Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
Classification of Systems-II01:31

Classification of Systems-II

651
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
651
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.1K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

8.7K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
8.7K
Aggregates Classification01:29

Aggregates Classification

1.0K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unifying network connectivity from geodesics to random walks via the random cluster model.

Nature communications·2026
Same author

Stability Switching and Oscillation Regulation Strategies for Large-Scale Fractional-Order Neural Networks With Double Hubs and Multiple Delays.

IEEE transactions on cybernetics·2026
Same author

Higher Order Interactions in Hub Neural Networks: Spatiotemporal Dynamics Reshaping and Control.

IEEE transactions on cybernetics·2026
Same author

A Soft and Robust Liquid Metal Textile Platform for Versatile Bioelectronic Applications.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

A Linearly Convergent Distributed Nash Equilibrium Seeking Algorithm for Aggregative Games Over Time-Varying Unbalanced Graphs.

IEEE transactions on cybernetics·2026
Same author

A Fixed Step-Size Algorithm for Distributed Optimization With Both Globally Coupled and Locally Separated Constraints.

IEEE transactions on cybernetics·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Videos

ABIGX: A Unified Framework for eXplainable Fault Detection and Classification.

Yue Zhuo, Jinchuan Qian, Junhua Zheng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 30, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ABIGX, a unified framework for explainable fault detection and classification (FDC). ABIGX enhances fault explanation accuracy and precision across various FDC models.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Fault detection and classification (FDC) models require robust explainability.
    • Existing methods like contribution plots (CP) and reconstruction-based contribution (RBC) have limitations in general FDC applications.
    • Gradient-based explanation methods struggle with fault class smearing, hindering accurate fault classification.

    Purpose of the Study:

    • To propose ABIGX (Adversarial fault reconstruction-Based Integrated Gradient eXplanation), a unified framework for explainable FDC.
    • To extend the applicability of established fault diagnosis principles to general FDC models.
    • To improve the precision and comprehensiveness of fault explanations.

    Main Methods:

    • Developed Adversarial Fault Reconstruction (AFR) by reframing fault reconstruction through adversarial attack perspectives.
    • Introduced a novel fault index for both fault detection and classification tasks.
    • Theoretically bridged ABIGX with CP and RBC, proving them as linear specifications of ABIGX in fault detection.

    Main Results:

    • Demonstrated that ABIGX effectively mitigates fault class smearing in classification tasks.
    • Showcased ABIGX outperforming current gradient-based explanation methods.
    • Validated the generality and accuracy of AFR through quantitative metrics and intuitive illustrations.

    Conclusions:

    • ABIGX provides more comprehensive and precise explanations for FDC models compared to existing methods.
    • The proposed AFR method is general and accurate, enhancing explainability in FDC.
    • ABIGX represents a significant advancement in explainable AI for fault detection and classification.