Rainflow Analysis of Switching Markov Loads
Research output: Thesis › Doctoral Thesis (monograph)
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.
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.
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.
Details
Authors  

Organisations  
Research areas and keywords  Subject classification (UKÄ) – MANDATORY
Keywords

Original language  English 

Qualification  Doctor 
Awarding Institution  
Supervisors/Assistant supervisor 

Award date  1999 Oct 29 
Publisher 

Print ISBNs  9162837842 
Publication status  Published  1999 
Publication category  Research 
Bibliographic note
Defence details
Date: 19991029
Time: 10:15
Place: Centre for Mathematical Sciences, Sölvegatan 18, sal MH:A
External reviewer(s)
Name: Krogstad, Harald E.
Title: Prof
Affiliation: Institutt for matematiske fag, NTH, N7491 TRONDHEIM, Norge
