scholarly journals Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation

2021 ◽  
Vol 11 (10) ◽  
pp. 4318
Author(s):  
Longhuan Cheng ◽  
Jiantao Lu ◽  
Shunming Li ◽  
Rui Ding ◽  
Kun Xu ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Original Data ◽  
Sensor Data ◽  
Fusion Method ◽  
Vibration Sensors ◽  
Fusion Data ◽  
Single Sensor ◽  
Spatio Temporal ◽  
Signal Processing Methods ◽  
Spatial Locations

Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.

Healing Seismic Data with Phase Corrections for Processing of Single-Sensor Data in the Desert Environment

2021 ◽  
Author(s):  
Andrey Bakulin ◽  
Ilya Silvestrov ◽  
Dmitry Neklyudov
Keyword(s):  
Speech Processing ◽  
Original Data ◽  
Sensor Data ◽  
Desert Environment ◽  
Time Frequency ◽  
Weak Reflection ◽  
Phase Corrections ◽  
Single Sensor ◽  
Efficient Processing ◽  
Novel Method

Abstract Acquiring data with single sensors or small arrays in a desert environment may lead to challenging data quality for subsequent processing. We present a new approach to effectively "heal" such data and allow efficient processing and imaging without requiring any additional acquisition. A novel method combines the power of seismic beamforming and time-frequency masking originating from speech processing. First, we create an enhanced version of the data with beamforming or local stacking. Beamforming effectively suppresses scattered noise and finds weak reflection signals, albeit sacrificing some higher frequencies. Next, we employ a seismic time-frequency masking procedure to fix the original data while using beamformed data as a guide. Time-frequency masking effectively fixes corrupt and broken phase of the original data. After such data-driven healing, prestack data can be effectively processed and imaged, while maintaining the higher frequencies lost during beamforming.

Vibration Correction With Kalman Filtering Based Data Fusion for Absolute Gravimeters

2019 ◽  
Author(s):  
Yi Wen ◽  
Kang Wu ◽  
Zhenxing Li ◽  
Jiamin Yao ◽  
Meiying Guo ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Kalman Filtering ◽  
Ground Vibration ◽  
Free Fall ◽  
Vibration Signal ◽  
Correction Method ◽  
Fusion Method ◽  
Absolute Gravimeter ◽  
Single Sensor ◽  
Better Than

Abstract Free-fall absolute gravimeters are important classical high precision absolute gravimeters in many branches of scientific research. But its performance is always troubled by the ground vibration. Vibration correction method is used to correct the result by detecting the ground vibration with sensors. A Kalman filter based fusion method is proposed to obtain more accurate ground vibration signal by fusing the outputs of the seismometer and the accelerometer. Experiment is conducted with the homemade T-1 absolute gravimeter, the standard deviation of the corrected results using seismometer data and fused data are 586.32 μGal (1 μGal = 10−8 m/s2) and 508.59 μGal respectively, much better than the uncorrected result’s 6548.96 μGal. The results prove the superiority of fused data over data measured from single sensor. It is believed that the application scene of the vibration correction will be broadened and the performance of the vibration correction will also be improved by using the proposed fusion method in the future.

scholarly journals Fault detection in an engine by fusing information from multivibration sensors

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771905 ◽  
Author(s):  
Ruili Zeng ◽  
Lingling Zhang ◽  
Jianmin Mei ◽  
Hong Shen ◽  
Huimin Zhao
Keyword(s):  
Fault Detection ◽  
Evidence Theory ◽  
Vibration Signal ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Detection Accuracy ◽  
Vector Data ◽  
Data Description ◽  
Vibration Sensors ◽  
Single Sensor

Fault detection based on the vibration signal of an engine is an effective non-disassembly method for engine diagnosis because a vibration signal includes a lot of information about the condition of the engine. To obtain multi-information for this article, three vibration sensors were placed at different test points to collect vibration information about the engine operating process. A method combining support vector data description and Dempster–Shafer evidence theory was developed for engine fault detection, where support vector data description is used to recognize the data from a single sensor and Dempster–Shafer evidence theory is used to classify the information from the three vibration sensors in detail. The experimental results show that the fault detection accuracy using three sensors is higher than using a single sensor. The multi-complementary sensor information can be adopted in the proposed method, which will increase the reliability of fault detection and reduce uncertainty in the recognition of a fault.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

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.

Dynamic Model Development of Wind Turbine Drivetrains by Using Sensor Measurements

2021 ◽  
Author(s):  
Diederik van Binsbergen ◽  
Amir R. Nejad ◽  
Jan Helsen
Keyword(s):  
Wind Turbine ◽  
Angular Displacement ◽  
Model Development ◽  
Rotational Frequency ◽  
Sensor Data ◽  
Gear Mesh ◽  
Modal Properties ◽  
Vibration Sensors ◽  
Displacement Sensors ◽  
Simulation Based

Abstract This paper aims to analyze the feasibility of establishing a dynamic drivetrain model from condition monitoring measurements. In this study SCADA data and further sensor data is analyzed from a 1.5MW wind turbine, provided by the National Renewable Energy Laboratory. A multibody model of the drivetrain is made and simulation based sensors are placed on bearings to look at the possibility to obtain geometrical and modal properties from simulation based vibration sensors. Results show that the axial proxy sensor did not provide any usable system information due to its application purpose. SCADA data did not meet the Nyquist frequency and cannot be used to determine geometrical or modal properties. Strain gauges on the shaft can provide the shaft rotational frequency, while torque and angular displacement sensors can provide the torsional eigenfrequency of the system. Simulation based vibration sensors are able to capture gear mesh frequencies, harmonics, sideband frequencies and shaft rotational frequencies.

scholarly journals Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

2018 ◽  
Vol 14 (04) ◽  
pp. 4
Author(s):  
Xuemei Yao ◽  
Shaobo Li ◽  
Yong Yao ◽  
Xiaoting Xie
Keyword(s):  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Monitoring And Diagnosis ◽  
Multi Sensor Data Fusion ◽  
Basic Probability ◽  
Optimal Fusion ◽  
Single Sensor ◽  
Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

scholarly journals Multi-source data fusion method for structural safety assessment of water diversion structures

2021 ◽  
Author(s):  
Sherong Zhang ◽  
Ting Liu ◽  
Chao Wang
Keyword(s):  
Data Fusion ◽  
Safety Assessment ◽  
Evidence Theory ◽  
Weighted Average ◽  
Water Diversion ◽  
Sensor Data ◽  
Structural Safety ◽  
Fusion Method ◽  
Bhattacharyya Distance ◽  
Source Data

Abstract Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%.

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