The ThreePointDetector class

class pylife.stress.rainflow.ThreePointDetector(recorder)[source]

Classic three point rainflow counting algorithm.

from pylife.stress.timesignal import TimeSignalGenerator
import pylife.stress.rainflow as RF

ts = TimeSignalGenerator(10, {
    'number': 50,
    'amplitude_median': 1.0, 'amplitude_std_dev': 0.5,
    'frequency_median': 4, 'frequency_std_dev': 3,
    'offset_median': 0, 'offset_std_dev': 0.4}, None, None).query(10000)

rfc = RF.ThreePointDetector(recorder=RF.LoopValueRecorder())
rfc.process(ts)

rfc.recorder.collective
from to
0 3.251855 3.176047
1 -1.291603 0.840173
2 2.399841 6.967480
3 -5.535270 7.737142
4 4.206911 1.819273
... ... ...
944 1.742229 14.452057
945 1.688455 -2.227643
946 -2.720787 1.716658
947 15.242698 -4.620461
948 -7.451687 15.268679

949 rows × 2 columns

Alternatively you can ask the recorder for a histogram matrix:

rfc.recorder.histogram(bins=16)

from                                       to                                        
(-15.72879772899442, -13.214558559501745]  (-15.082010850616276, -12.597163847382646]    0.0
                                           (-12.597163847382646, -10.112316844149017]    0.0
                                           (-10.112316844149017, -7.627469840915388]     0.0
                                           (-7.627469840915388, -5.142622837681758]      0.0
                                           (-5.142622837681758, -2.6577758344481293]     0.0
                                                                                        ... 
(21.984789813395707, 24.499028982888383]   (12.25130618495365, 14.736153188187279]       0.0
                                           (14.736153188187279, 17.221000191420906]      0.0
                                           (17.221000191420906, 19.705847194654538]      0.0
                                           (19.705847194654538, 22.190694197888163]      0.0
                                           (22.190694197888163, 24.6755412011218]        0.0
Length: 256, dtype: float64

We take three turning points into account to detect closed hysteresis loops.

  • start: the point where the loop is starting from

  • front: the turning point after the start

  • back: the turning point after the front

A loop is considered closed if following conditions are met:

  • the load difference between front and back is bigger than or equal the one between start and front. In other words: if the back goes beyond the starting point. For example (A-B-C) and (B-C-D) not closed, whereas (C-D-E) is.

  • the loop init has not been a loop front in a prior closed loop. For example F would close the loops (D-E-F) but D is already front of the closed loop (C-D-E).

  • the load level of the front has already been covered by a prior turning point. Otherwise it is considered part of the front residuum.

When a loop is closed it is possible that the loop back also closes unclosed loops of the past by acting as loop back for an unclosed start/front pair. For example E closes the loop (C-D-E) and then also (A-B-E).

Load -----------------------------
|        x B               F x
--------/-\-----------------/-----
|      /   \   x D         /
------/-----\-/-\---------/-------
|    /     C x   \       /
--\-/-------------\-----/---------
|  x A             \   /
--------------------\-/-----------
|                    x E
----------------------------------
|              Time
__init__(recorder)[source]

Instantiate a ThreePointDetector.

Parameters:

recorder (subclass of AbstractRecorder) – The recorder that the detector will report to.

process(samples)[source]

Process a sample chunk.

Parameters:

samples (array_like, shape (N, )) – The samples to be processed

Returns:

self – The self object so that processing can be chained

Return type:

ThreePointDetector