Predictive Maintenance of an Electro-Injector through Machine Learning Algorithms

被引:0
|
作者
Mangini, Agostino M. [1 ]
Rinaldi, A. [1 ]
Roccotelli, M. [1 ]
Fanti, M. P. [1 ]
机构
[1] Polytech Univ Bari, Dept Elect & Informat Engn, Bari, Italy
关键词
DIAGNOSIS;
D O I
10.1109/SMC52423.2021.9659261
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This work aims to define a system for measuring the "lift" of the anchor (the final part of the shutter) present inside the injector based on the use of Machine Learning classification algorithms. The measurement method determined is a non-invasive method, which guarantees that the internal organs of the injection system are not damaged to carry out the measurement and that it can be performed after welding the injector to prevent the "lift" from changing later. This measurement method provides for the classification of the currents circulating inside the solenoid, each of which can be associated with a specific value of the "injector lift. This approach is part of predictive maintenance techniques, a type of maintenance that tries to predict incorrect behavior of the system avoiding that critical operating conditions are reached. Finally, an analysis of the possible techniques for measuring the injector "lift" is carried out through the use of Machine Learning algorithms.
引用
收藏
页码:2334 / 2339
页数:6
相关论文
共 50 条
  • [31] Comparison of Machine Learning Models for Predictive Maintenance Applications
    Lazzaro, Alessia
    D'Addona, Doriana Marilena
    Merenda, Massimo
    ADVANCES IN SYSTEM-INTEGRATED INTELLIGENCE, SYSINT 2022, 2023, 546 : 657 - 666
  • [32] Rolling Stocks: A Machine Learning Predictive Maintenance Architecture
    Nappi, Roberto
    Striano, Valerio
    Cutrera, Gianluca
    Vigliotti, Antonio
    Franze, Giuseppe
    DEPENDABLE COMPUTING, EDCC 2020 WORKSHOPS, 2020, 1279 : 68 - 77
  • [33] An analysis of machine learning algorithms in rotating machines maintenance
    Roque, Alexandre S.
    Krebs, Vinicius W.
    Figueiro, Iuri Castro
    Jazdi, Nasser
    IFAC PAPERSONLINE, 2022, 55 (02): : 252 - 257
  • [34] Motor Classification with Machine Learning Methods for Predictive Maintenance
    Kammerer, Christoph
    Gaust, Michael
    Kuestner, Micha
    Starke, Pascal
    Radtke, Roman
    Jesser, Alexander
    IFAC PAPERSONLINE, 2021, 54 (01): : 1059 - 1064
  • [35] Proposal of a Machine Learning Predictive Maintenance Solution Architecture
    Patrascu, Aurelia
    Bucur, Cristian
    Tanasescu, Ana
    Toader, Florentina Alina
    INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2024, 19 (03) : 1 - 19
  • [36] Optimizing Predictive Maintenance With Machine Learning for Reliability Improvement
    Ren, Yali
    ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING, 2021, 7 (03):
  • [37] Digital optics and machine learning algorithms for aircraft maintenance
    Merola, Salvatore
    Guida, Michele
    Marulo, Francesco
    AEROSPACE SCIENCE AND ENGINEERING, IV AEROSPACE PHD-DAYS 2024, 2024, 42 : 18 - 21
  • [38] Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning
    Hsu, Jyh-Yih
    Wang, Yi-Fu
    Lin, Kuan-Cheng
    Chen, Mu-Yen
    Hsu, Jenneille Hwai-Yuan
    IEEE ACCESS, 2020, 8 : 23427 - 23439
  • [39] Prediction of departure flight delays through the use of predictive tools based on machine learning/deep learning algorithms
    Anguita, J. G. Muros
    Olariaga, O. Diaz
    AERONAUTICAL JOURNAL, 2023, 18 (01):
  • [40] Machine learning takes maintenance to a new level Strategy a compliment to predictive maintenance
    Cleveland, Fritz
    Plant Engineering, 2019,