Using nonlinear black-box models in fault detection

2002 ◽  
Author(s):  
Qinghua Zhang
Keyword(s):  
Fault Detection ◽  
Black Box ◽  
Box Models ◽  
Black Box Models

Black-box models for fault detection and performance monitoring of buildings

2010 ◽  
Vol 3 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Dirk Jacob ◽  
Sebastian Dietz ◽  
Susanne Komhard ◽  
Christian Neumann ◽  
Sebastian Herkel
Keyword(s):  
Fault Detection ◽  
Performance Monitoring ◽  
Black Box ◽  
Box Models ◽  
And Performance ◽  
Black Box Models

scholarly journals Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6749
Author(s):  
Reda El Bechari ◽  
Stéphane Brisset ◽  
Stéphane Clénet ◽  
Frédéric Guyomarch ◽  
Jean Claude Mipo
Keyword(s):  
Branch And Bound ◽  
Prediction Error ◽  
Global Solutions ◽  
Black Box ◽  
High Fidelity ◽  
Electromagnetic Devices ◽  
Box Models ◽  
Element Simulation ◽  
The Cost ◽  
Black Box Models

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

Creating Dynamic Pretrade Models: Beyond the Black Box

2018 ◽  
Keyword(s):  
Functional Form ◽  
Information Leakage ◽  
Black Box ◽  
Sensitivity Analyses ◽  
Stock Selection ◽  
Market Participants ◽  
Box Models ◽  
Portfolio Managers ◽  
Black Box Models ◽  
Shed Light

We provide a framework for investment managers to create dynamic pretrade models. The approach helps market participants shed light on vendor black-box models that often do not provide any transparency into the model’s functional form or working mechanics. In addition, this allows portfolio managers to create consensus estimates based on their own expectations, such as forecasted liquidity and volatility, and to incorporate firm proprietary alpha estimates into the solution. These techniques allow managers to reduce overdependency on any one black-box model, incorporate costs into the stock selection and portfolio optimization phase of the investment cycle, and perform “what-if” and sensitivity analyses without the risk of information leakage to any outside party or vendor.

scholarly journals The Role of Textualisation and Argumentation in Understanding the Machine Learning Process

2017 ◽  
Author(s):  
Kacper Sokol ◽  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Spatial Perception ◽  
Black Box ◽  
High Dimensional ◽  
Box Models ◽  
Machine Learning Applications ◽  
Black Box Models ◽  
Machine Learning Models

Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation -- a natural language narrative of selected phenomena -- can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.

Transformer Modelling for Impulse Voltage Distribution and Terminal Transient Analysis

2013 ◽  
pp. 239-320
Author(s):  
Marjan Popov ◽  
Bjørn Gustavsen ◽  
Juan A. Martinez-Velasco
Keyword(s):  
Black Box ◽  
Electromagnetic Transients ◽  
Main Section ◽  
Laboratory Measurements ◽  
Voltage Distribution ◽  
Box Models ◽  
Transformer Winding ◽  
Lightning Discharges ◽  
Transient Events ◽  
Black Box Models

Voltage surges arising from transient events, such as switching operations or lightning discharges, are one of the main causes of transformer winding failure. The voltage distribution along a transformer winding depends greatly on the waveshape of the voltage applied to the winding. This distribution is not uniform in the case of steep-fronted transients since a large portion of the applied voltage is usually concentrated on the first few turns of the winding. High frequency electromagnetic transients in transformers can be studied using internal models (i.e., models for analyzing the propagation and distribution of the incident impulse along the transformer windings), and black-box models (i.e., models for analyzing the response of the transformer from its terminals and for calculating voltage transfer). This chapter presents a summary of the most common models developed for analyzing the behaviour of transformers subjected to steep-fronted waves and a description of procedures for determining the parameters to be specified in those models. The main section details some test studies based on actual transformers in which models are validated by comparing simulation results to laboratory measurements.

Sign in / Sign up

Export Citation Format

Share Document