Improving the operational efficiency of construction and road machines based on intelligent monitoring of the technical condition of working systems

Abstract

The fleet of construction and road machines operated by contracting organizations is renewed more slowly than its service life is consumed, while the preventive maintenance system remains tied to calendar operating time and responds weakly to the actual degradation rate of working systems. The gap between the prescribed maintenance interval and the real change of technical condition generates two opposite losses – a sudden failure between scheduled interventions and the disassembly of a serviceable unit once a formal deadline is reached. The purpose of the work was to quantify the share of the operational efficiency reserve that is released by the transition from prescribed-interval maintenance to condition-based maintenance with residual life prediction. An author's computational model of a fleet of 46 machines has been built with a breakdown into five working systems; for each of them the failure flow parameter, the share of failures with a recognizable precursor, the duration of an unscheduled stoppage and the period of reduced-output operation before failure are specified. The calculation was performed for three maintenance strategies with threshold values of the monitored parameters taken from GOST R ISO 20816-3-2023 and GOST 17216-2001, the working time balance was closed iteratively, and the output increment was decomposed by the chain substitution method. The technical availability factor rises from 0.885 to 0.909, the annual output of a machine grows by 82,1 machine hours (4,64%), and unscheduled downtime is reduced by 37,9% with a 4,6-hour increase in scheduled downtime. The reduction of unscheduled downtime accounts for 62,0% of the increment, the elimination of operation in a degraded state accounts for 43,5%, while the contribution of scheduled maintenance is negative (−5,5%). The hydraulic drive accounts for 35,0% of failures and 65,7% of the productivity losses caused by degradation, which shifts the priority of instrumental control towards the parameters of the working fluid and the volumetric efficiency of pumps. The dependence of the effect on the prediction lead time is non-monotonic: beyond 120 engine hours the growing share of false alarms devalues the gain in detection completeness, and monitoring ceases to pay back the additional stoppages it introduces.