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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Mechanical Efficiency of Real Machines01:14

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
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Distribution Reliability and Automation01:25

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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...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Related Experiment Video

Updated: Nov 18, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach.

Yassine Bouabdallaoui1, Zoubeir Lafhaj1, Pascal Yim2

  • 1Laboratoire de Mécanique Multiphysique Multiéchelle, LaMcube, UMR 9013, Centrale Lille, CNRS, Université de Lille, F-59000 Lille, France.

Sensors (Basel, Switzerland)
|February 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework for predictive maintenance in buildings, aiming to reduce energy waste. The framework successfully predicted failures in a sports facility

Keywords:
HVACIoTautoencodersbuildingsdatamachine learningpredictive maintenance

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Design and Analysis for Fall Detection System Simplification
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Area of Science:

  • Building Operations & Maintenance
  • Artificial Intelligence in Engineering
  • Sustainable Building Management

Background:

  • Building maintenance is often inefficient, leading to significant energy waste.
  • Information and Communication Technology (ICT) solutions have improved management but not efficiency.
  • Current practices lack proactive strategies for preventing equipment failures.

Purpose of the Study:

  • To propose a predictive maintenance framework for building installations.
  • To provide guidelines for implementing machine learning-based predictive maintenance.
  • To demonstrate the framework's efficacy in a real-world building setting.

Main Methods:

  • Developed a five-step framework: data collection, processing, model development, fault notification, and model improvement.
  • Utilized Internet of Things (IoT) devices and Building Automation Systems (BAS) for data collection.
  • Employed a deep learning model for failure prediction in Heating, Ventilation, and Air Conditioning (HVAC) systems.

Main Results:

  • The case study demonstrated the framework's potential for accurate failure prediction.
  • Identified obstacles including data availability and feedback collection challenges.
  • Validated the predictive capabilities of the deep learning model for HVAC installations.

Conclusions:

  • The proposed framework offers a viable approach to implementing predictive maintenance in buildings.
  • Guidelines are provided for scientists and practitioners to adopt these advanced maintenance strategies.
  • Further work is needed to address data-related barriers for widespread implementation.