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

399
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...
399
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

489
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
489
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

663
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
663
Heating and Cooling Curves02:44

Heating and Cooling Curves

26.5K
When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
26.5K
Bus Impedance Matrix01:24

Bus Impedance Matrix

497
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
497
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

516
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
516

You might also read

Related Articles

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

Sort by
Same author

The impact of Sjögren's disease on ovarian reserve: a systematic review and meta-analysis.

Clinical and experimental rheumatology·2026
Same author

Quality changes in plum puree based on anti-Browning treatment: Nonvolatile and volatile compounds analysis.

Food chemistry·2026
Same author

Transcription factor ID3 promotes fibroblast differentiation and proliferation in lung fibrosis through augmenting the TGF-β signaling pathway.

Molecular immunology·2026
Same author

A Glycyrrhiza Glabra-derived pH/ROS Dual-Responsive Phytomedicine Nanoplatform for Combatting Triple-Negative Breast Cancer via Amplified Mitochondrial Damage.

Advanced healthcare materials·2026
Same author

A supersandwich fluorescent aptasensor leveraging the synergy of mini-nanotetrahedron, high-affinity split aptamer and rolling circle amplification for kanamycin analysis in milk.

Food chemistry·2026
Same author

The association between cystatin C and hip fracture risk: A longitudinal cohort study of China Health and Retirement Longitudinal Study (CHARLS).

Experimental gerontology·2026

Related Experiment Video

Updated: Jan 13, 2026

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

2.1K

Labeled Datasets for Air Handling Units Operating in Faulted and Fault-free States.

Naghmeh Ghalamsiah1, Jin Wen2, Guowen Li3

  • 1Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA, USA.

Scientific Data
|January 9, 2026
PubMed
Summary

This study introduces eight new datasets for air handling unit (AHU) fault detection and diagnosis. It also identifies optimal AHU dataset pairs for evaluating transfer learning (TL) algorithms in HVAC systems.

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.8K
Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

17.3K

Related Experiment Videos

Last Updated: Jan 13, 2026

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

2.1K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.8K
Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

17.3K

Area of Science:

  • Building energy systems
  • Artificial intelligence in engineering
  • Data science for infrastructure

Background:

  • Data-driven fault detection and diagnosis (FDD) for HVAC systems is advancing.
  • Labeled datasets for FDD are scarce, hindering new method development.
  • Transfer learning (TL) shows promise for FDD across different buildings, but requires suitable comparative datasets.

Purpose of the Study:

  • To address the lack of comparative studies for TL evaluation in building systems.
  • To publish new datasets for air handling unit (AHU) fault analysis.
  • To identify optimal AHU dataset pairs for evaluating TL algorithms.

Main Methods:

  • Publication of eight new AHU datasets covering fault-free and various faulty conditions.
  • Comprehensive analysis of AHU fault datasets to identify suitable pairs for TL.
  • Evaluation of dataset similarities and differences for inter-dataset transfer learning.

Main Results:

  • Eight new AHU datasets are now publicly available.
  • Identification of specific AHU fault dataset pairs suitable for TL evaluation.
  • Methodology established for selecting comparative datasets in building FDD.

Conclusions:

  • The new datasets and comparative analysis facilitate research in TL for building HVAC FDD.
  • This work enables more robust evaluation of TL algorithms across diverse building systems.
  • Progress in inter-dataset studies for building FDD is accelerated.