Dynamic Model-Embedded Intelligent Machine Fault Diagnosis Without Fault Data

Abstract

Intelligent machine fault diagnosis technique has recently exploded interest in digital health, energy power, and industrial maintenance. Collecting machine fault data in engineering practice is usually costly, causing big challenges in the intelligent diagnosis of most fresh-from-the-factory machines that are missing fault data. Inspired by the main idea of the digital twin, this paper proposes a dynamic model-embedded intelligent machine fault diagnosis framework. Specifically, a machine dynamic modeling and parameter identification approach is introduced for building the machine digital model. The digital model is used to predict machine fault data from the measured healthy vibration signals. Furthermore, a parameterized convolutional neural network structure is designed for learning optimization features and recognizing the machine's healthy state. Experimental investigation demonstrates the effectiveness of the framework. The results show that the proposed framework enables intelligent machine fault diagnosis without fault data. The diagnosis effect outperforms supervised, unsupervised and small-sample learning approaches and can be generalized to another load and speed with acceptable accuracy.