Real-Time Multi-Event Anomaly Detection using Elliptic Envelope and A Deep Neural Network for Enhanced MPD Robustness

2021 ◽  
Author(s):  
Qifan Gu ◽  
Amirhossein Fallah ◽  
Pradeepkumar Ashok ◽  
Dongmei Chen ◽  
Eric Van Oort
Keyword(s):  
Neural Network ◽  
Event Detection ◽  
Deep Neural Network ◽  
Change Point Detection ◽  
False Alarms ◽  
Two Phase ◽  
Signal Noise ◽  
Lost Circulation ◽  
Gas Kick ◽  
Few Data

Abstract In managed pressure drilling (MPD), robust and fast event detection is critical for timely event identification and diagnosis, as well as executing well control actions as quickly as possible. In current event detection systems (EDSs), signal noise and uncertainties often cause missed and false alarms, and automated diagnosis of the event type is usually restricted to certain event types. A new EDS method is proposed in this paper to overcome these shortcomings. The new approach uses a multivariate online change point detection (OCPD) method based on elliptic envelope for event detection. The method is robust against signal noise and uncertainties, and is able to detect abnormal features within a minute or less, using only a few data points. A deep neural network (DNN) is utilized for estimating the occurrence probability of various drilling events, currently encompassing (but not limited to) six event types: liquid kick, gas kick, lost circulation, plugged choke, plugged bit, and drillstring washout. The OCPD and the DNN are integrated together and demonstrate better performance with respect to robustness and accuracy. The training and testing of the OCPD and the DNN were conducted on a large dataset representing various drilling events, which was generated using a field-validated two-phase hydraulics software. Compared to current EDS methods, the new system shows the following advantages: (1) lower missed alarm rate; (2) lower false alarm rate; (3) earlier alarming; and (4) significantly improved classification capability that also allows for further extension to even more drilling events.

Using multi-stream hierarchical deep neural network to extract deep audio feature for acoustic event detection

2017 ◽  
Vol 77 (1) ◽  
pp. 897-916 ◽  
Author(s):  
Yanxiong Li ◽  
Xue Zhang ◽  
Hai Jin ◽  
Xianku Li ◽  
Qin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Event Detection ◽  
Deep Neural Network ◽  
Acoustic Event Detection ◽  
Acoustic Event ◽  
Audio Feature

Event Detection and Control of Blood Glucose Levels Using Deep Neural Network

2021 ◽  
pp. 235-245
Author(s):  
Divya Govindaraju ◽  
R. R. Rashmika Shree ◽  
S. Priyanka ◽  
S. Porkodi ◽  
Sutha Subbian
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Event Detection ◽  
Deep Neural Network ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
And Control

scholarly journals Joint Optimization of Deep Neural Network-Based Dereverberation and Beamforming for Sound Event Detection in Multi-Channel Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1883 ◽  
Author(s):  
Kyoungjin Noh ◽  
Joon-Hyuk Chang
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
Event Detection ◽  
Deep Neural Network ◽  
Joint Optimization ◽  
Audio Signals ◽  
Sound Event ◽  
Minimum Variance Distortionless Response ◽  
Reverberant Environments ◽  
Sound Event Detection

In this paper, we propose joint optimization of deep neural network (DNN)-supported dereverberation and beamforming for the convolutional recurrent neural network (CRNN)-based sound event detection (SED) in multi-channel environments. First, the short-time Fourier transform (STFT) coefficients are calculated from multi-channel audio signals under the noisy and reverberant environments, which are then enhanced by the DNN-supported weighted prediction error (WPE) dereverberation with the estimated masks. Next, the STFT coefficients of the dereverberated multi-channel audio signals are conveyed to the DNN-supported minimum variance distortionless response (MVDR) beamformer in which DNN-supported MVDR beamforming is carried out with the source and noise masks estimated by the DNN. As a result, the single-channel enhanced STFT coefficients are shown at the output and tossed to the CRNN-based SED system, and then, the three modules are jointly trained by the single loss function designed for SED. Furthermore, to ease the difficulty of training a deep learning model for SED caused by the imbalance in the amount of data for each class, the focal loss is used as a loss function. Experimental results show that joint training of DNN-supported dereverberation and beamforming with the SED model under the supervision of focal loss significantly improves the performance under the noisy and reverberant environments.

scholarly journals An Improved Attention-Based Integrated Deep Neural Network for PM2.5 Concentration Prediction

2021 ◽  
Vol 11 (9) ◽  
pp. 4001
Author(s):  
Pengfei Shi ◽  
Xiaolong Fang ◽  
Jianjun Ni ◽  
Jinxiu Zhu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Two Phase ◽  
Prediction Result ◽  
Air Quality Prediction ◽  
Proposed Model ◽  
Network Method ◽  
Dual Stage ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

The air quality prediction is a very important and challenging task, especially PM2.5 (particles with diameter less than 2.5 μm) concentration prediction. To improve the accuracy of the PM2.5 concentration prediction, an improved integrated deep neural network method based on attention mechanism is proposed in this paper. Firstly, the influence of exogenous series of other sites on the central site is considered to determine the best relevant site. Secondly, the data of all relevant sites are input into the improved dual-stage two-phase (DSTP) model, then the PM2.5 prediction result of each site is obtained. Finally, with the PM2.5 prediction result of each site, the attention-based layer predicts the PM2.5 concentration at the central site. The experimental results show that the proposed model is superior to most of the latest models.

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