Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis

by   Jong Moon Ha, et al.

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.


page 5

page 6

page 7

page 8

page 10

page 19


Controlled Generation of Unseen Faults for Partial and OpenSet Partial Domain Adaptation

New operating conditions can result in a performance drop of fault diagn...

Remaining Useful Lifetime Prediction via Deep Domain Adaptation

In Prognostics and Health Management (PHM) sufficient prior observed deg...

Bearing fault diagnosis under varying working condition based on domain adaptation

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

Pinning Fault Mode Modeling for DWM Shifting

Extreme scaling for purposes of achieving higher density and lower energ...

Novel features for the detection of bearing faults in railway vehicles

In this paper, we address the challenging problem of detecting bearing f...

Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of Chemical Processes

Fault diagnosis is an essential component in process supervision. Indeed...

Please sign up or login with your details

Forgot password? Click here to reset