Predictive maintenance of baggage handling conveyors using IoT

Gupta, Vishal; Mitra, Rony; Koenig, Frank; Kumar, Maneesh; Tiwari, Manoj Kumar

doi:10.1016/j.cie.2023.109033

Cited by 15 publications

(6 citation statements)

References 40 publications

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“…All the latter techniques have also reported promising performance in other industries (e.g., automation in productive processes [49] and predictive maintenance [11]).…”

Section: Discussion Of Recent ML Solutions In the Dairy Industrymentioning

confidence: 99%

“…Notably, the bulk of AI solutions nowadays is ML. The latter models exploit analytical and computational techniques to infer the behavior of the target feature given the available data and have been applied to a variety of industrial challenges related to predictive maintenance [11], and efficiency analysis [12], among other uses cases.…”

Section: Introduction To Industrial Informaticsmentioning

confidence: 99%

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Informatics and Dairy Industry Coalition: Artificial Intelligence Trends and Present Challenges

García-Méndez,

De Arriba-Pérez,

Del Carmen Somoza-López

2024

EEE Ind. Electron. Mag.

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Since 2015, she has worked as a researcher with the Information Technologies Group at the University of Vigo. She is collaborating with foreign research centers as part of her postdoctoral stage. Her research interests include Natural Language Processing techniques and Machine Learning algorithms. Francisco de Arriba-Pérez received a B.S. degree in telecommunication technologies engineering in 2013, an M.S. degree in telecommunication engineering in 2014, and a Ph.D. in 2019 from the University of Vigo, Spain. He is currently a researcher in the Information Technologies Group at the University of Vigo, Spain. His research includes the development of Machine Learning solutions for different domains like finance and health. María del Carmen Somoza-López received a Ph.D. from the University of Vigo, Spain, in 2015. She is currently an associate professor with the Applied Mathematics I Department at the University of Vigo. Her research focuses on advanced mathematical developments like new triangulation methods applied to topology, and Sage and Matlab applications.

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“…All the latter techniques have also reported promising performance in other industries (e.g., automation in productive processes [49] and predictive maintenance [11]).…”

Section: Discussion Of Recent ML Solutions In the Dairy Industrymentioning

confidence: 99%

Section: Introduction To Industrial Informaticsmentioning

confidence: 99%

Informatics and Dairy Industry Coalition: Artificial Intelligence Trends and Present Challenges

García-Méndez,

De Arriba-Pérez,

Del Carmen Somoza-López

2024

EEE Ind. Electron. Mag.

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“…Dengan kesibukan bandara ini, maka banyak penumpang yang hilir mudik di bandara tersebut. Maka, tingkat resiko kesalahan atau penanganan penumpang dan barang bawaan semakin tinggi juga, mengingat petugas pasti selalu konsentrasi dan menjalankan tugas sesuai SOP (Standard Operating Procedure) (Gupta, 2023;Liu, 2022;Noviantoro, 2022;Valarmathy, 2023;J. Wang, 2022).…”

Section: Pendahuluanunclassified

Analisis Penanganan Bagasi Outgoing oleh Petugas PT Gapura Angkasa pada Maskapai Penerbangan Citilink Indonesia di Bandar Udara Internasional Juanda Surabaya

Andromeda,

Rahimudin

2024

pjase

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Bandara Internasional Juanda Surabaya adalah bandara internasional yang terletak di Sidoarjo Jawa Timur. Penanganan Bagasi merupakan proses penanganan bagasi tercatat yang dilakukan pada saat sebelum keberangkatan (pre flight service) dan setelah keberangkatan (post flight service) pada suatu penerbangan. Latar belakang penelitian ini yaitu terjadi penyimpangan-penyimpangan saat penanganan bagasi outgoing seperti bagasi rusak saat sudah di bagian make up area, Yang artinya penumpang tidak mengetahui kalau bagasi miliknya mengalami kerusakan saat sudah tiba di Make Up Area. Tujuan dari penelitian ini untuk mengetahui bagaimana proses penanganan bagasi outgoing dan apa saja kendala yang dihadapi oleh petugas PT Gapura Angkasa Surabaya. Penelitian ini merupakan penelitian kualitatif dengan teknik pengumpulan data yaitu observasi, dokumentasi, dan wawancara. Sumber data dari penelitian ini yaitu Data primer wawancara, observasi, dokumentasi dan data sekunder berupa dokumen foto-foto pendukung. Teknik analisis data yang digunakan yaitu pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Hasil dari penelitian yang dilakukan oleh peneliti bahwa petugas sudah melaksanakan prosedur yang ada baik di bagian check-in counter maupun di make up area, namun masih ada beberapa kendala yang dihadapi oleh petugas salah satunya penumpang yang tidak memberitahu petugas bahwa bagasi tercatat miliknya sudah mengalami kerusakan. Upaya petugas untuk mencegah terjadinya hal serupa dengan bertanya dan memastikan kepada penumpang pada saat pelaporan bagasi.

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“…In this context, data extraction is a process that requires very sophisticated support. According to Gupta et al (2023), three modes of extracting relevant data are required: i) temporal side, ii) frequency side, and iii) frequency-time side. The extraction of lots (data) based on the temporal dimension is an action based on statistical parameters such as the root mean square, which can be used as a measure of the quality of an estimator, the kurtosis, which estimates whether distribution is sharp or spread, the mean, which estimates the center of gravity of lots, the variance which estimates the degree of dispersion of lots, and the asymmetry, which estimates the distribution of lots.…”

Section: Architecture Of Iiotmentioning

confidence: 99%

Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things

Kliestik,

Nica,

Durana

et al. 2023

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Research background: The article explores the integration of Artificial Intelligence (AI) in predictive maintenance (PM) within Industrial Internet of Things (IIoT) context. It addresses the increasing importance of leveraging advanced technologies to enhance maintenance practices in industrial settings. Purpose of the article: The primary objective of the article is to investigate and demonstrate the application of AI-driven PM in the IIoT. The authors aim to shed light on the potential benefits and implications of incorporating AI into maintenance strategies within industrial environments. Methods: The article employs a research methodology focused on the practical implementation of AI algorithms for PM. It involves the analysis of data from sensors and other sources within the IIoT ecosystem to present predictive models. The methods used in the study contribute to understanding the feasibility and effectiveness of AI-driven PM solutions. Findings & value added: The article presents significant findings regarding the impact of AI-driven PM on industrial operations. It discusses how the implementation of AI technologies contributes to increased efficiency. The added value of the research lies in providing insights into the transformative potential of AI within the IIoT for optimizing maintenance practices and improving overall industrial performance.

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Predictive maintenance of baggage handling conveyors using IoT

Cited by 15 publications

References 40 publications

Informatics and Dairy Industry Coalition: Artificial Intelligence Trends and Present Challenges

Informatics and Dairy Industry Coalition: Artificial Intelligence Trends and Present Challenges

Analisis Penanganan Bagasi Outgoing oleh Petugas PT Gapura Angkasa pada Maskapai Penerbangan Citilink Indonesia di Bandar Udara Internasional Juanda Surabaya

Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things

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