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Risssiumulator16153

Ein Simulator für Risse in historischen Bauwerken ;-) Bzw. ein Messaufbau für Sensoren für Abstandsmessung zur Überwachung von entsprechenden Rissen.

Der Rissimulator16153 und die entsprechenden „Sensoren in einer Aufputzdose“ sind Teil des Projekts MünsterMonitoring.

Aufbau

Fotos

Prototyp 1

Prototyp 2 (aktuell)

Codename: Risssi (mit drei 's')

Messwerte

Folgende Messwerte sollen erhoben werden

VL6180X BME280 Platine
Distanz x
Umgebungshelligkeit x
Temperatur x
Rel. Luftfeuchte x
Luftdruck x
Batteriespannung x

Modell

Einflussgrößen und Messunsicherheit

Laborbuch

Neueste Einträge oben; Zeitstempel jeweils Start eines neuen Experiments.

2019-12-02 0759: manuelle Temperatur-Kalibrierung

2019-11-23 1122: neuer Aufbau

2019-11-17 1030: Start neuer Messreihe

2019-11-17 0030: ToF-Sensor verschraubt und Ambient Light

2019-11-12 2321: platine01 sendet erste Daten

2019-11-10 1738: erste Versuche

sSingle = c(29,27,28,27,25,27,27,28,26,25,28,28,28,28,26,28,27,26,26,27,27,28,26,27,27,26,28,27,28,26,28,26,26,28,27,27,26,29,27,26,26,26,26,26,28,27,26,27,26,26,26,28,27,26,26,27,27,27,28,26,27,27,27,28,27,26,26,27,26,26,28,27,28,27,28,27,27,26,28,28,29,26,27,27,26,27,27,27,26,28,27,27,26,25,28,28,29,27,26,27,27,30,28,27,27,26,27,27,28,26,30,28,27,26,27,29,28,26,26,26,28,29,27,27,28,27,27,26,29,25,25,28,25,26,28,27,27,26,27,28,28,26,28,25,27,28,28,26,28,26,28,25,25,26,27,27,27,27,27,26,28,27,28,27,27,26,27,27,27,28,27,27,25,27,27,27,26,28,27,28,27,26,28,26,27,27,27,27,28,27,24,26,28,27,27,26,28,26,27,27,25,29,28,26,27,27,27,27,27,27,28,26,26,26,27,27,27,27,27,26,27,26,26,27,27,26,28,28,26,25,27,25,26,26,27,25,29,27,28,27,27,28,27,27,26,28,26,25,26,28,28,27,27,26,26,26,26,26,27,27,27,26,27,27,27,27,27,26,27,27,28,28,27,26,28,27,27,25,26,27,26,27,27,27,28,26,28,27,27,27,28,27,28,27,27,26,26,27,26,27,27,27,28,26,26,26,26,26,26,26,26,27,27,28,26,28,25,27,27,26,27,27,27,27,26,27,28,24,28,28,26,28,26,27,27,27,25,26,27,27,27,28,29,27,29,27,27,27,27,27,27,27,28,25,28,27,29,28,27,27,27,27,26,27,27,28,27,27,27,27,26,28,27,28,27,28,24,27,27,27,28,25,25,27,26,27,26,28,25,28,29,29,27,25,28,27,28,27,26,28)
 
summary(sSingle)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  24.00   26.00   27.00   26.91   28.00   30.00 
sd(sSingle)
[1] 0.9691481
    

sContinuous = c(27,28,30,28,27,27,27,27,27,27,28,28,29,29,27,28,26,27,26,26,28,26,26,27,26,27,28,29,27,26,28,27,27,27,27,26,26,26,27,28,28,26,28,27,29,29,27,27,27,27,27,27,27,28,26,27,27,27,25,26,29,25,27,26,28,27,27,28,27,26,26,27,27,26,29,27,26,26,28,29,25,27,26,28,27,27,28,27,28,27,27,26,27,27,28,27,26,27,26,27,27,26,26,26,27,25,29,26,26,27,26,27,28,27,27,26,26,27,27,28,26,27,27,26,27,26,27,27,27,26,27,26,26,28,27,27,27,28,25,27,27,27,27,25,26,26,27,27,26,28,26,26,26,26,26,28,26,27,25,28,27,28,27,27,26,29,27,27,27,25,28,28,24,27,28,27,28,25,25,26,27,26,28,28,27,26,26,25,28,28,28,27,27,27,27,27,25,26,25,26,27,27,26,27,28,27,26,27,28,27,28,27,25,26,26,26,28,27,27,26,27,28,26,26,26,27,27,27,27,28,27,28,27,27,28,28,27,27,26,26,27,28,26,27,28,28,26,26,26,28,27,28,26,27,27,27,27,28,28,27,28,28,29,27,27,29,27,29,26,29,25,27,27,29,28,26,27,29,26,28,27,26,27,26,26,27,27,27,27,26,27,26,27,26,28,28,26,27,28,28,28,25,26,27,26,28,28,26,27,27,29,27,26,26,29,29,28,28,27,27,27,28,27,27,27,27,28,26,28,27,29,28,27,27,27,28,25,29,28,28,28,26,27,25,26,26,28,28,26,28,26,28,28,28,28,27,27,28,28,27,27,28,29,26,29,27,25,26,27,27,28,28,28,27,27,27,28,27,28,27,28,26,28,27,28,26,27,27,26,27,29,26,27,28,27,26,27,28,28,28)''

summary(sContinuous)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  24.00   26.00   27.00   27.02   28.00   30.00 
sd(sContinuous)
[1] 0.9782599
    

sContinuousFast = c(29,28,27,30,27,28,27,28,27,27,28,28,26,29,28,29,29,29,28,28,28,28,28,28,28,26,28,28,27,28,27,27,28,27,28,28,27,29,29,27,29,28,27,28,26,28,27,30,28,27,27,27,28,27,29,27,27,28,26,27,26,29,27,28,27,29,29,28,27,27,27,27,28,27,27,28,29,27,26,27,27,27,28,26,28,27,27,28,29,27,28,29,27,26,28,26,28,28,28,28,28,28,26,28,27,28,28,29,28,28,26,28,26,28,28,28,27,26,27,28,29,27,29,31,28,26,27,27,28,28,28,28,30,28,28,26,26,29,29,26,27,27,27,28,25,26,27,29,27,27,28,28,29,28,28,27,28,27,29,27,25,28,27,27,27,28,28,28,27,27,27,27,27,26,27,27,28,28,27,27,29,28,27,27,27,28,27,28,27,25,26,27,28,27,27,29,29,29,27,27,30,28,28,29,27,29,28,28,27,28,28,28,27,27,29,28,28,29,26,26,27,27,28,30,28,28,28,27,26,26,30,27,28,28,29,26,27,27,30,30,28,28,28,30,29,27,29,27,27,27,28,28,29,28,27,28,26,27,27,28,28,27,29,27,26,27,30,28,27,27,27,28,29,28,28,28,28,27,28,28,27,29,28,27,27,28,28,27,26,27,27,27,27,28,26,28,28,26,28,27,28,27,28,29,27,27,26,27,28,28,28,28,28,27,28,29,27,28,28,27,27,26,28,26,29,26,28,29,27,27,29,28,28,26,28,30,27,28,28,28,28,26,30,30,27,27,27,29,28,29,28,27,27,28,28,28,29,29,28,29,29,29,28,29,27,26,29,27,28,27,28,26,27,29,26,28,28,27,28,27,27,27,28,27,26,26,27,26,27,27,29,29,26,27,29,28,29,28,26,27)''

summary(sContinuousFast)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  25.00   27.00   28.00   27.63   28.00   31.00
sd(sContinuousFast)
[1] 1.004972
    

Datenanalyse mit Rolf

install.packages("tidyverse")
library(tidyverse)

# Daten laden, z.B. sSingle von oben:

summary(sSingle)

df_sSingle = data.frame(sSingle)
plot = ggplot(df_sSingle, aes(sSingle))
plot + geom_bar()