Libor Žák

Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data

At present, numerous approaches are devoted to monitoring a system state. Their intention is to determine the current state of a system and predict reliability parameters for the future. This article addresses one of the several possible approaches that allows us to determine a system technical state on the basis of diagnostic data. These diagnostic data are from the area of tribiodagnostics, namely, engine oil. The article examines iron and lead particles that are selected deliberately with respect to their origin in kinematic parts of the system and their degree of correlation with operation measures. The particles occur in oil during both operating time and calendar time development. To model their occurrence during operation time, we have used, in the first part of the article, a mathematical regression method to set parameters. In the second part, we have applied a diffusion model based on a Wiener process. The results confirm that we are able to estimate the residual technical life of a system. Moreover, the results enable us to schedule properly the intervals of preventive maintenance (oil change) and to plan a mission/operation. This results in optimising life cycle costs. It is assumed that the potential of the diagnostic data will be extracted by other approaches and methods. In the subsequent work, it will be useful to determine specific interval values of optimised preventive maintenance.

Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance

Abstract The aim of this article is to estimate system soft failure occurrence and residual technical life in order to optimise firmly planned preventive maintenance. To do this, selected wear particles from oil field data are analysed. Using a large tribodiagnostic dataset we estimate the residual technical life of the observed system statistically. The dataset includes information about particles contained in oil which testify to oil conditions as well as system conditions. We focus here on the wear particles which we (and other analysts) consider to be interesting, ferrum (Fe) and lead (Pb), regarded as contact degradation and wear products. By modelling the occurrence of particles in oil we plan to determine the expected moment when soft failure occurs; this moment might then be set as the time to perform preventive maintenance (PM). Both operation time and calendar time are used here for modelling, for soft failure occurrence determination and for residual technical life estimation. The modelling is based on the specific characteristics of two diffusion processes, the Wiener process (WP) with positive drift and the Ornstein–Uhlenbeck process (OUP). We also applied a fuzzy inference system to support our first results from the diffusion processes as there is a level of uncertainty and fuzziness in the oil field data. Following the modelling outcomes we are able to judge the system hazard rate, predict expected mean residual life and set up principles of “condition based maintenance” (CBM) for this system. However, the possible uses of our outcomes are much wider. For example, they can be used as inputs for operation and mission planning, and life cycle costs can be significantly reduced thanks to the maintenance optimisation.

System Condition Estimation Based on Selected Tribodiagnostic Data

The aim of the paper is to estimate a system-soft failure occurrence and residual technical life. When estimating a residual technical life statistically, usually a big amount of tribodiagnostic data is used. Data include the information about particles contained in oil that testifies to oil and system conditions. We focus here on the particles that we consider to be interesting. They are ferrum (Fe) and lead (Pb) as contact degradation product. By modelling the occurrence of particles in oil, we expect to determine the expected moment for soft failure occurrence or adequate moment to perform preventive maintenance. The way of our modelling is based on the specific characteristics of diffusion processes, namely the Wiener process with positive drift and Ornstein–Uhlenbeck process. Following the modelling results, we could judge hazard rate and set-up principles of ‘CBM - Condition Based Maintenance’ (CBM). However, the possibilities are much wider, because we can also plan operation, mission and reduce life cost. Copyright

One of the Possible Ways to Find Partial Dependencies in Multidimensional Data.

The article deals with one of the possible ways of determining the impact of only one (or selected group) input variable on the output one. When measuring in a real process, the output variable is affected by multiple input variables. The linear dependence of the output variable on the input ones can be determined by the correlation coefficient (multiple correlation coefficient). The influence of only one input variable (or group) can be expressed using the partial correlation coefficient, which is often different from the correlation coefficient. The aim of the paper is to find a way to modify the measured data to match the correlation coefficient of the modified data to the partial correlation coefficient for the original data. This is illustrated by the amount of engine oil soot in the number of hours of operation and the number of days from oil change.

Approaches in Correlation Analysis and Application on Oil Field Data

The paper deals with selected approaches which unite several correlation analysis principles. Field data very often has various forms and contents. The comparison of different approaches will help to determine more precisely which correlation analysis is better for assessing input and output data. In this paper we introduce several correlation principles which can help to select the most suitable correlation approach. We present a traditional correlation analysis and compare it with Pearson and Spearman correlation coefficients. The value of our article lies in comparing several different approaches of the correlation analysis in which the oil field data from diesel combustion engine are used

Assessment of Off-Line Diagnostic Oil Data with Using Selected Mathematical Tools

The paper deals with assessment of oil filed data from heavy off-road vehicle. The oil sample is collected off-line and processed consequently in a tribolaboratory. We call the outcomes from tribolaboratory as oil field data. Firstly we apply selected regression functions for description of the most interesting oil particles generation. It is the vehicle engine and its metal – ferrum, lead or cooper – oil data which are explored for further utilisation. We apply and present methods of multi-variate regression analysis to model the metal – Fe and Pb – data and provide outcomes + estimations for system operation so far and also proposals for system further operation. The novelty is to providing inputs for soft failure identification, to helping to change the life cycle costing, to change the system of maintenance policy, system operation and mission planning.