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Cited by 401Fault Detection and Control SystemsDigital Transformation in IndustryTechnology and Data Analysis

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

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Syafrudin, Muhammad, Alfian, Ganjar, Fitriyani, Norma Latif, Rhee, Jongtae·2018·Sensors
DOI 10.3390/s18092946PMC PMC6164307
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Abstract

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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  • Two online data cleaning techniques use ML solutions (DBSCAN and decision trees and random forest classification [5]).

    “Two online data cleaning techniques use ML solutions (DBSCAN -an unsupervised learning method utilized in ML algorithms [40] and decision trees and random forest classification [5]).”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [40]

    The Methodology (§3.3; page unavailable) describes Random Forest classification built from decision trees, alongside DBSCAN-based removal of outliers. It does not describe decision trees and Random Forest as two online data-cleaning techniques: DBSCAN performs the cleaning, while Random Forest performs prediction. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Nine primary studies [4,8,10,16,23,24,31,40,49] cover edge-Cloud orchestration for data processing (not necessarily data quality management).

    “Nine primary studies [4,8,10,16,23,24,31,40,49] cover edge-Cloud orchestration for data processing (not necessarily data quality management).”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [40]

    The System Design and System Implementation sections describe a primary study in which IoT sensor devices send data to a cloud server for real-time processing. That supports its inclusion as a study of sensor-to-cloud data processing, but the passages do not describe orchestration of processing between the edge and cloud. This paper also cannot establish the citing sentence’s count of nine studies. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Four of these studies [23,31,40,49] provide a data quality management technique (data cleaning and repair) in the architecture layers from L1 to L7.

    “Four of these studies [23,31,40,49] provide a data quality management technique (data cleaning and repair) in the architecture layers from L1 to L7.”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [40]

    The Methodology (sections 3.1 and 3.3; page unavailable) describes removing inconsistent, missing-value, and outlier sensor data, and places outlier detection within the processing system. This supports data cleaning, but our copy does not describe data repair or identify its architecture using layers L1 to L7. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • They are all online techniques and implement data monitoring and cleaning/repair across the edge-Cloud continuum.

    “They are all online techniques and implement data monitoring and cleaning/repair across the edge-Cloud continuum.”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [40]

    Section 3.1 (page not provided) describes real-time sensor monitoring and outlier filtering; Section 3.3 says the detected outliers were removed. The paper places processing on a cloud server, but does not describe data repair or processing across an edge–cloud continuum. It also describes one system, not all the techniques referred to in the claim. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • We identified five online cleaning techniques [5,8,29,40,48,49].

    “We identified five online cleaning techniques [5,8,29,40,48,49].”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [40]

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Cited by

Sorted by citations

Authors
Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J.
Affiliations
Dongguk University (KR)
Corresponding author
  • Alfian, Ganjar
  • Rhee, Jongtae
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2018-09-04
Volume
18
Issue
9
Pages
2946
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s18092946
PMC
PMC6164307
OpenAlex
W2890098390
Cited by
401
References
1
Citations by year
2026: 38 · 2025: 58 · 2024: 55 · 2023: 58 · 2022: 73 · 2021: 61 · 2020: 33 · 2019: 23 · 2018: 2
FWCI
18.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Ministry of Trade, Industry and Energy
  • Korea Evaluation Institute of Industrial Technology
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Computer Science › Information Systems › Technology and Data Analysis
Keywords
IoT sensors, real-time monitoring, automotive manufacturing, big data processing, Apache Kafka, Apache Storm, MongoDB, random forest classification, hybrid forecasting model, sensor data, assembly line

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