4,068 research outputs found
Early-stage gas identification using convolutional long short-term neural network with sensor array time series data
Gas identification/classification through pattern recognition techniques based on gas sensor arrays often requires the equilibrium responses or the full traces of time-series data of the sensor array. Leveraging upon the diverse gas sensing kinetics behaviors measured via the sensor array, a computational intelligence-based meta-model is proposed to automatically conduct the feature extraction and subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. The time-series data contains implicit temporal dependency/correlation that is worth being characterized to enhance the gas identification performance and reliability. In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network is developed to perform the identification task incorporating temporal characteristics within time-series data. This novel approach shows the enhanced accuracy and robustness as compared to the baseline models, i.e., multilayer perceptron (MLP) and support vector machine (SVM) through the comprehensive statistical examination. Specifically, the classification accuracy of CLSTM reaches as high as 96%, regardless of the operating condition specified. More importantly, the excellent gas identification performance of CLSTM at early stages of gas exposure indicates its practical significance in future real-time applications. The promise of the proposed method has been clearly illustrated through both the internal and external validations in the systematic case investigation
Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
With the rapid industrialization and technological advancements, innovative
engineering technologies which are cost effective, faster and easier to
implement are essential. One such area of concern is the rising number of
accidents happening due to gas leaks at coal mines, chemical industries, home
appliances etc. In this paper we propose a novel approach to detect and
identify the gaseous emissions using the multimodal AI fusion techniques. Most
of the gases and their fumes are colorless, odorless, and tasteless, thereby
challenging our normal human senses. Sensing based on a single sensor may not
be accurate, and sensor fusion is essential for robust and reliable detection
in several real-world applications. We manually collected 6400 gas samples
(1600 samples per class for four classes) using two specific sensors: the
7-semiconductor gas sensors array, and a thermal camera. The early fusion
method of multimodal AI, is applied The network architecture consists of a
feature extraction module for individual modality, which is then fused using a
merged layer followed by a dense layer, which provides a single output for
identifying the gas. We obtained the testing accuracy of 96% (for fused model)
as opposed to individual model accuracies of 82% (based on Gas Sensor data
using LSTM) and 93% (based on thermal images data using CNN model). Results
demonstrate that the fusion of multiple sensors and modalities outperforms the
outcome of a single sensor.Comment: 14 Pages, 9 Figure
An Adaptive GViT for Gas Mixture Identification and Concentration Estimation
Estimating the composition and concentration of ambient gases is crucial for
industrial gas safety. Even though other researchers have proposed some gas
identification and con-centration estimation algorithms, these algorithms still
suffer from severe flaws, particularly in fulfilling industry demands. One
example is that the lengths of data collected in an industrial setting tend to
vary. The conventional algorithm, yet, cannot be used to analyze the
variant-length data effectively. Trimming the data will preserve only
steady-state values, inevitably leading to the loss of vital information. The
gas identification and concentration estimation model called GCN-ViT(GViT) is
proposed in this paper; we view the sensor data to be a one-way chain that has
only been downscaled to retain the majority of the original in-formation. The
GViT model can directly utilize sensor ar-rays' variable-length real-time
signal data as input. We validated the above model on a dataset of 12-hour
uninterrupted monitoring of two randomly varying gas mixtures, CO-ethylene and
methane-ethylene. The accuracy of gas identification can reach 97.61%, R2 of
the pure gas concentration estimation is above 99.5% on average, and R2 of the
mixed gas concentration estimation is above 95% on average
Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper
The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.publishedVersio
Situation Awareness for Smart Distribution Systems
In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas
Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being
dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has
to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed
to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the
oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint,
more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become
greener, and hence to act in a manner not required previously.
While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for
many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance
technologies, that would foresee potential failures, making production safer, lowering downtime,
increasing productivity and diminishing maintenance costs. Many efforts were applied in order to
define the most accurate and effective predictive methods, however data scarcity affects the speed
and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest
in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection,
using the open public data from Petrobras, that was developed by experts.
For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM
and GRU backbones, were implemented for multi-class classification of undesirable events on naturally
flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on
Genetic Algorithms as being the most advanced methods for such kind of tasks.
The research concluded with the best performing algorithm with 2 stacked GRU and the following
vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units
47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%.
As the world faces many issues, one of which is the detrimental effect of heavy industries to the
environment and as result adverse global climate change, this project is an attempt to contribute to
the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it
more efficient, transparent and sustainable
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