On-line adaptive data-driven fault prognostics of complex systems

Data-driven prognostics based on sensor or historical test data have become appropriate prediction means in prognostics and health management (PHM) application. However, most traditional data-driven forecasting methods are off-line which would be seriously limited in many PHM systems that need on-line predicting and real-time processing. Furthermore, even in some on-line prediction methods such as Online SVR, there are conflicts and trade-offs between prognostics efficiency and accuracy. Therefore, in different PHM applications, prognostics algorithms should be on-line, flexible and adaptive to balance the prediction efficiency and accuracy. An on-line adaptive data-driven prognostics strategy is proposed with five different improved on-line prediction algorithms based on Online SVR. These five algorithms are improved with kernel combination and sample reduction to realize higher precision and efficiency. These algorithms can achieve more accurate results by data pre-processing, moreover, faster operation speed and different computational complexity can be achieved by improving training process with on-line data reduction. With these different improved Online SVR approaches, varies of demands with different precision and efficiency could be fulfilled by an adaptive prediction strategy. To evaluate the proposed prognostics strategy, we have executed simulation experiments with Tennessee Eastman (TE) process. In addition, the prediction strategies are also tested and evaluated by traffic mobile communication data from China Mobile Communications Corporation Heilongjiang Co., Ltd. Experiments and test results prove its effectiveness and confirm that the algorithms can be effectively applied to the on-line status prediction with excellent performance in both precision and efficiency.