DENOISING MICRO-ELECTROMECHANICAL SYSTEMS (MEMS) DATA USING ADAPTIVE WAVELET THRESHOLD FOR PREDICTIVE RAILWAY MAINTENANCE - Azka Huda Prastiwi, Nadia
As the most prevalent cause of train accidents, derailment becomes more dangerous with increasing train passengers in Indonesia. Manually prevention and maintenance used to do in recent times cannot bring any reduction in total cases. Predictive maintenance (PdM), a data-driven technique based on the factual condition, has become popular in many fields, including railway maintenance. An essential step in this technique is the ETL (Extract – Transform – Load) process, where data quality becomes its concern to support the decision-making process. One dimension in data quality is accuracy. However, the MEMS (micro-electromechanical systems) sensor installed in the train contains noise that influences the data accuracy. Hence, this study aims to find the best method to support railway maintenance. Related to that problem, Shi et al. (2021) proposed a wavelet threshold to denoise the MEMS signal with the SURE Shrink threshold estimator. In addition, this wavelet threshold technique highly depends on a threshold value. Meanwhile, Nisha and Mohideen (2016) claimed that SURE Shrink has not better result than Bayes Shrink to denoise an image. Thus, this study concern with finding the best threshold estimator by comparing SNR (Signal-to-Noise Ratio) from the SURE Shrink threshold estimator and Bayes Shrink threshold estimator. According to the Matlab simulation, the author finds that the raw MEMS output needs to be observed in a 5-second duration before denoising to get the best representation. In this duration, the author infers that the Bayes Shrink threshold estimator can give better results than the SURE Shrink threshold estimator with around 7 dB of a significant SNR value in accelerometer and about 14 dB of a significant SNR value in gyroscope.