Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application

by   Te Han, et al.

In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.


page 1

page 8


Bearing fault diagnosis under varying working condition based on domain adaptation

Traditional intelligent fault diagnosis of rolling bearings work well on...

Unsupervised Deep Transfer Learning for Intelligent Fault Diagnosis: An Open Source and Comparative Study

Recent progress on intelligent fault diagnosis has greatly depended on t...

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

In the process industry, condition monitoring systems with automated fau...

mRMR-DNN with Transfer Learning for IntelligentFault Diagnosis of Rotating Machines

In recent years, intelligent condition-based monitoring of rotary machin...

Deep Learning based Intelligent Coin-tap Test for Defect Recognition

The coin-tap test is a convenient and primary method for non-destructive...

Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data

Artificial Intelligence (AI) is one of the approaches that has been prop...

Foundational Models for Fault Diagnosis of Electrical Motors

A majority of recent advancements related to the fault diagnosis of elec...

Please sign up or login with your details

Forgot password? Click here to reset