block diagrams. Monte Carlo simulation experiments were conducted and the system availability data were extracted. Resilience indexes were obtained in order to carry out the definition of the best maintenance policies in critical equipment and the assessment of the impact of modifying system redundancies. The main results of this work lead to the overall conclusion that redundancy is an important system attribute in order to improve resiliency along time.

Evaluation of Technical Resilience and Its Estimation by the Least Squares Method

“…The technical resilience value is directly proportional to the availability A and inversely proportional to the recovery time t of the availability function after the occurrence of a defect. Thus, the A/t ratio is used to describe the degree of technical resilience [20]. The time value t is logarithmized to use to balance the level of effects between the availability of A and the recovery time t [21].…”

“…After the appearance of a fault and the repair operation to remove its effects, availability, as a function of probabilities, is reduced. The data were parsed in a numerical system so that all the variables in equation 2 [20] could be calculated.…”

“…The data were centralized on different time intervals delimited to every ten faults that appeared (table 1). R0 represents the resilience scores over the time interval bounded by defects [1][2][3][4][5][6][7][8][9][10], R1 represents the resilience scores over the time interval determined by defects [11][12][13][14][15][16][17][18][19][20] and so on. The graphs of the values of the resilience function as a function of time for every ten defects that appeared in the system after the stabilization period of the technical system operation are presented in Figure 1.…”