Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis

• URL: http://arxiv.org/abs/2604.16459v1
• Date: Wed, 08 Apr 2026 06:25:40 GMT
• Title: Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis
• Abstract summary: Fault intensity diagnosis (FID) plays a pivotal role in intelligent manufacturing.<n>This paper introduces a novel framework with deep hierarchical knowledge loss to achieve hierarchical consistent representation and prediction.<n>Experiments are performed on four real-world datasets from various industrial domains.
• Score: 16.42275905622711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fault intensity diagnosis (FID) plays a pivotal role in intelligent manufacturing while neglecting dependencies among target classes hinders its practical deployment. This paper introduces a novel and general framework with deep hierarchical knowledge loss (DHK) to achieve hierarchical consistent representation and prediction. We develop a novel hierarchical tree loss to enable a holistic mapping of same-attribute classes, leveraging tree-based positive and negative hierarchical knowledge constraints. We further design a focal hierarchical tree loss to enhance its extensibility and devise two adaptive weighting schemes based on tree height. In addition, we propose a group tree triplet loss with hierarchical dynamic margin by incorporating hierarchical group concepts and tree distance to model boundary structural knowledge across classes. The joint two losses significantly improve the recognition of subtle faults. Extensive experiments are performed on four real-world datasets from various industrial domains (three cavitation datasets from SAMSON AG and one publicly available dataset) for FID, all showing superior results and outperforming recent state-of-the-art FID methods.

Related papers

• Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity [1.5166105038254163]
  We introduce SCALA (Scaffolded Cognitive Architecture for Learning under limited dAta), a hierarchical learning framework grounded in cognitive psychology.<n>Our model exhibits human-like cognitive selectivity by effectively prioritizing task-relevant features while suppressing background distractors.<n> Empirically, SCALA significant accuracy improvements under severe data scarcity.
  arXiv  Detail & Related papers  (2026-07-06T06:24:32Z)
• Online Continual Learning with Dynamic Label Hierarchies [72.33335166136045]
  We introduce a new problem setting, DHOCL, where severity evolve across granularities and each sample provides supervision at a single hierarchical level.<n>In this setting, we find two fundamental issues: (i) partial supervision under mixed granularities provides only point-wise signals over an evolving path-wise hierarchy, which constrains plasticity and undermines cross-level semantic consistency.<n>We propose HALO, which adaptively combines complementary classification heads, regularized by organized learnable hierarchical prototypes, enabling rapid adaptation, hierarchical consistency, and structured knowledge consolidation as the taxonomy evolves.
  arXiv  Detail & Related papers  (2026-05-12T08:20:23Z)
• Hierarchically Robust Zero-shot Vision-language Models [74.79155369056639]
  Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks.<n>Existing approaches align fixed text embeddings with an image embedding, sacrificing natural performance and robustness.<n>We propose a novel adversarial fine-tuning framework based on hierarchical embeddings and adversarially robust alignment of image-text modalities.
  arXiv  Detail & Related papers  (2026-04-20T21:42:14Z)
• Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis [10.444771588267436]
  We propose the Brain Hierarchical Organization Learning (BrainHO) to learn inherently hierarchical brain network dependencies.<n>Specifically, we design a hierarchical attention mechanism that allows the model to aggregate nodes into a hierarchical organization.<n>Experiments on the publicly available ABIDE and REST-meta-MDD datasets demonstrate that BrainHO not only achieves state-of-the-art classification performance but also uncovers interpretable, clinically significant biomarkers.
  arXiv  Detail & Related papers  (2026-03-10T12:49:45Z)
• Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining [58.69016084278948]
  We consider a hierarchical context-free grammar introduced by arXiv:2307.02129 and conjectured to separate deep and shallow networks.<n>We prove that, under mild conditions, a deep convolutional network can be efficiently trained to learn this function class.
  arXiv  Detail & Related papers  (2026-01-27T16:19:54Z)
• Learning Consistent Taxonomic Classification through Hierarchical Reasoning [61.372270953201955]
  We propose a two-stage, hierarchy-based reasoning framework designed to improve leaf-level accuracy and hierarchical consistency in taxonomic classification.<n>Our framework, implemented on the Qwen2.5-VL-7B model, outperforms its original 72B counterpart by over 10% in both leaf-level and hierarchical consistency accuracy.
  arXiv  Detail & Related papers  (2026-01-21T03:00:00Z)
• A Semantics-Aware Hierarchical Self-Supervised Approach to Classification of Remote Sensing Images [12.282079123411947]
  We present a novel Semantics-Aware Hierarchical Consensus (SAHC) method for learning hierarchical features and relationships.<n>The SAHC method is evaluated on three benchmark datasets with different degrees of hierarchical complexity.<n> Experimental results show both the effectiveness of the proposed approach in guiding network learning and the robustness of the hierarchical consensus for remote sensing image classification tasks.
  arXiv  Detail & Related papers  (2025-10-06T15:30:39Z)
• Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems [9.830457900441939]
  We propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought.<n>The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of global hierarchical classifiers.<n>In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix.
  arXiv  Detail & Related papers  (2025-08-17T14:22:03Z)
• Hierarchical Quantized Diffusion Based Tree Generation Method for Hierarchical Representation and Lineage Analysis [49.00783841494125]
  HDTree captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and quantized diffusion processes.<n> HDTree's effectiveness is demonstrated through comparisons on both general-purpose and single-cell datasets.<n>These contributions provide a new tool for hierarchical lineage analysis, enabling more accurate and efficient modeling of cellular differentiation paths.
  arXiv  Detail & Related papers  (2025-06-29T15:19:13Z)
• Hybrid Losses for Hierarchical Embedding Learning [4.2525210928495625]
  We investigate hybrid losses, such as generalised triplet and cross-entropy losses, within a multi-task learning framework.<n>Our experiments on OrchideaSOL, a four-level hierarchical instrument sound dataset, demonstrate that the proposed hybrid losses outperform previous works in classification, retrieval, embedding space structure, and generalisation.
  arXiv  Detail & Related papers  (2025-01-22T10:58:04Z)
• Hierarchical clustering with dot products recovers hidden tree structure [53.68551192799585]
  In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cluster variance. We demonstrate that the tree output by this algorithm provides a bona fide estimate of generative hierarchical structure in data, under a generic probabilistic graphical model.
  arXiv  Detail & Related papers  (2023-05-24T11:05:12Z)
• Provable Hierarchy-Based Meta-Reinforcement Learning [50.17896588738377]
  We analyze HRL in the meta-RL setting, where learner learns latent hierarchical structure during meta-training for use in a downstream task. We provide "diversity conditions" which, together with a tractable optimism-based algorithm, guarantee sample-efficient recovery of this natural hierarchy. Our bounds incorporate common notions in HRL literature such as temporal and state/action abstractions, suggesting that our setting and analysis capture important features of HRL in practice.
  arXiv  Detail & Related papers  (2021-10-18T17:56:02Z)
• Learn Class Hierarchy using Convolutional Neural Networks [0.9569316316728905]
  We propose a new architecture for hierarchical classification of images, introducing a stack of deep linear layers with cross-entropy loss functions and center loss combined. We experimentally show that our hierarchical classifier presents advantages to the traditional classification approaches finding application in computer vision tasks.
  arXiv  Detail & Related papers  (2020-05-18T12:06:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.