ENBIS Workshop at MMR2007
ENBIS is organising a Case Study Workshop on Reliability in Glasgow, UK, in the afternoon of July, 1st, 2007, prior to the MMR2007 (Mathematical Methods in Reliability) conference. More information on MMR2007 can be found at the conference page
The ENBIS workshop will take place at Strathclyde University in the same location as MMR2007. It will be held in John Anderson Building, Room K327.
Two case studies will be discussed, from different perspectives, in 90 minutes sessions. Researchers with different backgrounds (namely, engineers and mathematical statisticians) will illustrate the problems, discuss the entertained models and the selected ones, along with findings and open problems. In particular, the case studies will be presented by Italian and Swedish researchers.
Participants will have the opportunity to contribute to the discussion, as well. Registered participants will receive the available papers in advance so that they can take part to the discussion more fruitfully.
The peculiarity of the workshop is the combination of the mathematical aspects characterising the MMR conferences with the very applied nature of ENBIS, showing not only the final results of a research but exploring thoroughly the motivations for it and critically reviewing both failures and successes during the investigation.
Session 1 (14.00 - 15.30)
Stochastic modelling of cylinder liners wear in a marine diesel engine
- Massimiliano Giorgio: Engineering aspects
- Fabrizio Ruggeri: Mathematical aspects
- Bo Bergman, Ake Lonnqvist and Thomas Svensson: Discussion
Session 2 (16.00 - 17.30)
Reliability and Variation
- Bo Bergman: A short introduction to the relation between variation and reliability with an emphasis on Failure Mode and Effect Analysis (FMEA) and Variation Mode and Effect Analysis (VMEA)
- Ake Lonnqvist: Including noise factors in design-FMEA
- Thomas Svensson: Probabilistic VMEA in practice
- Massimiliano Giorgio and Fabrizio Ruggeri: Discussion
Stochastic modelling of cylinder liners wear in a marine diesel engine
Massimiliano Giorgio (Second University of Napoli)
Fabrizio Ruggeri (CNR - IMATI)
Dario Bocchetti (Grimaldi Group)
Fernanda D'Ippoliti (CNR - IMATI)
Maurizio Guida (University of Salerno)
Gianpaolo Pulcini (CNR-IM)
Ship Diesel engines are requested to have high reliability and availability levels. A major factor in determining failure of heavy-duty Diesel engines is the ring/liner wear. In high power Diesel marine propulsion engines maximum wear usually occurs in the top region of the cylinder liner, which is subjected to high thermomechanical and tribological stresses that produce relevant early local damages.
Many studies of Diesel engine wear agree that the dominant wear mechanism in this region is due to the high quantity of abrasive particles on the piston surface, occurring by the combustion of heavy fuels and oil degradation (soot). The soot abrasive wear mechanism can be envisioned as a three-body contact mode. If the lubricant film thickness is less than the soot particle size, then the soot can be involved in a three-body abrasive action with the ring/liner metal surfaces. Indeed soot particles have shown to be harder than the corresponding engine parts. In addition to abrasive wear, a corrosive wear has also been observed. The corrosive agents were identified as sulphuric acid, nitrous/nitric acids and water. Cylinder liner, almost always, fails when wear at the Top Dead Center (TDC) exceeds a specified threshold.
This paper presents some sthocastics models of cumulative damage that can be used to model the wear process of the cylinder liners of marine naval Diesel engines. These models can be used to perform condition based reliability estimation and to plan condition based maintenance activities. Both processes with independent and dependent increments are coinsidered.
The proposed models have been applied to a data set consisting of wear measures of the cylinder liners of two SULZER RTA 58 engines equipping twin ships of the Grimaldi Group. For each model, parameter estimation procedures are also given.
The version with a picture is available here
Including noise factors in design-FMEA
Ake Lonnqvist (Volvo Car Corporation/Division of Quality Sciences and Chalmers University of Technology)
Reliability Engineering, Off-line Quality Control and Robust Design Methodology (RDM) are all methodologies that strive to avoid failures.
The well known method Failure Mode and Effect Analysis (FMEA) is frequently used to identify potential causes of failures and their potential consequences. It is a technique to study each part of a product or a process and identify potential failure mechanisms that can cause part failure and then understand the potential consequences.
The approach for Off-line Quality Control and Robust Design Methodology (RDM) is to regard the so called noise factors, defined by e.g. G Taguchi, as the sources or causes of failures and the methodologies aim to avoid failures by making products and processes less sensitive to these factors.
Does this view of noise factors as the causes of failure correspond to the failure mechanism approach in the Failure Mode and Effect Analysis (FMEA)?
The study shows that it is possible to connect noise factors, or rather noise factor categories, to a majority of potential causes of failures identified in already performed FMEAs, but it appears that a non-negligible number of causes fall outside the noise factor categories. It was, in fact, necessary to introduce a specific non-noise-factor category in order to address all causes to categories. An additional result was that the categorization of causes into noise factor categories revealed valuable information on what noise factors that were considered in the FMEAs.
Since Failure Mode and Effect Analysis (FMEA) is one of the most utilized methods in the automotive industry this result opens up for several opportunities to increase efficiency and mutual benefit for both Reliability Engineering and Robust Engineering regarding Failure Mode Avoidance.
Probabilistic VMEA in practice
Thomas Svensson (SP Technical Research Institute of Sweden)
Par Johannesson (Fraunhofer Chalmers Research Centre for Industrial Mathematics)
We will 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 with respect to the main influencing variables, second moment means that only means and variances are taken into account in the statistical procedure.
We study the fatigue life of an air engine component and aim at a safety margin that takes all scatter and 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 variation. Uncertainty is defined as unknown possible systematic errors, such as model errors in the numerical calculation of fatigue life, statistical errors in estimates of parameters, and unknown usage profile.
By defining also uncertainties as random variables, the whole safety margin problem is put into a common framework of second order statistics, with the Gaussian approximation formula as the main tool. By using a simple log transformation, the failure function is regarded as linear enough for a proper estimate of the scatter and uncertainty contributions based on the first order approximation.
Thus, the final estimated variance of the logarithmic life is obtained through summing the variance contributions of all sources of scatter and uncertainty, 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 that, in turn, is transformed back to fatigue life margins for comparisons with demands.
The distinction between scatter and uncertainty sources makes it straightforward to make updates based on new information, such as laboratory experiments for estimating model errors or failure reports at usage.