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.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc
Orguner, U., & Demırekler, M. (2007). Analysis of single Gaussian approximation of Gaussian mixtures in Bayesian filtering applied to mixed multiple-model estimation. International Journal of Control, 80(6), 952-967.
Orchard, M. E. (2007). AParticle filtering-based framework for on-line fault diagnosis and failure prognosis (Doctoral dissertation, Georgia Institute of Technology).
Bechhoefer, E., Clark, S., & He, D. (2010). A state space model for vibration based prognostics. In Annual Conference of the Prognostics and Health Management Society (pp. 10-16).
Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. (2011). Machine condition prediction based on adaptive neuro–fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, 58(9), 4353-4364.
Bechhoefer, E., He, D., & Dempsey, P. (2011, September). Gear health threshold setting based on a probability of false alarm. In Annual Conference of the Prognostics and Health Management Society (Vol. 2011).
Yu, J. (2012). Health condition monitoring of machines based on hidden Markov model and contribution analysis. IEEE Transactions on Instrumentation and Measurement, 61(8), 2200-2211.
Zio, Enrico. (2012) "Prognostics and health management of industrial equipment." Diagnostics and prognostics of engineering systems: methods and techniques: 333-356
He, D., Bechhoefer, E., Dempsey, P., & Ma, J. (2012). An integrated approach for gear health prognostics.
Al-Arbi, S. K. (2012). Condition Monitoring of Gear Systems using Vibration Analysis (Doctoral dissertation, University of Huddersfield).
Qu, Y., Bechhoefer, E., He, D., & Zhu, J. (2012). A new acoustic emission sensor based gear fault detection approach. International Journal of Prognostics and Health Management, 4(2), 32-45.
Chen, B., Matthews, P. C., & Tavner, P. J. (2013). Wind turbine pitch faults prognosis using a-priori knowledgebased ANFIS. Expert Systems with Applications, 40(17), 6863-6876.
An, Dawn, Joo-Ho Choi, and Nam Ho Kim. (2013) "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab." Reliability Engineering & System Safety 115: 161-169.
kamran Javed. (2014). A Robust and Reliable Data-driven Prognostics Approach Based on Extreme Learning Machine and Fuzzy Clustering (Doctoral dissertation).
Hao, L., Bian, L., Gebraeel, N., & Shi, J. (2016). Residual Life Prediction of Multistage Manufacturing Processes With Interaction Between Tool Wear and Product Quality Degradation. IEEE Transactions on Automation Science and Engineering.