An online hybrid prognostics ANFIS-PF method with an application to gearbox for RUL prediction
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Abstract
Rotary components are dealing with performance degradation phenomenon, which contains the message of unexpected damages. Therefore, prognostics and health management (PHM), has been introduced to calculate and
predict the remaining useful life (RUL) in order to prevent costly damages or repairs. Data-driven, model-based and hybrid-based techniques are three main categories of PHM techniques. From the health monitoring view, the main idea is to use the experimental run-to-failure data as an intelligence-based model for our gearbox and predict the RUL with the probability (model) based method (Particle Filtering). Firstly, to perform our prognostics technique, we require the degradation information from gearbox. Therefore in duration of 10 days, we conduct a run-to-failure experiment for a test bench with initiative fault injection in day 7th. Period of last-three-days is considered as run-to-failure signal for proposed algorithm. After preprocessing the data, we apply a combined prediction method ANFIS-PF, using Adaptive Neuro Fuzzy Inference System (ANFIS) and Particle Filtering (PF). ANFIS used as a prediction model tool, while the particle filter method was used to find a step-ahead behavior of the gear. ANFIS as a powerful data-driven method will model the prediction of degradation data and finally this model is
applied to particle filtering to predict a-step-ahead of the gear behavior until failure will happen. Meanwhile, some important signal characteristics known as condition indicators (CIs) have been extracted from the residual, energy, frequency based data processing. Then, the energy-based health index (HI) is calculated using threshold and sum of distributions, to show the degradation trend of tested gearbox. The online prediction results properly
demonstrate the performances of the proposed ANFIS-PF algorithm, to predict the RUL of gearbox system with a 95% confidence boundary distribution.
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PHM
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