A Deep Learning Approach for Fusing Sensor Data from Screw Compressors

Alonso, Serafí­n; Pérez, Daniel; Morán, Antonio; Fuertes, Juan J.; Díaz, Ignacio; Domí­nguez, Manuel

doi:10.3390/s19132868

Cited by 11 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scholars also believe that multisensor data fusion is an interdisciplinary research field, using technologies from different fields [30], such as artificial intelligence and information theory. Henrik Boström thinks traditional multisensor data fusion focuses on online sensor data, while modern data fusion should consider other sources.…”

Section: Development and Definition Of Multisensor Data Fusionmentioning

confidence: 99%

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Chen

Liu

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

show abstract

Section: Development and Definition Of Multisensor Data Fusionmentioning

confidence: 99%

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Chen

Liu

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Recently, there have been detailed surveys on the combination of sensor fusion and big data analysis [9,10]. Some applications to actual problems have also been reported [11][12][13]. The popular approach for big data analysis is the use of machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…The details of the DL are not described, and the processing time of the observation system is not known; however, it may be useful to learn that they predicted the deterioration of sensors performance through their combination. Alonso S., et al [12] also adopted the same approach for observing a screw compressor in a chiller. They used five kinds of sensor data and a 1D convolutional neural network (CNN) for their analysis.…”

Section: Introductionmentioning

confidence: 99%

AI-Based Approach for Lawn Length Estimation in Robotic Lawn Mowers

Shiraishi¹,

Zhang²,

Motegi³

2021

Robotics Software Design and Engineering

View full text Add to dashboard Cite

This chapter describes a part of autonomous driving of work vehicles. This type of autonomous driving consists of work sensing and mobility control. Particularly, this chapter focuses on autonomous work sensing and mobility control of a commercial electric robotic lawn mower, and proposes an AI-based approach for work vehicles such as a robotic lawn mower. These two functions, work sensing and mobililty control, have a close correlation. In terms of efficiency, the traveling speed of a lawn mower, for example, should be reduced when the workload is high, and vice versa. At the same time, it is important to conserve the battery that is used for both work execution and mobility. Based on these requirements, this chapter is focused on developing an estimation system for estimating lawn grass lengths or ground conditions in a robotic lawn mower. To this end, two AI algorithms, namely, random forest (RF) and shallow neural network (SNN), are developed and evaluated on observation data obtained by a fusion of ten types of sensor data. The RF algorithm evaluated on data from the fusion of sensors achieved 92.3% correct estimation ratio in several experiments on real-world lawn grass areas, while the SNN achieved 95.0%. Furthermore, the accuracy of the SNN is 94.0% in experiments where sensor data are continuously obtained while the robotic lawn mower is operating. Presently, the proposed estimation system is being developed by integrating two motor control systems into a robotic lawn mower, one for lawn grass cutting and the other for the robot’s mobility.

show abstract

“…Recently, there are detailed surveys on the combination of sensor fusion and big data analysis [6,7]. Some applications to actual problems are also reported [8][9][10]. The popular approach for the big data analysis is the utilization of machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…However, it may be useful that they suggested the expectation of deterioration analysis of sensors by their combination. S. Alonso et al [9] also adopted the same approach for observing a screw compressor in a chiller. They used five kinds of sensor data and 1D convolutional neural network (CNN) for their analysis.…”

Section: Introductionmentioning

confidence: 99%

Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower

Zushida¹,

Zhang²,

Shimamura³

et al. 2021

IJMEA

View full text Add to dashboard Cite

This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.

show abstract

A Deep Learning Approach for Fusing Sensor Data from Screw Compressors

Cited by 11 publications

References 32 publications

A New View of Multisensor Data Fusion: Research on Generalized Fusion

A New View of Multisensor Data Fusion: Research on Generalized Fusion

AI-Based Approach for Lawn Length Estimation in Robotic Lawn Mowers

Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower

Contact Info

Product

Resources

About