Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Navigation ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 333-351
Author(s):  
Jelena Gabela ◽  
Allison Kealy ◽  
Mark Hedley ◽  
Bill Moran
Keyword(s):  
Fault Detection ◽  
Spatial Feature ◽  
Sensor Positioning

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

Urban Fault Detection based on AETA: A Case Study on Line 9 Subway of Shenzhen, China

2021 ◽  
Author(s):  
Shanshan Yong ◽  
Qinmeng Guo ◽  
Xin'An Wang ◽  
Jing Wang ◽  
Chao Yang ◽  
...  
Keyword(s):  
Fault Detection ◽  
On Line

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

Case study results: fault detection in air-handling units in buildings

2018 ◽  
Vol 14 (3) ◽  
pp. 305-321 ◽  
Author(s):  
Suhrid Deshmukh ◽  
Leon Glicksman ◽  
Leslie Norford
Keyword(s):  
Fault Detection ◽  
Study Results ◽  
Air Handling Units

scholarly journals Combining Performance Testing and Metadata Models to Support Fault Detection and Diagnostics in Smart Buildings

2019 ◽  
Vol 2 (3) ◽  
pp. 28
Author(s):  
Elena Markoska ◽  
Aslak Johansen ◽  
Mikkel Baun Kjærgaard ◽  
Sanja Lazarova-Molnar ◽  
Muhyiddine Jradi ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Fault Detection ◽  
Performance Test ◽  
Performance Testing ◽  
Performance Tests ◽  
Actual Performance ◽  
Hypothetical Scenario ◽  
Wide Range ◽  
Promising Practice

Performance testing of components and subsystems of buildings is a promising practice for increasing energy efficiency and closing gaps between intended and actual performance of buildings. A typical shortcoming of performance testing is the difficulty of linking a failing test to a faulty or underperforming component. Furthermore, a failing test can also be linked to a wrongly configured performance test. In this paper, we present Building Metadata Performance Testing (BuMPeT), a method that addresses this shortcoming by using building metadata models to extend performance testing with fault detection and diagnostics (FDD) capabilities. We present four different procedures that apply BuMPeT to different data sources and components. We have applied the proposed method to a case study building, located in Denmark, to test its capacity and benefits. Additionally, we use two real case scenarios to showcase examples of failing performance tests in the building, as well as discovery of causes of underperformance. Finally, to examine the limits to the benefits of the applied procedure, a detailed elaboration of a hypothetical scenario is presented. Our findings demonstrate that the method has potential and it can serve to increase the energy efficiency of a wide range of buildings.

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