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 3.188508 3.484890
1 -3.780078 3.444729
2 -4.677518 1.831337
3 -2.277233 -0.731859
4 3.406270 -5.472779
... ... ...
1086 -0.682471 0.476438
1087 -12.315975 10.315186
1088 -13.129631 13.588141
1089 -13.579655 18.647978
1090 6.118420 -2.576016

1091 rows × 2 columns

Alternatively you can ask the recorder for a histogram matrix:

rfc.recorder.histogram(bins=16)

from                                        to                                        
(-18.213395585606847, -15.766840290773057]  (-18.226496442303667, -15.762645612945093]    0.0
                                            (-15.762645612945093, -13.298794783586521]    0.0
                                            (-13.298794783586521, -10.834943954227949]    0.0
                                            (-10.834943954227949, -8.371093124869375]     0.0
                                            (-8.371093124869375, -5.907242295510802]      0.0
                                                                                         ... 
(18.484933836899998, 20.931489131733784]    (8.875862680640633, 11.339713509999207]       0.0
                                            (11.339713509999207, 13.80356433935778]       0.0
                                            (13.80356433935778, 16.26741516871635]        0.0
                                            (16.26741516871635, 18.731265998074928]       0.0
                                            (18.731265998074928, 21.1951168274335]        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