Vibration Analysis for Condition Monitoring of an Automatic Press Machine for Thermoplastic Polymers

Conese, Chiara; Conti, Fabio M.; Cinquemani, Simone; Bono, Francesco Morgan; Zavalloni, Alessandro; Tarabini, Marco

doi:10.1109/metroind4.0iot51437.2021.9488525

Cited by 1 publication

(2 citation statements)

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“…The comparison between the two points is detailed in Table 4. The formula to extrapolate the Hot Spot stress in a type A point (assuming a fine mesh) is shown in (21), with t being the thickness of the main plate [40]. The boundary conditions of the simulation are described below.…”

Section: Model Of the Prototypementioning

confidence: 99%

“…In the case of CM systems applied to industrial presses and similar machines, vibration measurements obtained with accelerometers are generally used as input data. Some examples can be found in sheet metal-forming presses [ 20 ], presses for thermoplastics [ 21 ], and paper-production roll presses [ 22 ]. To estimate the accumulated fatigue in certain parts of the machine, most fatigue calculation methods use the accumulated cycles of strain/stress [ 23 ]; therefore, strain/stress measurement results are of special interest for evaluating the structural health of the monitored machines.…”

Section: Introductionmentioning

confidence: 99%

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Strain Virtual Sensing Applied to Industrial Presses for Fatigue Monitoring

Mora,

Basurko,

Leturiondo

et al. 2024

Sensors

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The techniques that allow one to estimate measurements at the unsensed points of a system are known as virtual sensing. These techniques are useful for the implementation of condition monitoring systems in industrial equipment subjected to high cyclic loads that can cause fatigue damage, such as industrial presses. In this article, three different virtual sensing algorithms for strain estimation are tested using real measurement data obtained from a scaled bed press prototype: two deterministic algorithms (Direct Strain Observer and Least-Squares Strain Estimation) and one stochastic algorithm (Static Strain Kalman Filter). The prototype is subjected to cyclic loads using a hydraulic fatigue testing machine and is sensorized with strain gauges. Results show that sufficiently accurate strain estimations can be obtained using virtual sensing algorithms and a reduced number of strain gauges as input sensors when the monitored structure is subjected to static and quasi-static loads. Results also show that is possible to estimate the initiation of fatigue cracks at critical points of a structural component using virtual strain sensors.

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Section: Model Of the Prototypementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%