Predicting Remaining Lifetime of Transmission Gears 2010-01-0903
Real-time, integrated health monitoring of gearboxes that can detect, classify, and predict developing gearbox faults is critical to reducing operating and maintenance costs while optimizing the life of critical gearbox components. Statistical-based anomaly detection algorithms, noise measurement related faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these technologies presents a great opportunity to significantly enhance current gearbox health monitoring capabilities and risk management practices. However, methods for analyzing system status and health and for predicting system life expectancy need to be made more powerful, insightful, reliable, and robust for data collection onboard systems in real time.
However, the aim of this paper is to develop robust an analytical method for predicting remaining lifetime of transmission gears. The development is focused specifically on the investigation of a generalized statistical method for characterizing and predicting system degradation (hazard rate). A simple geared system is used as a medium for real data collection, where the sound pressure level (SPL) was measured and analyzed using Bruel & Kjaer (B&K) portable and multi-channel PULSE with condenser 1/2-microphone and preamplifier. The results indicate that the knowing of the remaining lifetime of the faulty gear can enhance the process of scheduling maintenance, order spare parts and using resources; consequently reduce maintenance cost.