Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Chao Chen,Fei Shen,Jiawen Xu,Ruqiang Yan

doi:10.1186/s10033-020-00520-9

Chao Chen, Fei Shen + Show 2 more

Open Access

https://doi.org/10.1186/s10033-020-00520-9

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Abstract

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications. However, the various working conditions would degrade the diagnostic performance and make gear fault diagnosis (GFD) more and more challenging. In this paper, a novel model parameter transfer (NMPT) is proposed to boost the performance of GFD under varying working conditions. Based on the previous transfer strategy that controls empirical risk of source domain, this method further integrates the superiorities of multi-task learning with the idea of transfer learning (TL) to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition (target domain) and another (source domain), and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable. For NMPT implementation, insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task. Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions. The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

Highlights

Gear has been used extensively in transmission system due to its large velocity ratio, strong bearing capacity, compactness and high efficiency [1,2,3,4]
The target data are fed into novel model parameter transfer (NMPT) to output the predicted fault categories
Where p denotes the final decomposition level, Hi is the ith proper rotation components (PRCs), Lp is the remaining baseline signal. These obtained PRCs with intrinsic time-scale decomposition (ITD) technology are too complex to be taken as fault vectors as inputs for conducting fault classification directly

Summary

Introduction

Gear has been used extensively in transmission system due to its large velocity ratio, strong bearing capacity, compactness and high efficiency [1,2,3,4]. According to the above analysis, a novel model parameter transfer (NMPT) approach, which aims at excavating and further transferring the shared characteristic parameters of hyperplane for the problems of insufficient labeled training samples and non-IID between source and target domains, is developed to assist target gear fault identification using source domain data On this basis of controlling the empirical risk of source domain, the proposed method further integrates the advantage of the conventional MPT and TL together, which can be concluded that: (a) the least square support vector machine (LSSVM) based MPT can characterize the shared and domain-specific parameters over tasks; and (b) the idea of TL is introduced to dig and extract transferable knowledge and to minimize the. Different from the single task learning and multitask learning, the proposed NMPT utilizes SD data (related but different from TD) to solve target domain problems with a specific structure, which is introduced

Basic Definition

Experiment and Discussion

Complete Process of NMPT Model for Gear Fault Diagnosis

Conclusions