Method of fault prediction of cyclically moving machine component

A technology of fault prediction and machine components, applied in the field of condition monitoring, can solve problems such as complicated implementation, and achieve the effect of improved condition monitoring and less troubleshooting

Inactive Publication Date: 2020-02-07
TETRA LAVAL HLDG & FINANCE SA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Some methods are employed to accommodate variable speeds, but such solutions are also complex to implement and are also associated with other constraints and undesired assumptions

Method used

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  • Method of fault prediction of cyclically moving machine component
  • Method of fault prediction of cyclically moving machine component
  • Method of fault prediction of cyclically moving machine component

Examples

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Embodiment Construction

[0028] Specific examples of the present invention will now be described with reference to the accompanying drawings. However, this invention may be embodied in many different forms and should not be construed as limited to the examples set forth herein. Rather, these examples are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. The terms used in the detailed description of the embodiments shown in the drawings are not intended to limit the invention. In the drawings, the same reference numerals denote the same elements.

[0029] Figure 5 A flowchart of a method 100 for failure prediction of periodically moving machine components is shown. The order in which the steps of method 100 are described and illustrated should not be construed as limiting, and it is contemplated that the steps may be performed in varying orders.

[0030] Accordingly, a method 100 for failure prediction of p...

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Abstract

A method of fault prediction of a cyclically moving machine component is disclosed, wherein each cycle of a plurality of cycles of a motion of the component generates a data distribution of values ofmeasurable movement characteristics during a duration of each cycle. The method comprises, for each cycle; determining the data distribution of the movement characteristics; calculating a measure of central tendency of the values in the data distribution; calculating a quantified measure of a shape of the data distribution over the duration of each cycle; associating the measure of central tendency with the quantified measure of the shape as a coupled set of condition parameters; determining a degree of dispersion of a plurality of coupled sets of condition parameters associated with a plurality of cycles of the cyclically moving component; and comparing the degree of dispersion with a dispersion threshold value, or determining a trend of the degree of dispersion over time, for the fault prediction.

Description

technical field [0001] The present invention relates generally to the field of condition monitoring. More particularly, the present invention relates to a failure prediction method for periodically moving machine parts, a related computer program product and a method for predicting failures in periodically moving machine parts such as bearings, belts or engines. Malfunctioning devices, said machine parts are used in systems such as filling machines or related systems for the production of hermetically sealed packages. Background technique [0002] Condition monitoring of machine components (e.g., bearings, belts, motors, or any other part in a production line), such as in filling machines or related systems when making hermetically sealed packages, is essential to ensure desired functionality and failure prediction over time important. Monitoring deformations in the motion of such components (e.g., vibrations of periodically moving parts in these systems) is an important p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/00
CPCG01M13/00G01H17/00G05B23/024G05B23/0283G01H1/003G01M13/045G05B23/0232
Inventor 马尔科·可康切利卢卡·卡普里大卫·博尔基
Owner TETRA LAVAL HLDG & FINANCE SA
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