Abstract
With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitoring data is affected by the operating conditions. Aiming at this problem, this paper proposes an improved similarity trajectory method that can directly use the monitoring data under multiple operating conditions for life prediction. The morphological pattern and symbolic aggregate approximation-based similarity measurement method (MP-SAX) is first used to measure the similarity between the monitoring data under multiple operating conditions. Then, the similar life candidate set, and corresponding weight are obtained according to the MP-SAX. Finally, the life prediction results of equipment under multiple operating conditions can be calculated by aggregating the similar life candidate set. The proposed method is validated by the public datasets from NASA Ames Prognostics Data Repository. The results show that the proposed method can directly and effectively use the original monitoring data for life prediction without extracting the degradation trend of the monitoring data.
Highlights
According to the characteristics that the equipment performance degradation is reflected in the trend change of monitoring data, while the changes of operating conditions and environment are reflected in the detailed change of monitoring data, the morphological pattern and symbolic aggregate approximation (MP-symbolic aggregate approximation (SAX)) is first used to measure the similarity between the monitoring data under multiple operating conditions
similarity trajectory method (STM) was used to predict the life based on the degradation model and original monitoring data respectively, and the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) were calculated as shown in Figures 6 and 7
In the scenario of multiple operating conditions, the degradation model obtained only based on part of the monitoring data of the service equipment cannot be completely equivalent to that obtained by whole life, and the accurate life prediction results cannot be obtained in the process of life prediction
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The excellent prediction results of equipment can be obtained by the life prediction method based on machine learning and deep learning, the accuracy of prediction results mostly depends on the design of network structure and the selection of parameters At this time, the similarity trajectory method (STM) based on the casebased reasoning (CBR) which can be regarded as a form of intradomain analogy, and a branch of machine learning shown obvious advantages. Aiming at the abovementioned problem that the degradation trend cannot be extracted effectively under multiple operating conditions, this paper proposes a novel prediction scheme for the life prediction of equipment under multiple operating conditions based on morphological pattern and symbolic aggregate approximation-based similarity measurement method (MP-SAX) and STM.
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