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 -1.504503 1.789666
1 -5.378955 6.024227
2 7.689641 0.721345
3 -3.167308 -2.653538
4 -1.161159 1.114979
... ... ...
1024 -8.101027 1.366819
1025 -9.989371 3.250017
1026 -13.705893 12.305978
1027 1.875057 -7.202823
1028 0.108368 -4.589748

1029 rows × 2 columns

Alternatively you can ask the recorder for a histogram matrix:

rfc.recorder.histogram(bins=16)

from                                        to                                        
(-24.120703051666418, -21.445334039036137]  (-21.756490952306315, -19.259009315002267]    0.0
                                            (-19.259009315002267, -16.761527677698222]    0.0
                                            (-16.761527677698222, -14.264046040394174]    0.0
                                            (-14.264046040394174, -11.766564403090127]    0.0
                                            (-11.766564403090127, -9.26908276578608]      0.0
                                                                                         ... 
(16.009832137787804, 18.68520115041809]     (5.715807058038202, 8.21328869534225]         0.0
                                            (8.21328869534225, 10.710770332646298]        0.0
                                            (10.710770332646298, 13.20825196995034]       0.0
                                            (13.20825196995034, 15.705733607254388]       0.0
                                            (15.705733607254388, 18.203215244558436]      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