A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines
KTP Nguyen, K Medjaher, DT Tran - Artificial Intelligence Review, 2023 - Springer
The past decade has witnessed the adoption of artificial intelligence (AI) in various
applications. It is of no exception in the area of prognostics and health management (PHM) …
applications. It is of no exception in the area of prognostics and health management (PHM) …
A holistic fault impact analysis of the high-performance sequences of operation for HVAC systems: Modelica-based case study in a medium-office building
ASHRAE Guideline 36: High-performance sequences of operation (SOO) for Heating,
Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30 …
Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30 …
An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications
Deep learning-based models, while highly effective for prognostics and health management,
fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution …
fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution …
[Retracted] SWOT: A Hybrid Hardware‐Based Approach for Robust Fault‐Tolerant Framework in a Smart Day Care
Internet of Things (IoT) has made its imprint on every part of the globe today. Offices,
households, factories, industries, agriculture, and day cares, among other places, have all …
households, factories, industries, agriculture, and day cares, among other places, have all …
A semi-supervised autoencoder with an auxiliary task (SAAT) for power transformer fault diagnosis using dissolved gas analysis
This paper proposes a semi-supervised autoencoder with an auxiliary task (SAAT) to extract
a health feature space for power transformer fault diagnosis using dissolved gas analysis …
a health feature space for power transformer fault diagnosis using dissolved gas analysis …
A performance evaluation framework for building fault detection and diagnosis algorithms
Fault detection and diagnosis (FDD) algorithms for building systems and equipment
represent one of the most active areas of research and commercial product development in …
represent one of the most active areas of research and commercial product development in …
A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings
In data-driven automated fault detection and diagnostics (AFDD) modeling for building
energy systems, feature engineering is a critical process of extracting information from high …
energy systems, feature engineering is a critical process of extracting information from high …
Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation
The performance of data-driven fault detection and diagnostics (FDD) is heavily dependent
on sensors. However, sensor inaccuracy and sensor faults are pervasive in building …
on sensors. However, sensor inaccuracy and sensor faults are pervasive in building …
Fault diagnosis of DCV and heating systems based on causal relation in fuzzy bayesian belief networks using relation direction probabilities
A Behravan, B Kiamanesh, R Obermaisser - Energies, 2021 - mdpi.com
The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each
category has its strengths and shortcomings. The knowledge-driven methods rely mainly on …
category has its strengths and shortcomings. The knowledge-driven methods rely mainly on …
[PDF][PDF] The Use of eXplainable Artificial Intelligence and Machine Learning Operation Principles to Support the Continuous Development of Machine Learning-Based …
Machine learning (ML) revolutionized traditional machine fault detection and identification
(FDI), as complex-structured models with well-designed unsupervised learning strategies …
(FDI), as complex-structured models with well-designed unsupervised learning strategies …