Search for Author, Title, Keyword
RESEARCH PAPER
Monitoring and maintenance of a gantry based on a wireless system for measurement and analysis of the vibration level
 
More details
Hide details
1
Institute of Informatics, Silesian University of Technology ul. Akademicka 16, 44-100 Gliwice, Poland
 
 
Publication date: 2019-06-30
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(2):341-350
 
KEYWORDS
ABSTRACT
The paper describes a system for monitoring and diagnosing a gantry. The main goal of the system is to acquire, visualize and monitor vibration levels of the gantry crucial elements. The system is also equipped with a computing and analytical part which enables predictive maintenance related to the vibration level assessment. The system architecture can be used in other applications too, i.e. those which require a wireless network of vibration sensors to carry out diagnostic tasks.
 
REFERENCES (29)
1.
Antoni J. The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing 2006;20(2): 282-307, https://doi.org/10.1016/j.ymss....
 
2.
Bartelmus W, Zimroz R. Vibration condition monitoring of planetary gearbox under varying external load. Mechanical Systems and Signal Processing 2008; 23: 246-257, https://doi.org/10.1016/j.ymss....
 
3.
Bartelmus W, Zimroz R. A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mechanical Systems and Signal Processing 2009; 23(5):1528-1534, https://doi.org/10.1016/j.ymss....
 
4.
Breiman L. Random forests. Machine Learning 2001; 45(1): 5-32, https://doi.org/10.1023/A:1010....
 
5.
Chen B, Yin P, Gao Y, Peng F. Use of the correlated EEMD and time-spectral kurtosis for bearing defect detection under large speed variation. Mechanism and Machine Theory 2018; 129: 162-174, https://doi.org/10.1016/j.mech....
 
6.
Du W, Li A, Ye P, Liu C. Fault diagnosis of plunger pump in truck crane based on relevance vector machine with particle swarm optimization algorithm. Shock and Vibration 2013; 20(4): 781-792, https://doi.org/10.1155/2013/6....
 
7.
Elforjani M, Bechhoefer E. Analysis of extremely modulated faulty wind turbine data using spectral kurtosis and signal intensity estimator.Renewable Energy 2018; 127: 258-268.
 
8.
Głowacz A, Głowacz W. Vibration-Based Fault Diagnosis of Commutator Motor. Shock and Vibration 2018; art. id 7460419, https://doi.org/10.1155/2018/7....
 
9.
Głowacz A, Głowacz Z. Diagnosis of the three-phase induction motor using thermal imaging. Infrared Physics & Technology 2017; 81: 7-16, https://doi.org/10.1016/j.infr....
 
10.
Henao H, Capolino G, Manes F. Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE industrial electronics magazine 2014; 8(2): 31-42, https://doi.org/10.1109/MIE.20....
 
11.
ISA95 – Enterpise-Control System Integration Standard (https://www.isa.org/isa95/).
 
12.
Jingwei G, Niaoqin H, Lehua J, Jianyi F. A New Condition Monitoring and Fault Diagnosis Method of Engine Based on Spectrometric Oil Analysis. Advances in Intelligent and Soft Computing 2011, 110:117-124, https://doi.org/10.1007/978-3-....
 
13.
Korbicz J, Kościelny M (eds.).Modeling, Diagnostics and Process Control. Implementation in the DiaSter System. Springer-Verlag Berlin, Heidelberg 2011, https://doi.org/10.1007/978-3-....
 
14.
Korbicz J, Kościelny M, Kowalczuk Z, Cholewa W (eds.). Fault Diagnosis. Models, Artificial Intelligence, Appications. Springer-Verlag Berlin Heidelberg 2004, https://doi.org/10.1007/978-3-....
 
15.
Li Y, Liang X, Xu M, Huang W. Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform. Mechanical Systems and Signal Processing 2017, 86(Part A): 204-223.
 
16.
Macián V, Tormos B, Olmeda P, Montoro L. Analytical approach to wear rate determination for internal combustion engine condition monitoring based on oil analysis. Tribology International 2003; 36: 771–776, https://doi.org/10.1016/S0301-....
 
17.
Mazurkiewicz, D. Computer-aided maintenance and reliability management systems for conveyor belts. Eksploatacja i Niezawodność-Maintenance and Reliability 2014; 16(3):377-382.
 
18.
Mobley R. An Introduction to Predictive Maintenance. Second Edition. Butterworth-Heinemann 2013.
 
19.
Peng Z, Chu F. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 2004; 18(2): 199-221, https://doi.org/10.1016/S0888-....
 
20.
Przystałka P, Moczulski W. Methodology of neural modelling in fault detection with the use of chaos engineering. Engineering Applications of Artificial Intelligence 2015; 41: 25-40, https://doi.org/10.1016/j.enga....
 
21.
R Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2014, http://www.R-project.org.
 
22.
Samuel P, Pines D. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration 2005;282(1-2): 475-508, https://doi.org/10.1016/j.jsv.....
 
23.
Silva S, Costa P, Gouvea M, Lacerda A, Alves F, Leite D. High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electric Power Systems Research 2018, 154: 474-483, https://doi.org/10.1016/j.epsr....
 
24.
Therneau T, Atkinson B. Package: rpart (http://cran.r-project.org/web/...).
 
25.
Wachla D,Moczulski W. Identification of dynamic diagnostic models with the use of methodology of knowledge discovery in databases. Engineering Applications of Artificial Intelligence 2007; 20(5): 699-707, https://doi.org/10.1016/j.enga....
 
26.
Wu S, Zuo M. Linear and Nonlinear Preventive Maintenance Models. IEEE Transactions on Reliability 2010; 59(1): 242-249, https://doi.org/10.1109/TR.201....
 
27.
Yan R, Gaob R, Chen X. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 2014; 96(A): 1-15.
 
28.
Ye Z, Wu B, Zargari N.: Online mechanical fault diagnostics of induction motor by wavelet artificial neural network using stator current. IECON Proceedings 2000; 2: 1183–1188.
 
29.
Zio E. Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability 2016; 65(4): 1769-1782, https://doi.org/10.1109/TR.201....
 
 
CITATIONS (11):
1.
Lightweight IT Operation and Maintenance Integrated Monitoring Method for APP System
Xuyong Wang, Rui Chen
Journal of Physics: Conference Series
 
2.
Introduction to Special Issue on Symmetry in Mechanical Engineering
Grzegorz Krolczyk, Stanislaw Legutko, Zhixiong Li, Daviu Antonino
Symmetry
 
3.
Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles
Krzysztof Lalik, Filip Wątorek
Energies
 
4.
Maintenance 4.0 Technologies for Sustainable Manufacturing - an Overview
- Jasiulewicz, Arkadiusz Gola
IFAC-PapersOnLine
 
5.
Machine Vibration Monitoring for Diagnostics through Hypothesis Testing
Alessandro Daga, Luigi Garibaldi
Information
 
6.
Detection of Deterioration of Three-phase Induction Motor using Vibration Signals
Adam Glowacz, Witold Glowacz, Jarosław Kozik, Krzysztof Piech, Miroslav Gutten, Wahyu Caesarendra, Hui Liu, Frantisek Brumercik, Muhammad Irfan, Khan Faizal
Measurement Science Review
 
7.
Fatigue lifetime correction of structural joints of opencast mining machinery
Paweł Grabowski, Artur Jankowiak, Witold Marowski
Eksploatacja i Niezawodnosc - Maintenance and Reliability
 
8.
Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox
Yaxin Li, Kesheng Wang
Eksploatacja i Niezawodność – Maintenance and Reliability
 
9.
Fault diagnosis of multistage centrifugal pump unit using non-local means-based vibration signal denoising
Te Han, Dongxiang Jiang
Eksploatacja i Niezawodność – Maintenance and Reliability
 
10.
Energy consumption and energy efficiency improvement of overhead crane’s mechanisms
Andrzej Kosucki, Łukasz Stawiński, Piotr Malenta, Jakub Zaczyński, Justyna Skowrońska
Eksploatacja i Niezawodność – Maintenance and Reliability
 
11.
Derin öğrenme teknikleri kullanılarak üretim sistemlerinde KPI tabanlı performans tahminleme
Taha AKKURT, İnci SARIÇİÇEK
Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi
 
eISSN:2956-3860
ISSN:1507-2711
Journals System - logo
Scroll to top