The prediction determination used modeling with an Artificial Neural Network (ANN) with the back-propagation method. Meanwhile, the resistivity, conductivity, and humidity data were taken from previous studies in the exact area measurements.
Temperature and depth are the two variables being measured. This study aims to predict the temperature in the subsurface temperature measurement using a temperature probe with a depth of 2 m in the Blawan geothermal area. Research on temperature gradient has been carried out in Blawan geothermal area. The parameters sensitive to faults are expected to act as a guiding tool for condition-based maintenance of dragline to improve its reliability and availability. Finally, the fault analysis results are validated through three-axiom based sensitivity analysis, and their prediction accuracy revealed the capability of the model for successful identification of faults. Thereafter identified faults, based on their dependency of occurrence on cause( s)-symptom(s) in the causal model, are categorized as catastrophic fault, degraded fault, or intermittent fault. Historical fault records, experts’ opinion, and characteristics of faults helped defining the threshold limits for fault type identification. The test dataset is used for building a 16-node three-layer BN.
Therefore, the present paper demonstrates a data-driven approach for fault analysis of drag system using inference-based Bayesian network (BN) through generated sensor data, and logbook records of 28 months. Application of an effective fault detection and analysis method can reduce its failure frequency as well as downtime. Draglines are capital-intensive HEMM deployed in large surface coal mines for stripping overburden, and drag system failures contribute to substantial downtimes thus impacting its availability, reliability and productivity. Unidentified and undetected faults in heavy earthmoving machinery (HEMM) are linked with high failure frequency and downtime across the industries.
0 kommentar(er)
