Libor Zak

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.

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.

Applying regression diagnostics for identifying non-standard behaviour of a technical system

System condition is an important characteristic in the stage of operation and maintenance during a life cycle. The system condition in respective time periods usually correlates to system time deterioration. Since the degradation may lead to both soft and hard failure, reliability characteristics might be needed to describe each type of such failure. We concentrate on selected oil characteristics which are strongly correlated to both operating and calendar time elapse. In this paper we focus on soot particles and we model mathematically the effect of independent operating values [Mh] and [day] in relation to soot concentration [%]. Using the recorded data we are trying to find the records which could help us to change the oil concentration of soot particles significantly.

Oil additives used as indicator and input for preventive maintenance optimisation

In this contribution we present selected mathematical - statistical methods which are used to analyse oil field data from heavy off-road military vehicles. We apply selected regression functions for description of the oil additives degradation in time. This helps for creating the picture about the system - vehicle engine in our case - in service operation behaviour. We investigate the oil field data which are further explored. The oil contains pieces of information both about the engine oil degradation as well as about the engine deterioration. These characteristics are indicated when analysing oil quality using characteristics such as changed viscosity, decreasing additives like Mn, Si, Zn, etc. Other published approaches present wear and polluting particles in oil (e.g. particles such as Fe, Pb, Cu, etc. from engine wear). The novelty here is to providing picture of oil deterioration as inputs to change e.g. life cycle costing, policy of item maintenance, operation and mission planning of the system.

Using oil quality indicators as system technical life characteristics and maintenance optimizers

Using the information from oil which is a part of a technical system has been presented a couple of times. The results introduced so far usually dealt with wear metals which get into the oil. However, this paper is based on a different approach. We have assessed Anti-Oxidant and Anti-Wear Particles (AOWP) - oil quality is primarily determined by these particles. These particles are additives in the oil and their amount depends both on operating time and calendar time. In this paper we introduce some mathematical approaches used assess how the number of these particles affects residual useful life (RUL) and maintenance optimizing.

Prediction of vehicle further operation and fault based on tribo-diagnostic data

The paper deals with the application of selected analytical methods for analysing field data from heavy off-road military vehicles. The information from the engine oil is interpreted in a form of polluting particles like particles from a wear process (e.g. Fe, Pb, Cu, etc.) and particles from oil deterioration itself (like Mn, Si, Zn, etc.). These data have good technical and analytical potential which has not been explored well yet. A reasonable set of vehicles and their oil data from in-field-operation are available. Taking into account data processing we assume it will be possible to determine some changes. This may help to modify e.g. a system maintenance policy, estimate system operation and help with mission planning.

Prediction of further Operation based on vehicle Tribo data

The paper deals with application of selected analytical methods for analysing 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 pieces of information have good technical and analytical potential which has not been explored well yet. There is available reasonable set of vehicles and their oil data from in-field-operation. Based on the data we assume it will be possible to determine some changes in the system. This may help to change e.g. the system maintenance policy, may estimate system operation and help in mission planning.

On Approaches of Assessment of Tribo Data From Heavy Tracked Vehicle

The paper deals with application of selected analytical methods in order to analyse field data from heavy tracked 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.

Assessment of Engine Deterioration Based on Oil Fe Data

Nowadays system requirements are set up and evaluated in various manners. When determining an item technical state, there are many options available. However, in order to specify the state and the condition of a system, we choose one off-line approach. The paper deals with mathematical processing, monitoring and analyzing oil field data. Such data comes from the laser spectrography within tribodiagnostic oil tests. When analyzing oil data, we apply mathematical methods based on the analyses and calculations of time series. It is expected to get the results which will help to improve maintenance policy, life cycle costing and operations. Due to the fact that the data sample has been classified as fuzzy and uncertain, the FIS (Fuzzy Inference System) is used.