David Valis

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.

Modeling and Analysis of the Reliability of Systems with One-shot Items

This article deals with modelling and analysis of the reliability of complex systems that use one-shot items during their operation. It includes an analysis of the impact of the reliability of used one-shot items on the resulting reliability of the system as a whole. Practical application of theoretical knowledge is demonstrated on an example of a model of reliability of an aircraft gun that was used for optimization of the guns design during its development and designing. The analyzed gun uses two types of one-shot items - rounds intended for the conduct of fire and special pyrotechnic cartridges designed for re-charging a gun after a failure of the round. A proposed model enables us to analyze the responsiveness of the whole system due to a change of reliability of rounds and pyrotechnic cartridges and a change of the number of used rounds and pyrotechnic cartridges. This article demonstrates utilization of a proposed model to determine an optimum number of pyrotechnic cartridges that will achieve a required probability of fulfilment of mission with the given reliability of individual subsystems of the gun, rounds and pyrotechnic cartridges. Proposed procedures of modelling and analysis of the reliability of complex systems with one-shot items were used in practice in designing and development of the PL-20 aircraft gun. Development of the gun was successfully finished in 2005, and the gun was fielded in the armament of the Czech Republic Air Force.

A Tool for Decision Making in K-Out-of-N System Maintenance

The paper deals with possibilities of maintenance optimisation of k-out-of-n systems. The first part of the paper briefly analyses influence of preventive maintenance concept on reliability of k-out-of-n system and presents possibilities of preventive maintenance optimisation with respect to maintenance costs. The second part of the paper presents a suggested method of maintenance optimisation in the case of a processing machine for dosing tiny loose material and smaller piece goods. The method takes into account not only maintenance costs but also production losses caused by a prospective failure of the system. The proposed method facilitates decisions on maintenance intervals modification and the range of maintenance from the economic point of view.

Modelling of influence of various operational conditions on Li-ion battery capability

We need energy resources to assure performance of mechatronic systems. The performance assurance is of major importance in present systems since it leads to high availability and capability. It also helps to reduce failure occurrence. In the article we want to find the up state time of Li-ion batteries. Results are related to influence of the operating environment at different climatic levels. We want to find the hitting time of critical threshold, when the capacity/voltage of the battery drops below the determined threshold. One part of the results was achieved due to experiments; another part was achieved by simulation supported by mathematical analysis. For the simulation we use the Wiener process with drift whose parameters are estimated using the least square method and the Weierstass theorem. The aim is to determine the time interval of the first hitting time which assures that the battery will work under given climatic conditions.

Mathematical analysis of soot particles in oil used as system state indicator

Different state indicators are used when assessing technical systems. If we are to use indirect diagnostic measures, lubrication oil seems to be a good source of different information. It is possible to get the information on the state of the oil and the system. In our article we focus on specific oil particles, i.e. soot. It is formed as a by-product during fuel combustion. Soot contaminates the oil and its concentration might indicate that operation conditions are getting worse. The essential and interesting thing is that some additives in the oil are able to dissolve the soot. In our article we introduce some results obtained from processing oil soot data. The data introduced depend on a few operating measures - kilometres [km], days [day] and moto-hours [Mh]. In the analysis we use deterministic and fuzzy mathematical methods. Our aim is to estimate and study the hitting time of a critical threshold.

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

Selected reliability measures of composites with natural fibres tested in climatic environment

The contribution presents selected reliability measures of composite materials with natural fibres. These composites were tested in climatic environment. Our contribution shows ways for determination of essential reliability characteristics. Such characteristics are based on material tests - bending strength. In this contribution it is presented the bending strength and related qualities for estimating essential reliability measures and testing probability distributions. Novelty of this paper is in performing reliability estimations on polymer composites with selected layers tested and with natural fibres.

On Approaches of Assessment of Tribo Data from Medium Lorry Truck

The paper deals with application of selected analytical methods in order to analyse field data from heavy off-road military vehicles. The information from the engine oil are interpreted in form of polluting particles like particles from wear process (e.g. Fe, Pb, Cu, etc.) and particles from oil deterioration itself (like Mn, Si, Zn, etc.). These particles can give us information both about system state and about oil state. We have reasonable set of oil data from field operation available. Based on the data we assume being able to determine the system condition and propose some changes (e.g. in residual operation life, in maintenance modifications in the intervals, in mission planning, etc.). Selected methods like regression analysis and fuzzy inference system are used for the data assessment.

Contribution to Mathematical Modelling of Oil Field Data

The paper deals with application of selected suitable analytical methods use to analyse field data from medium off-road military vehicles. Some pieces of information from oil are interpreted thank to diagnostics in form of polluting particles. Such particles represent processes in the engine like wear (e.g. Fe, Cu, Pb, etc.) and oil condition (like e.g. Mn, Si, Zn, etc.). These particles can give us information both about system state and about oil state. Thank to good recording system we have data from field operation available. Therefore we decided to use selected analytical – algebraic methods for determining the specific particles generating trend. Based on the outcomes we hope to be able to determine the system condition (e.g. residual operation life, maintenance modifications in the intervals, etc.). Selected methods like regression analysis, base functions and fuzzy inference system will be used for the data assessment. The results itself might be beneficial in other forthcoming analysis like quality, risk and dependability management processes, e.g. for optimization during an operation and maintenance phase, logistics and spare parts planning, life cycle costing, mission planning, etc.