scholarly journals Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3740 ◽  
Author(s):  
Shucong Liu ◽  
Dezhi Zheng ◽  
Rui Li
Keyword(s):  
Oil And Gas ◽  
Short Term Memory ◽  
Compensation Method ◽  
Sensor Data ◽  
Measurement Unit ◽  
Main Method ◽  
Position Errors ◽  
Multi Sensor Data Fusion ◽  
Lstm Network ◽  
Energy Transportation

The accurate measurement of pipeline centerline coordinates is of great significance to the management of oil and gas pipelines and energy transportation security. The main method for pipeline centerline measurement is in-line inspection technology based on multi-sensor data fusion, which combines the inertial measurement unit (IMU), above-ground marker, and odometer. However, the observation of velocity is not accurate because the odometer often slips in the actual inspection, which greatly affects the accuracy of centerline measurement. In this paper, we propose a new compensation method for oil and gas pipeline centerline measurement based on a long short-term memory (LSTM) network during the occurrence of odometer slip. The field test results indicated that the mean of absolute position errors reduced from 8.75 to 2.02 m. The proposed method could effectively reduce the errors and improve the accuracy of pipeline centerline measurement during odometer slips.

scholarly journals Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 919 ◽  
Author(s):  
Hao Du ◽  
Wei Wang ◽  
Chaowen Xu ◽  
Ran Xiao ◽  
Changyin Sun
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

scholarly journals Incremental learning of LSTM framework for sensor fusion in attitude estimation

2021 ◽  
Vol 7 ◽  
pp. e662
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shashi Poddar ◽  
Ketan Kotecha
Keyword(s):  
Sensor Fusion ◽  
Incremental Learning ◽  
Short Term Memory ◽  
Inertial Sensors ◽  
Dynamic Environment ◽  
Attitude Estimation ◽  
Measurement Unit ◽  
Fusion Methods ◽  
Processing Module ◽  
Lstm Network

This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network. Gyroscope, accelerometer, and magnetometer are few widely used sensors in attitude estimation applications. Traditionally, multi-sensor fusion methods such as the Extended Kalman Filter and Complementary Filter are employed to fuse the measurements from these sensors. However, these methods exhibit limitations in accounting for the uncertainty, unpredictability, and dynamic nature of the motion in real-world situations. In this paper, the inertial sensors data are fed to the LSTM network which are then updated incrementally to incorporate the dynamic changes in motion occurring in the run time. The robustness and efficiency of the proposed framework is demonstrated on the dataset collected from a commercially available inertial measurement unit. The proposed framework offers a significant improvement in the results compared to the traditional method, even in the case of a highly dynamic environment. The LSTM framework-based attitude estimation approach can be deployed on a standard AI-supported processing module for real-time applications.

Decentralized Data Fusion Algorithm Using Factor Analysis Model

2013 ◽  
Vol 278-280 ◽  
pp. 1182-1186 ◽  
Author(s):  
Sayed Abulhasan Quadri ◽  
Othman Sidek
Keyword(s):  
Factor Analysis ◽  
Data Fusion ◽  
Sensor Data ◽  
Measurement Unit ◽  
Analysis Model ◽  
Fusion Algorithm ◽  
Factor Analysis Model ◽  
Multi Sensor Data Fusion ◽  
Decentralized Data Fusion ◽  
Improved Accuracy

Multi-sensor data fusion provides significant advantages over single source data to achieve an improved accuracy and better precision. Decentralized data fusion approach is one in which features are extracted and processed individually and finally fused to obtain global estimates. The paper presents decentralized data fusion algorithm using factor analysis model. Factor analysis is a statistical method used to study the effect and interdependence of various factors within a system. The proposed algorithm fuses accelerometer and gyroscope data in an inertial measurement unit (IMU). Simulations are carried out on Matlab platform to illustrate the algorithm.

scholarly journals Tool Remaining Useful Life Prediction Using Deep Transfer Reinforcement Learning Based on Long Short Term Memory Networks

2021 ◽  
Author(s):  
Jiachen Yao ◽  
Baochun Lu ◽  
Junli Zhang
Keyword(s):  
Reinforcement Learning ◽  
Tool Wear ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory ◽  
Lstm Network

Abstract Tool wear and faults will affect the quality of machined workpiece and damage the continuity of manufacturing. The accurate prediction of remaining useful life (RUL) is significant to guarantee processing quality and improve productivity of automatic system. At present, the most methods for tool RUL prediction are trained by history fault data. However, when researching on new types of tools or processing high value parts, fault datasets are difficult to acquired, which led to RUL prediction a challenge under limited fault data. To overcome shortcomings of above prediction methods, a deep transfer reinforcement learning (DTRL) network based on long short term memory (LSTM) network is presented in this paper. Local features are extracted from consecutive sensor data to track the tool states, and the trained network size can be dynamically adjusted by controlling time sequence length. Then in DTRL network, LSTM network is employed to construct the value function approximation for smoothly processing temporal information and mining long-term dependencies. On this basis, a novel strategies of Q-function update and transfer are presented to transfer the DRL network trained by historical fault data to a new tool for RUL prediction. Finally, tool wear experiments are performed to validate effectiveness of the DTRL model. The prediction result demonstrate that the proposed method has high accuracy and generalization for similar tools and cutting conditions.

scholarly journals A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4535
Author(s):  
Marion Mundt ◽  
William R. Johnson ◽  
Wolfgang Potthast ◽  
Bernd Markert ◽  
Ajmal Mian ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Ground Truth ◽  
Sensor Data ◽  
Measurement Unit ◽  
Joint Angles ◽  
Accurate Analysis ◽  
Inertial Measurement ◽  
Gait Kinematics ◽  
Kinematics And Kinetics

The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings—the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4370
Author(s):  
Yongze Jin ◽  
Guo Xie ◽  
Yankai Li ◽  
Xiaohui Zhang ◽  
Ning Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expectation Maximization ◽  
Monitoring Data ◽  
Sensor Data ◽  
Operating Environment ◽  
Mass Model ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Mass ◽  
Parameter Identifications

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

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