Pär Johannesson

Modelling of road profiles using roughness indicators

The vertical road input is the most important load for durability assessments of vehicles. We focus on stochastic modelling of the road profile with the aim to find a simple but still useful model. The proposed nonstationary Laplace model with ISO spectrum has only two parameters, and can be efficiently estimated from a sequence of roughness indicators, such as IRI or ISO roughness coefficient. Thus, a road profile can be stochastically reconstructed from roughness indicators. Further, explicit approximations for the fatigue damage due to Laplace roads are developed. The usefulness of the proposed Laplace-ISO model is validated for eight measured road profiles.

Variation mode and effect analysis: an application to fatigue life prediction

We present an application of the probabilistic branch of variation mode and effect analysis (VMEA) implemented as a first-order, second-moment reliability method. First order means that the failure function is approximated to be linear around the nominal values with respect to the main influencing variables, while second moment means that only means and variances are taken into account in the statistical procedure. We study the fatigue life of a jet engine component and aim at a safety margin that takes all sources of prediction uncertainties into account. Scatter is defined as random variation due to natural causes, such as non-homogeneous material, geometry variation within tolerances, load variation in usage, and other uncontrolled variations. Other uncertainties are unknown systematic errors, such as model errors in the numerical calculation of fatigue life, statistical errors in estimates of parameters, and unknown usage profile. By treating also systematic errors as random variables, the whole safety margin problem is put into a common framework of second-order statistics. The final estimated prediction variance of the logarithmic life is obtained by summing the variance contributions of all sources of scatter and other uncertainties, and it represents the total uncertainty in the life prediction. Motivated by the central limit theorem, this logarithmic life random variable may be regarded as normally distributed, which gives possibilities to calculate relevant safety margins.

Modelling roughness of road profiles on parallel tracks using roughness indicators

The vertical road input is the most important load for durability assessments of vehicles. We focus on stochastic modelling of the parallel road profiles with the aim to find a simple but still accurate model for such bivariate records. A model is proposed that is locally Gaussian with randomly gamma distributed variances leading to a generalised Laplace distribution of the road profile. This Laplace model is paired with the ISO spectrum and is specified by only three parameters. Two of them can be estimated directly from a sequence of roughness indicators, such as IRI or ISO roughness coefficients. The third parameter, needed to define the cross-spectrum between the left and right road profiles, is estimated from the sample correlation. Explicit approximations for the expected fatigue damage for the proposed Laplace-ISO model are developed. The usefulness of the methods is validated using measured road profiles.

Detection of steering events using hidden Markov models with multivariate observations

In this article we propose a method to identify steering events, such as curves and manoeuvres for vehicles. We use a hidden Markov model with multidimensional observations, to estimate the number of events. Three signals, lateral acceleration, steering angle speed and vehicle speed, are used as observations. We demonstrate that hidden Markov models with a combination of continuous and discrete distributions for observations can be used to detect steering events. Further, the expected number of events is estimated using the transition matrix of hidden states. The results from both measured and simulated data show that the method works well and accurately estimates the number of steering events.