# Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain

arXiv: https://arxiv.org/abs/2503.13534 · HTML: https://mlsanity.com/paper/2503.13534/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)

## Built on by (most cited first, 2 of 2)

- [Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition](https://mlsanity.com/paper/2505.24001/) (2025)
- [Cross-talk based multi-task learning for fault classification of machine system influenced by multiple variables](https://mlsanity.com/paper/2602.05146/) (2026)

## Abstract (arXiv)

This study presents a novel multi-output classification (MOC) framework designed for domain adaptation in fault diagnosis, addressing challenges posed by partially labeled (PL) target domain dataset and coexisting faults in rotating machinery. Unlike conventional multi-class classification (MCC) approaches, the MOC framework independently classifies the severity of each fault, enhancing diagnostic accuracy. By integrating multi-kernel maximum mean discrepancy loss (MKMMD) and entropy minimization loss (EM), the proposed method improves feature transferability between source and target domains, while frequency layer normalization (FLN) effectively handles stationary vibration signals by leveraging mechanical characteristics. Experimental evaluations across six domain adaptation cases, encompassing partially labeled (PL) scenarios, demonstrate the superior performance of the MOC approach over baseline methods in terms of macro F1 score.