Rainflow Analysis of Switching Markov Loads
(1999) In Doctoral Theses in Mathematical Sciences 1999:4. Abstract
 Rainflow cycles are often used in fatigue analysis of materials for describing the variability of applied loads. Therefore, an important characteristic of a random load process is the intensity of rainflow cycles, also called the expected rainflow matrix (RFM), which can be used for evaluation of the fatigue life. In this thesis mainly two problems are addressed: the first is computation of the expected RFM for a given load model; and the second is modelling of a load from its RFM. Special interest is given to switching random loads, which are random loads where the stochastic properties change over time, due to changes of the system dynamics. For a vehicle, the change of properties could reflect different driving conditions.
... (More)  Rainflow cycles are often used in fatigue analysis of materials for describing the variability of applied loads. Therefore, an important characteristic of a random load process is the intensity of rainflow cycles, also called the expected rainflow matrix (RFM), which can be used for evaluation of the fatigue life. In this thesis mainly two problems are addressed: the first is computation of the expected RFM for a given load model; and the second is modelling of a load from its RFM. Special interest is given to switching random loads, which are random loads where the stochastic properties change over time, due to changes of the system dynamics. For a vehicle, the change of properties could reflect different driving conditions.
The first Markov model to be considered is a Markov chain (MC). In rainflow analysis only the local extremes, also called turning points, of the load are of interest. Hence, another very useful approach in applications is to apply a Markov chain of turning points (MCTP) model. Both switching Markov chains and switching Markov chains of turning points are considered as models for switching loads. The switching is modelled by a Markov chain, which leads to a so called hidden Markov model.
The problem of computing the expected RFM is solved for all the Markov models described above, including the switching loads, and the results are explicit matrix formulas. Rainflow inversion, which is the problem of computing a load model given an expected RFM, is solved for the MCTP model. A switching load gives rise to a mixed RFM. Methods for decomposition of a measured mixed RFM are derived, where estimates of the proportions and the switching frequencies of the subloads, as well as estimates of the models for the subloads can be obtained. By including sideinformation in the decomposition the accuracy of the estimates can be improved. The rainflow inversion and decomposition can be used for generating random load sequences from a measured RFM. Finally, the exact distribution of the number of interval crossings by a MC for a finite time horizon is computed. Crossings of intervals have a direct connection to rainflow cycles. Several examples are given in order to illustrate the different topics in the thesis. (Less)
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https://lup.lub.lu.se/record/19297
 author
 Johannesson, Pär ^{LU}
 supervisor
 opponent

 Prof Krogstad, Harald E., Institutt for matematiske fag, NTH, N7491 TRONDHEIM, Norge
 organization
 publishing date
 1999
 type
 Thesis
 publication status
 published
 subject
 keywords
 actuarial mathematics, programming, operations research, Statistics, rainflow matrix, fatigue, vehicle load, hidden Markov model, random load, Markov model, Switching process, Statistik, operationsanalys, programmering, aktuariematematik
 in
 Doctoral Theses in Mathematical Sciences
 volume
 1999:4
 pages
 205 pages
 publisher
 Mathematical Statistics, Centre for Mathematical Sciences, Lund University
 defense location
 Centre for Mathematical Sciences, Sölvegatan 18, sal MH:A
 defense date
 19991029 10:15:00
 external identifiers

 other:ISRN: LUTFMS10121999
 ISSN
 14040034
 ISBN
 9162837842
 language
 English
 LU publication?
 yes
 id
 76939f609fb74a859735e1ebc00e67f2 (old id 19297)
 date added to LUP
 20160401 15:36:17
 date last changed
 20190521 13:25:49
@phdthesis{76939f609fb74a859735e1ebc00e67f2, abstract = {{Rainflow cycles are often used in fatigue analysis of materials for describing the variability of applied loads. Therefore, an important characteristic of a random load process is the intensity of rainflow cycles, also called the expected rainflow matrix (RFM), which can be used for evaluation of the fatigue life. In this thesis mainly two problems are addressed: the first is computation of the expected RFM for a given load model; and the second is modelling of a load from its RFM. Special interest is given to switching random loads, which are random loads where the stochastic properties change over time, due to changes of the system dynamics. For a vehicle, the change of properties could reflect different driving conditions.<br/><br> <br/><br> The first Markov model to be considered is a Markov chain (MC). In rainflow analysis only the local extremes, also called turning points, of the load are of interest. Hence, another very useful approach in applications is to apply a Markov chain of turning points (MCTP) model. Both switching Markov chains and switching Markov chains of turning points are considered as models for switching loads. The switching is modelled by a Markov chain, which leads to a so called hidden Markov model.<br/><br> <br/><br> The problem of computing the expected RFM is solved for all the Markov models described above, including the switching loads, and the results are explicit matrix formulas. Rainflow inversion, which is the problem of computing a load model given an expected RFM, is solved for the MCTP model. A switching load gives rise to a mixed RFM. Methods for decomposition of a measured mixed RFM are derived, where estimates of the proportions and the switching frequencies of the subloads, as well as estimates of the models for the subloads can be obtained. By including sideinformation in the decomposition the accuracy of the estimates can be improved. The rainflow inversion and decomposition can be used for generating random load sequences from a measured RFM. Finally, the exact distribution of the number of interval crossings by a MC for a finite time horizon is computed. Crossings of intervals have a direct connection to rainflow cycles. Several examples are given in order to illustrate the different topics in the thesis.}}, author = {{Johannesson, Pär}}, isbn = {{9162837842}}, issn = {{14040034}}, keywords = {{actuarial mathematics; programming; operations research; Statistics; rainflow matrix; fatigue; vehicle load; hidden Markov model; random load; Markov model; Switching process; Statistik; operationsanalys; programmering; aktuariematematik}}, language = {{eng}}, publisher = {{Mathematical Statistics, Centre for Mathematical Sciences, Lund University}}, school = {{Lund University}}, series = {{Doctoral Theses in Mathematical Sciences}}, title = {{Rainflow Analysis of Switching Markov Loads}}, volume = {{1999:4}}, year = {{1999}}, }