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Zhu H, Rank S, Schmidt T (2021). Automated, AI-based Inspection of Drive Wheels on Overhead Hoist Transport Vehicles. Logistics Journal : Proceedings, Vol. 2021. (urn:nbn:de:0009-14-54509)

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%0 Journal Article
%T Automated, AI-based Inspection of Drive Wheels on Overhead Hoist Transport Vehicles
%A Zhu, Hailong
%A Rank, Sebastian
%A Schmidt, Thorsten
%J Logistics Journal : Proceedings
%D 2021
%V 2021
%N 17
%@ 2192-9084
%F zhu2021
%X Overhead hoist transport systems are used to transport wafers in 300 mm semiconductor factories. These rail-based systems usually consist of hundreds of vehicles to ensure fast and safe transport of wafers between tools. Faults of individual vehicles can cause damage to the transferred goods and production downtimes. To minimize the risk of failure, extensive preventive maintenance of the vehicle's heavily stressed components is required. This includes the chassis and drive wheels. This article describes an automatic inspection approach that can drastically accelerate the inspection process. We have developed an automatic inspection approach for the drive wheels that can drastically speed up the inspection process. From the data obtained, we trained a deep convolutional autoencoder network to predict the growth of fractures on the surface of the wheels. With the help of our inspection approach, it is possible to carry out conditionbased predictive maintenance of the OHT vehicles. This approach promises cost savings compared to routine or time-based strategies for preventive maintenance, as we can carry out maintenance tasks only when they are justified.
%L 620
%K Fehlererkennung
%K OHT
%K Verschleißmodell
%K Zustandsüberwachung
%K autoencoder
%K condition monitoring
%K faults detection
%K wear out model
%R 10.2195/lj_Proc_zhu_en_202112_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-54509
%U http://dx.doi.org/10.2195/lj_Proc_zhu_en_202112_01
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Bibtex

@Article{zhu2021,
  author = 	"Zhu, Hailong
		and Rank, Sebastian
		and Schmidt, Thorsten",
  title = 	"Automated, AI-based Inspection of Drive Wheels on Overhead Hoist Transport Vehicles",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2021",
  volume = 	"2021",
  number = 	"17",
  keywords = 	"Fehlererkennung; OHT; Verschlei{\ss}modell; Zustands{\"u}berwachung; autoencoder; condition monitoring; faults detection; wear out model",
  abstract = 	"Overhead hoist transport systems are used to transport wafers in 300 mm semiconductor factories. These rail-based systems usually consist of hundreds of vehicles to ensure fast and safe transport of wafers between tools. Faults of individual vehicles can cause damage to the transferred goods and production downtimes. To minimize the risk of failure, extensive preventive maintenance of the vehicle's heavily stressed components is required. This includes the chassis and drive wheels. This article describes an automatic inspection approach that can drastically accelerate the inspection process. We have developed an automatic inspection approach for the drive wheels that can drastically speed up the inspection process. From the data obtained, we trained a deep convolutional autoencoder network to predict the growth of fractures on the surface of the wheels. With the help of our inspection approach, it is possible to carry out conditionbased predictive maintenance of the OHT vehicles. This approach promises cost savings compared to routine or time-based strategies for preventive maintenance, as we can carry out maintenance tasks only when they are justified.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_zhu_en_202112_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-54509"
}
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TY  - JOUR
AU  - Zhu, Hailong
AU  - Rank, Sebastian
AU  - Schmidt, Thorsten
PY  - 2021
DA  - 2021//
TI  - Automated, AI-based Inspection of Drive Wheels on Overhead Hoist Transport Vehicles
JO  - Logistics Journal : Proceedings
VL  - 2021
IS  - 17
KW  - Fehlererkennung
KW  - OHT
KW  - Verschleißmodell
KW  - Zustandsüberwachung
KW  - autoencoder
KW  - condition monitoring
KW  - faults detection
KW  - wear out model
AB  - Overhead hoist transport systems are used to transport wafers in 300 mm semiconductor factories. These rail-based systems usually consist of hundreds of vehicles to ensure fast and safe transport of wafers between tools. Faults of individual vehicles can cause damage to the transferred goods and production downtimes. To minimize the risk of failure, extensive preventive maintenance of the vehicle's heavily stressed components is required. This includes the chassis and drive wheels. This article describes an automatic inspection approach that can drastically accelerate the inspection process. We have developed an automatic inspection approach for the drive wheels that can drastically speed up the inspection process. From the data obtained, we trained a deep convolutional autoencoder network to predict the growth of fractures on the surface of the wheels. With the help of our inspection approach, it is possible to carry out conditionbased predictive maintenance of the OHT vehicles. This approach promises cost savings compared to routine or time-based strategies for preventive maintenance, as we can carry out maintenance tasks only when they are justified.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-54509
DO  - 10.2195/lj_Proc_zhu_en_202112_01
ID  - zhu2021
ER  -
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Wordbib

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<b:Comments>Overhead hoist transport systems are used to transport wafers in 300 mm semiconductor factories. These rail-based systems usually consist of hundreds of vehicles to ensure fast and safe transport of wafers between tools. Faults of individual vehicles can cause damage to the transferred goods and production downtimes. To minimize the risk of failure, extensive preventive maintenance of the vehicle&apos;s heavily stressed components is required. This includes the chassis and drive wheels. This article describes an automatic inspection approach that can drastically accelerate the inspection process. We have developed an automatic inspection approach for the drive wheels that can drastically speed up the inspection process. From the data obtained, we trained a deep convolutional autoencoder network to predict the growth of fractures on the surface of the wheels. With the help of our inspection approach, it is possible to carry out conditionbased predictive maintenance of the OHT vehicles. This approach promises cost savings compared to routine or time-based strategies for preventive maintenance, as we can carry out maintenance tasks only when they are justified.</b:Comments>
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ISI

PT Journal
AU Zhu, H
   Rank, S
   Schmidt, T
TI Automated, AI-based Inspection of Drive Wheels on Overhead Hoist Transport Vehicles
SO Logistics Journal : Proceedings
PY 2021
VL 2021
IS 17
DI 10.2195/lj_Proc_zhu_en_202112_01
DE Fehlererkennung; OHT; Verschleißmodell; Zustandsüberwachung; autoencoder; condition monitoring; faults detection; wear out model
AB Overhead hoist transport systems are used to transport wafers in 300 mm semiconductor factories. These rail-based systems usually consist of hundreds of vehicles to ensure fast and safe transport of wafers between tools. Faults of individual vehicles can cause damage to the transferred goods and production downtimes. To minimize the risk of failure, extensive preventive maintenance of the vehicle's heavily stressed components is required. This includes the chassis and drive wheels. This article describes an automatic inspection approach that can drastically accelerate the inspection process. We have developed an automatic inspection approach for the drive wheels that can drastically speed up the inspection process. From the data obtained, we trained a deep convolutional autoencoder network to predict the growth of fractures on the surface of the wheels. With the help of our inspection approach, it is possible to carry out conditionbased predictive maintenance of the OHT vehicles. This approach promises cost savings compared to routine or time-based strategies for preventive maintenance, as we can carry out maintenance tasks only when they are justified.
ER
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Mods

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  <abstract>Overhead hoist transport systems are used to transport wafers in 300 mm semiconductor factories. These rail-based systems usually consist of hundreds of vehicles to ensure fast and safe transport of wafers between tools. Faults of individual vehicles can cause damage to the transferred goods and production downtimes. To minimize the risk of failure, extensive preventive maintenance of the vehicle's heavily stressed components is required. This includes the chassis and drive wheels. This article describes an automatic inspection approach that can drastically accelerate the inspection process. We have developed an automatic inspection approach for the drive wheels that can drastically speed up the inspection process. From the data obtained, we trained a deep convolutional autoencoder network to predict the growth of fractures on the surface of the wheels. With the help of our inspection approach, it is possible to carry out conditionbased predictive maintenance of the OHT vehicles. This approach promises cost savings compared to routine or time-based strategies for preventive maintenance, as we can carry out maintenance tasks only when they are justified.</abstract>
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    <topic>Fehlererkennung</topic>
    <topic>OHT</topic>
    <topic>Verschleißmodell</topic>
    <topic>Zustandsüberwachung</topic>
    <topic>autoencoder</topic>
    <topic>condition monitoring</topic>
    <topic>faults detection</topic>
    <topic>wear out model</topic>
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2021, Iss. 17
Title

Automated, AI-based Inspection of Drive Wheels on Overhead Hoist Transport Vehicles (eng)
Author Hailong Zhu, Sebastian Rank, Thorsten Schmidt
Language eng
Abstract Overhead hoist transport systems are used to transport wafers in 300 mm semiconductor factories. These rail-based systems usually consist of hundreds of vehicles to ensure fast and safe transport of wafers between tools. Faults of individual vehicles can cause damage to the transferred goods and production downtimes. To minimize the risk of failure, extensive preventive maintenance of the vehicle's heavily stressed components is required. This includes the chassis and drive wheels. This article describes an automatic inspection approach that can drastically accelerate the inspection process. We have developed an automatic inspection approach for the drive wheels that can drastically speed up the inspection process. From the data obtained, we trained a deep convolutional autoencoder network to predict the growth of fractures on the surface of the wheels. With the help of our inspection approach, it is possible to carry out conditionbased predictive maintenance of the OHT vehicles. This approach promises cost savings compared to routine or time-based strategies for preventive maintenance, as we can carry out maintenance tasks only when they are justified. Overhead Hoist Transportsysteme werden zum Transport von Wafern in 300-mm-Halbleiterfabriken eingesetzt. Diese schienenbasierten Systeme bestehen normalerweise aus Hunderten von Fahrzeugen, um einen schnellen und sicheren Transport von Wafern zwischen Werkzeugen zu gewährleisten. Fehler einzelner Fahrzeuge können zu Schäden an den übergebenen Waren und Produktionsausfällen führen. Um das Ausfallrisiko zu minimieren, ist eine umfassende vorbeugende Wartung der stark beanspruchten Fahrzeugkomponenten erforderlich. Wir haben einen automatischen Inspektionsansatz für die Antriebsräder entwickelt, der den Inspektionsprozess drastisch beschleunigen kann. Aus den erhaltenen Daten haben wir ein deep convolutional-Autoencoder-Netzwerk trainiert, um das Wachstum von Frakturen auf der Oberfläche der Räder vorherzusagen. Mit Hilfe unser Inspektionsansatz kann eine zustandsbasierte prädiktive Wartung der OHT-Fahrzeuge durchgeführt werden. Dieser Ansatz verspricht Kosteneinsparungen gegenüber routinemäßigen oder zeitbasierten Strategien zur präventiven Wartung, da Aufgaben nur dann ausgeführt werden, wenn dies gerechtfertigt ist.
Subject Fehlererkennung, OHT, Verschleißmodell, Zustandsüberwachung, autoencoder, condition monitoring, faults detection, wear out model
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-54509
DOI https://doi.org/10.2195/lj_Proc_zhu_en_202112_01