Multi-source distillation-migration mechanical fault intelligent diagnosis method based on high-order moment matching

Abstract

The invention discloses a multi-source distillation-migration mechanical fault intelligent diagnosis method based on high-order moment matching, and the method comprises the steps: building a multi-source data set through the operation data collected from a plurality of mechanical devices, carrying out the preprocessing, and dividing the multi-source data set into a source domain data set, a target domain training data set, and a target domain test data set; constructing a multi-source distillation-transfer learning network model based on high-order moment matching, and performing high-order moment matching, maximum classifier difference and multi-source distillation training by using the source domain data set and the target domain training data set; and taking the target domain test data set as test input, and synthesizing outputs of the plurality of classifiers by using an adaptive weighting strategy to complete cross-domain fault diagnosis. According to the method, features of a source domain and a target domain are aligned at domain and category levels by utilizing multi-source data, the classification capability of the model on target samples is improved through multi-source distillation, and adaptive weighting is provided to integrate diagnosis results, so that the problem that the performance of a traditional method is reduced in cross-domain diagnosis is solved, and the performance of a deep model is greatly improved.

Description

technical field [0001] The invention relates to the field of fault diagnosis of mechanical equipment, in particular to a multi-source distillation-migration intelligent fault diagnosis method based on high-order moment matching. Background technique [0002] Large-scale rotating mechanical equipment such as fans, compressors and gas turbines are key production equipment in the national economy. Serious mechanical failures during their operation will pose a great threat to the safety of operators and equipment operation. Accurate and timely identification of faults arising and evolving during the operation of such mechanical equipment is of great significance to ensure the safe operation of mechanical equipment and avoid catastrophic accidents. [0003] In the traditional intelligent diagnosis methods of mechanical faults, the machine learning-based method relies on time-consuming feature engineering and has weak generalization ability due to its shallow structure; in the con...

AI Technical Summary

Problems solved by technology

However, the existing methods are all based on the model migration of a single source domain. In practice, it is a great waste to obtain historical data of multiple devices and various working conditions.

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