Prognostic Methods for Predictive Maintenance: A generalized Topology

被引:3
|
作者
Leohold, Simon [1 ]
Engbers, Hendrik [1 ]
Freitag, Michael [1 ,2 ]
机构
[1] Univ Bremen, BIBA Bremer Inst Prod & Logist GmbH, Bremen, Germany
[2] Univ Bremen, Fac Prod Engn, Bremen, Germany
来源
IFAC PAPERSONLINE | 2021年 / 54卷 / 01期
关键词
Prognostics; Predictive Maintenance; Condition Monitoring; Remaining Useful Lifetime Estimation; Machine Learning; SYSTEM;
D O I
10.1016/j.ifacol.2021.08.073
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Prognostic methods for predictive maintenance have been presented extensively in the literature. While this area's continuing effort positively affects individual predictive maintenance solutions' performance and capabilities, a method's setup remains a big hurdle as the solution space is becoming more complex. The critical settings of a prognostic method are the selection of suitable modeling techniques used for behavior- and condition-modeling, as well as a forecast model for failure prediction. This paper presents a generalized topology of a prognostic method to ease the design of maintenance systems and allow for quicker individual method design and modification. After a broad literature review, the topology and its base components are presented, and an overview of the different kinds of models related to predictive maintenance applications is given. Copyright (C) 2021 The Authors.
引用
收藏
页码:629 / 634
页数:6
相关论文
共 50 条
  • [21] Federated Learning for Predictive Maintenance: A Survey of Methods, Applications, and Challenges
    Purkayastha, Arnab A.
    Aggarwal, Shobhit
    2024 IEEE 67TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, MWSCAS 2024, 2024, : 238 - 242
  • [22] PrimaVera: Synergising Predictive Maintenance
    Ton, Bram
    Basten, Rob
    Bolte, John
    Braaksma, Jan
    Di Bucchianico, Alessandro
    van de Calseyde, Philippe
    Grooteman, Frank
    Heskes, Tom
    Jansen, Nils
    Teeuw, Wouter
    Tinga, Tiedo
    Stoelinga, Marielle
    APPLIED SCIENCES-BASEL, 2020, 10 (23): : 1 - 19
  • [23] Intelligent Predictive Maintenance System
    Marzec, Mateusz
    Morkisz, Pawel
    Wojdyla, Jakub
    Uhl, Tadeusz
    PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 1, 2018, 15 : 794 - 804
  • [24] Predictive maintenance with machine learning and
    Ersoz, Olcay Ozge
    Ifraz, Metin
    Tebrizcik, Semra
    Inal, Ali Firat
    Eskicioglu, Omer Can
    Aktepe, Adnan
    Turker, Ahmet Kursad
    Barisci, Necaattin
    Cetinyokus, Tahsin
    Ersoz, Suleyman
    JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2025,
  • [25] Prognostics and Health Management Methods for Reliability Prediction and Predictive Maintenance
    Zio, Enrico
    IEEE TRANSACTIONS ON RELIABILITY, 2024, 73 (01) : 41 - 41
  • [26] Context Awareness in Predictive Maintenance
    Schmidt B.
    Galar D.
    Wang L.
    Lecture Notes in Mechanical Engineering, 2016, : 197 - 211
  • [27] Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
    Cummins, Logan
    Sommers, Alexander
    Ramezani, Somayeh Bakhtiari
    Mittal, Sudip
    Jabour, Joseph
    Seale, Maria
    Rahimi, Shahram
    IEEE ACCESS, 2024, 12 : 57574 - 57602
  • [28] Deep-Learning-Enabled Predictive Maintenance in Industrial Internet of Things: Methods, Applications, and Challenges
    Wang, Hongchao
    Zhang, Weiting
    Yang, Dong
    Xiang, Yuhong
    IEEE SYSTEMS JOURNAL, 2023, 17 (02): : 2602 - 2615
  • [29] A comparative study of ensemble methods and multi-output classifiers for predictive maintenance of hydraulic systems
    Noura, Hassan N.
    Chu, Thomas
    Allal, Zaid
    Salman, Ola
    Chahine, Khaled
    RESULTS IN ENGINEERING, 2024, 24
  • [30] The benefits of predictive maintenance in manufacturing excellence: a case study to establish reliable methods for predicting failures
    Meddaoui, Anwar
    Hain, Mustapha
    Hachmoud, Adil
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2023, 128 (7-8) : 3685 - 3690