Monitoring and maintenance of a gantry based on a wireless system for measurement and analysis of the vibration level

Sikora, Marek; Szczyrba, Krzysztof; Wróbel, Łukasz; Michalak, Marcin

doi:10.17531/ein.2019.2.19

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…The presented solutions, methods, and approaches can be improved and used in the future. Moreover, mechanical engineering is essential for fault diagnosis of machines [10][11][12][13][14][15][16][17][18][19][20][21][22] and the analysis of temperature [23][24][25]. The mechanical properties of materials are also investigated in the literature [26][27][28].…”

Section: The Contentmentioning

confidence: 99%

Introduction to Special Issue on Symmetry in Mechanical Engineering

Królczyk

Legutko

et al. 2020

Symmetry

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Section: The Contentmentioning

confidence: 99%

Introduction to Special Issue on Symmetry in Mechanical Engineering

Królczyk

Legutko

et al. 2020

Symmetry

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show abstract

“…Usually, the ratio of Peak and RMS defines a third level indicator called Crest Factor, which is very reliable in the presence of significant impulsiveness. The level indicators are very commonly used (e.g., in [13]). The most common are summarized in Table 2.…”

Section: Featuresmentioning

confidence: 99%

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Daga

Garibaldi

2019

Information

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Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher's Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes.

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“…High transmission ratio, strong load-bearing and high efficiency makes modern gearboxes are always considered to be critical components in various industrial applications, such as wind turbine generator system, helicopter main speed reducer, aerospace engineering and etc [27,38]. In real practice, however, gearboxes will inevitably be subjected with dynamic heavy-duty loads under complex operating conditions, making the breakdown or even accidents of the engineering system [3,4,8,32,39].…”

Section: Introductionmentioning

confidence: 99%

Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox

Li¹,

Wang²

2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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Article citation info: develop the condition monitoring and fault diagnostic techniques for the gearboxes.Most of the modern gearbox fault diagnostic methods utilize vibration analysis to extract the fault features, and then make decision according to sophisticated signal processing techniques or expert knowledge of diagnosticians [1,2,6,9,18,33]. For instance, Feng et al. [10] successfully introduced the Vold-Kalman filter into timefrequency analysis to extract fault features of the planetary gearbox under unstable operation conditions. Tang et al. [28] firstly presented a novel fault detection method to identify the categories of gearbox

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Monitoring and maintenance of a gantry based on a wireless system for measurement and analysis of the vibration level

Abstract: Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl

Cited by 13 publications

References 23 publications

Introduction to Special Issue on Symmetry in Mechanical Engineering

Introduction to Special Issue on Symmetry in Mechanical Engineering

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox

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