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

Fault Types01:18

Fault Types

149
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
149
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

198
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
198
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

999
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
999
Survival Tree01:19

Survival Tree

180
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
180
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K
Observational Learning01:12

Observational Learning

372
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
372

You might also read

Related Articles

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

Sort by
Same author

Structural-budgeted QUBO learning of Bayesian networks with spectral and credibility diagnostics.

Scientific reports·2026
Same author

Evaluating Technicians' Workload and Performance in Diagnosis for Corrective Maintenance.

Sensors (Basel, Switzerland)·2024
Same author

Leveraging Active Learning for Failure Mode Acquisition.

Sensors (Basel, Switzerland)·2023
Same author

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems.

Sensors (Basel, Switzerland)·2022
Same author

Real-Time Sensing of Output Polymer Flow Temperature and Volumetric Flowrate in Fused Filament Fabrication Process.

Materials (Basel, Switzerland)·2022
Same author

Sensor Selection Framework for Designing Fault Diagnostics System.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 12, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K

Fusion-Learning of Bayesian Network Models for Fault Diagnostics.

Toyosi Ademujimi1, Vittaldas Prabhu1

  • 1Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fusion-learning method for Bayesian Network (BN) models, integrating quantitative sensor data with qualitative maintenance logs to enhance fault diagnosis accuracy and coverage in systems like uninterruptible power supplies (UPS).

Keywords:
Bayesian Networkfault diagnosticsfusion-learningnatural language processingsmart maintenancetechnical language processing

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Related Experiment Videos

Last Updated: Oct 12, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Bayesian Network (BN) models are widely used for fault diagnosis, primarily trained on quantitative sensor data.
  • Existing BN models often suffer from incomplete fault coverage due to sensor limitations.
  • Maintenance logs contain valuable qualitative data, including unstructured natural language with technical terms, that is underutilized.

Purpose of the Study:

  • To develop a fusion-learning method for Bayesian Networks (BNs) that integrates both quantitative and qualitative data sources.
  • To improve the accuracy and fault coverage of BN models for enhanced equipment diagnostics.
  • To incorporate a human-in-the-loop approach for expert knowledge elicitation using natural language data.

Main Methods:

  • Proposed a fusion-learning approach for BNs, combining quantitative data (sensors, metrology) and qualitative data (maintenance logs, reports).
  • Developed a method to fuse two separately learned BNs derived from different data types.
  • Introduced a human-in-the-loop expert knowledge elicitation strategy, leveraging logged natural language data to aid BN structure definition.

Main Results:

  • The proposed fused BN model demonstrated improved diagnostic capabilities compared to individual BNs.
  • The method achieved wider fault coverage by incorporating diverse data sources.
  • Real-world data from uninterruptible power supply (UPS) fault diagnostics validated the efficacy of the approach.

Conclusions:

  • Fusion-learning of BNs by integrating quantitative and qualitative data significantly enhances fault diagnosis.
  • The human-in-the-loop approach, aided by natural language processing of maintenance logs, improves BN model development.
  • The developed method offers a more comprehensive and accurate fault diagnostic solution, increasing equipment uptime and customer service.