Abstract
The high-pressure pulsation test (HPPT) bench is used to test the functionality and resilience of components under high pressure and pulsation. In highly automated machining systems, it is vital to reduce the number of unplanned machine downtimes due to equipment failure, as these can lead to significant losses in resources. The objective of this study is to enhance the efficiency of HPPT benches by addressing specimen, bench, and test environment- based problems and to develop a failure condition tracking tool (FCTT) by using machine learning (ML) algorithms. The findings of this study provide a basis for the development of the company's data-driven smart predictive maintenance applications while providing an increase in the operational efficiency of HPPT benches. The data set used in the study was obtained from the HPPT benches of an automotive parts manufacturing company. Decision tree (DT), gradient boosting tree (GBT), Naïve Bayes (NB), and random forest (RF) algorithms are used to determine the best model. The comparative analysis of ML algorithms revealed that the GBT algorithm exhibits superior predictive capabilities regarding HPPT bench failure predictions. The FCTT is developed using the results of the GBT algorithm and integrated into the company's HPPT bench maintenance system. The results of this study are described as a fundamental step in the company's smart maintenance programme. Implementing FCTT has resulted in a 20% increase in HPPT utilization, a reduction in maintenance costs, and a positive contribution to the company's overall competitiveness and profitability. The utilization of FCTT has enabled the prediction of HPPT failures, the optimization of maintenance schedules, the minimization of downtime, and the improvement of maintenance practices. Furthermore, using ML technologies provides valuable insights into the performance and maintenance trends of the HPPT bench, enabling data-driven decision-making and strategic planning for the company's HPPT bench maintenance operations.