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Related Concept Videos

Refrigerators and Heat Pumps01:07

Refrigerators and Heat Pumps

Refrigerators or heat pumps are heat engines operating in a reverse direction. For a refrigerator, the focus is on removing heat from a specific area, whereas, for a heat pump, the focus is on dumping heat into one particular area. A refrigerator (or heat pump) absorbs heat Qc from the cold reservoir at Kelvin temperature Tc and discards heat Qh to the hot reservoir at Kelvin temperature Th, while work W is done on the engine’s working substance.
A household refrigerator removes heat from the...
Heating and Cooling Curves02:44

Heating and Cooling Curves

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...
Temperature Dependent Deformation01:12

Temperature Dependent Deformation

In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added together...
Thermal Stress01:09

Thermal Stress

If the temperature of an object is changed while it is prevented from expanding or contracting, the object is subjected to stress. The stress is compressive if the object expands in the absence of constraint and tensile if it contracts. This stress resulting from temperature change is known as thermal stress. It can be quite large and can cause damage. To avoid this stress, engineers may design components so they can expand and contract freely. For instance, on highways, gaps are deliberately...

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Related Experiment Video

Updated: May 28, 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

A Multi-Gradient-Descent-Integrated Physics-Informed Autoencoder for Sensor Fault Detection in Data Center Chillers.

Xinyue Shen1, Pan Li1, Chen Xu1

  • 1School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

A new Multi-Gradient Descent Algorithm-integrated Physics-Informed Autoencoder (MGDA-PIAE) enhances chiller fault detection in data centers. This improved system offers greater accuracy and reliability for stable, efficient operations.

Keywords:
chillerdata centermultiple gradient descent algorithmphysics-informed autoencodersensor fault detection

Related Experiment Videos

Last Updated: May 28, 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

Area of Science:

  • Data Center Operations
  • Artificial Intelligence in Engineering
  • Thermal Management Systems

Background:

  • Chiller performance is crucial for data center temperature stability and energy efficiency.
  • Existing sensor fault detection methods, like Physics-Informed Autoencoders (PIAE), struggle with complex faults and generalization.
  • Challenges include gradient conflicts and limited adaptability to sensor drift, stuck faults, and precision attenuation.

Purpose of the Study:

  • To develop an advanced sensor fault detection system for data center chillers.
  • To overcome the limitations of existing PIAE models in handling complex fault scenarios.
  • To improve the accuracy, robustness, and adaptability of fault detection for enhanced operational stability.

Main Methods:

  • Proposed a novel Multi-Gradient Descent Algorithm-integrated Physics-Informed Autoencoder (MGDA-PIAE).
  • Embedded the chiller thermal balance equation as a hard constraint within the model.
  • Dynamically determined Pareto-optimal weights balancing data reconstruction and physical consistency.

Main Results:

  • MGDA-PIAE demonstrated significantly improved performance over conventional Autoencoders (AE) and PIAE models.
  • Average recall increased by ~20%, and F1-score by ~10% in validation tests.
  • For flow sensor faults, the F1-score improved by over 80% compared to AE and 20% compared to PIAE.

Conclusions:

  • The MGDA-PIAE model offers superior generalization and stable performance across diverse operational conditions.
  • It achieves high-precision fault detection with low false negatives, adaptable to various sensor types and operating modes.
  • The system provides a practical, reliable solution for maintaining efficient, stable, and safe data center refrigeration operations.