Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Robust Deep Learning-Based Fault Diagnosis Method for Rotating Machinery

Version 1 : Received: 20 May 2020 / Approved: 22 May 2020 / Online: 22 May 2020 (05:37:13 CEST)

A peer-reviewed article of this Preprint also exists.


In the recent years, intelligent data-driven faultdiagnosis methods on gearboxes have been successfully developedand popularly applied in the industries. Currently, most ofthe machine learning techniques require that the training andtesting data are from the same distribution. However, thisassumption is difficult to be met in the real industries, sincethe gearbox operating conditions usually change in practice,which results in significant data distribution gap and diagnosticperformance deteriorations in applying the learned knowledgeon the new conditions. This paper proposes a deep learning-based domain adaptation method to address this issue. Theraw current signals are directly used as the model inputs fordiagnostics, which are easy to collect in the real industries andfacilitate practical applications. The maximum mean discrepancymetric is introduced to the deep neural network, the optimizationof which guarantees the extraction of generalized machineryhealth condition features across different operating conditions.The experiments on a real-world gearbox condition monitoringdataset validate the effectiveness of the proposed method, whichoffers a promising tool for cross-domain diagnosis in the realindustries.


fault diagnosis; deep learning; domain adapta-tion; gearbox; current signal


Engineering, Mechanical Engineering

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