A Decision-Level Data Fusion Approach to Surface Roughness Prediction

2019 ◽  
Author(s):  
Yupeng Wei ◽  
Dazhong Wu ◽  
Janis Terpenny
Keyword(s):  
Surface Roughness ◽  
Data Fusion ◽  
Process Monitoring ◽  
Predictive Models ◽  
Sensor Data ◽  
Fused Deposition ◽  
Decision Level ◽  
Level Data ◽  
Fusion Approach

Abstract To improve the quality of additively manufactured parts, it is crucial to develop real-time process monitoring systems and data-driven predictive models. While various sensor- and image-based process monitoring methods have been developed to improve the quality of additively manufactured parts, very limited research has been conducted to predict surface roughness. To fill this gap, this paper presents a decision-level data fusion approach to predicting surface roughness in the fused deposition modeling (FDM) process. The predictive models are trained by the random forests method using multiple sensor signals. A decision-level data fusion method is introduced to integrate sensor data sources. Experimental results have shown that the decision-level data fusion approach can predict surface roughness in FDM with high accuracy.

Fault Diagnosis of Hydraulic Pump Based on Multi-Sensor Data Fusion

2010 ◽  
Vol 33 ◽  
pp. 539-543
Author(s):  
Ying Liu ◽  
Dun Wen Zuo ◽  
Yao Hua Wang ◽  
Jun Han ◽  
Xiao Qiang Yang
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Decision Level ◽  
Multi Sensor Data Fusion ◽  
Level Data ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Novel Method

Due to hydraulic pump’s multiple fault parameters, imprecision of fault diagnosis and bad fuzzy properties, a novel method of data preprocess to remove the noise disturbance and extract the characteristics of parameters, in which the order analysis is applied, is put forward. Then the hydraulic pump’s fault is diagnosed with decision-level data fusion of multiple sensors. The practical results showed that the fault diagnosis method based on D-S proof theory and decision-level data fusion could promote the accuracy and efficiency of hydraulic pump’s fault diagnosis.

Towards the development of a smart fused filament fabrication system using multi-sensor data fusion for in-process monitoring

2020 ◽  
Vol 26 (7) ◽  
pp. 1249-1261 ◽  
Author(s):  
Michele Moretti ◽  
Federico Bianchi ◽  
Nicola Senin
Keyword(s):  
Data Fusion ◽  
Process Monitoring ◽  
Sensor Data ◽  
Fused Filament Fabrication ◽  
Content Type ◽  
Heterogeneous Sensors ◽  
Sensor Data Fusion ◽  
Part Quality ◽  
Multi Sensor Data Fusion ◽  
Smart Machine

Purpose This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built. Design/methodology/approach Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects. Findings Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts. Research limitations/implications Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers. Practical implications Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts. Originality/value While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

scholarly journals Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 880 ◽  
Author(s):  
Aimilia Papagiannaki ◽  
Evangelia Zacharaki ◽  
Gerasimos Kalouris ◽  
Spyridon Kalogiannis ◽  
Konstantinos Deltouzos ◽  
...  
Keyword(s):  
Older People ◽  
Predictive Models ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Physiological Monitoring ◽  
Ageing Population ◽  
Post Processing ◽  
Valuable Alternative ◽  
Learning Architectures

The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.

A geophysical and spectrometric sensor data fusion approach for homogeneous within-field zone delineation

2019 ◽  
Author(s):  
G. Buttafuoco ◽  
R. Quarto ◽  
F. Quarto ◽  
M. Conforti ◽  
A. Venezia ◽  
...  
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Field Zone ◽  
Sensor Data Fusion ◽  
Fusion Approach
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