The interaction of an industrial enterprise as a system with the state economic system as a supersystem has been analyzed. The information flow at an operating enterprise has been analyzed in compliance with the recommendations of ISO 9000. We tried to formalize competitive indices of products produced by industrial enterprises. A scheme of expanding (developing) product quality characteristics into the parameters of technological processes is presented. We have also tried to formalize the components of the model that guarantees product quality. The article presents a scheme of model algorithm for control and management of quality.

In the context of risk assessment, we focus on the prediction of an unknown quantity Z whose value is realised in the future, and for which experimental data are not available. We deal with the issue of the uncertainty associated to the difference between the output of the model used for the prediction of Z and the true unknown value of Z itself. Accepted principles and methods for handling this uncertainty in the specific conditions of risk assessment are still lacking. Through the paper we seek to contribute by:

1. making a clear distinction between model output uncertainty (epistemic uncertainty about the differences between the true values of the output quantities and the values predicted by the model) and sources of model output uncertainty, from incomplete/imprecise knowledge on the values of the parameters of the model to model assumptions, simplifications and approximations introduced in the model,
2. distinguishing between model output uncertainty, structural model uncertainty and parameter (model input quantities) uncertainty,
3. establishing explicit links between the different purposes of modelling and risk assessment, discussing how model output uncertainty should be treated in the different instances.

We argue that in risk assessment, quantification of model output uncertainty serves for the qualification and acceptance of the models used, whose outputs feed the following risk-informed decision making process.

In this paper the Kamat-Riley (K-R) event-based Monte Carlo simulation method was used for reliability analysis of longwall shearer machine. Shearer machine consists of six subsystems; water, haulage, electrical, hydraulic, cutting arms and cable systems in a series network configuration. A shearer in the Tabas coal mine was selected as case study and its all failure data were collected and used for reliability analysis of subsystems. With negligible assumption of time to repair, a flowchart was built for programming the simulation process. The Matlab mathematical programming software was used for reliability simulation process. Finally the reliability plot of longwall shearer machine was achieved and upper and lower bound reliability were calculated. The results illustrate that the reliability of shearer machine reduces to zero in a period of 100h. There is a 50% chance that the shearer will not fail for the first 12h of operation.

This paper investigates technical and organizational tools to improve performances of a multi-state degraded system subject to multiple competing failure processes. The competing failure processes treated in this paper are oil insulating aging and electrical contacts wear out of high voltage oil circuit breaker. To degradation processes, is associated random shocks process highlighted by the stresses due to short-circuit solicitations. To keep a high level of circuit breaker reliability, two policies are developed and cover reliability increasing of the downstream feeder and reliability based preventive maintenance of the item. The first policy is developed using technical and organizational measures, while the second policy is based on improvement factors method optimizing availability under threshold reliability and a maximum benefit. The results obtained using a case study allow the decision maker to reach better information, to target the equipment that reduces the performances of the system, and to practice suitable maintenance actions.

In this paper a few issues of handling maintenance inventory with heterogeneous part characteristics have been discussed. A typical aeroengine having several years of operational history has been utilized to highlight the differential inventory management approach based on the relative significance of the parts. The replacement rate of the operationally significant components has been evaluated using the maintenance data and it serves as a direct input for assessing the dynamic demand for spare parts. In the overall view, this paper documents the process of identifying the operationally significant high value components, assessing their spares requirement under realistic operational/ maintenance conditions and managing the maintenance inventory in a simple manner.

Structural safety and high reliability are most important requirements of aerospace product. Until recently, a design was considered to be robust if all the variables that affected its life had been taken into account and brought under control. So far designers have traditionally handled variability with safety factors. Some safety factors are derived from observation and analysis and in many cases it use to be pure guesswork. In those cases, the bigger the guess, the bigger the risk, the bigger the safety factor resulting in over design which is undesirable in case of aerospace product. In the present paper Latin Hypercube Sampling (LHS) based quasi random polar sampling technique is suggested for structural safety evaluation. The data generated from LHS are selected quasi-randomly to establish structural load & resistance dispersion and it has been used for safety evaluation. This technique is illustrated with a thin cylindrical pressure vessel, which is used extensively in aerospace applications. The proposed technique found to be simple and this technique will be useful to pressure vessel designer during initial design phase.

In many desalination plants, multi stage flash desalination process is normally used for sea water purification and the reliability analysis of such a complex system with standby support mechanism is of great importance to avoid big loses. Thus, the aim of this paper is to present a reliability analysis of evaporators of a desalination plant. The desalination plant operates round the clock and during the normal operation; six of the seven evaporators are in operation for water production while one evaporator is always under scheduled maintenance and used as standby. The complete plant is shut down for about one month during winter season for annual maintenance. The water supply during shutdown period is maintained through ground water and storage system. Any major failure or annual maintenance brings the evaporator/plant to a complete halt and the plant goes under forced outage state. For the present analysis, seven years failure data has been extracted from operations and maintenance reports of a desalination plant in Oman. Various measures of the plant effectiveness have been obtained probabilistically. Semi-Markov processes and regenerative point techniques are used in the entire analysis.

This paper aims to find the optimal allocation of defense resources between protecting the genuine elements of a k–out–of–n system and deployment of imperfect false elements. Networks such as telecommunication networks, oil pipeline networks can be modeled as k–out–of–n systems. Different type of weapons used in Army, Navy and Air defenses are considered as branches of the force. A country in warfare is defeated if a considerable number of branches are destroyed. This phenomenon is also identical with the failure of a k–out–of–n system. In this work, the destruction probability of a network, protected using imperfect false elements, under intentional attacks is obtained as the damage of a k–out–of–n system. A numerical example is shown to explain this model, and the methodology used in this paper.

The processing time intervals for a generational garbage collector may be ephemeral enough to consider the objects that would survive increase with time continuously according to some probabilistic law. From such a viewpoint, this paper firstly answers for the weak points of cumulative damage model whose damage is additive at discrete times. Secondly, we take up a continuous damage model and apply such model to garbage collection policies. Costs for garbage collections are estimated and two models with tenuring collection times, where tenuring collection is made at time T or at level K for random collections and at the Nth collection or at level K for periodic collections, are proposed. Four cases of optimal tenuring collection times for every model are discussed analytically and numerically, and comparisons of these policies and some useful results are given.

Sensors are widespread in applications ranging from environmental monitoring to distributed surveillance for physical security. Novel protocols and appropriate topologies enable large networks of cheap smart-sensors with the main objective of providing pervasiveness and resilience. In this paper we provide a model-based analysis of a ‘k-out-of-m’ (‘KooM’) voting approach which can be used to correlate data coming from heterogeneous event detecting devices. The approach is based on the assumption of diverse redundancy on sensor technologies. The Bayesian Network formalism is employed to perform the analysis. The results show that by choosing appropriate correlation logics an optimal trade-off can be achieved among probability of detection, false alarm rate, availability and robustness against spoofing attempts, depending on the specific application. Furthermore, it will be shown that majority voting on detector outputs allows for a high cost effectiveness in obtaining performance improvements.

The diesel generator set is one of the most critical systems for a ship’s operation. Consequently, the ship preserves four different diesel generators in addition to the power battery packs. This paper describes the generator operation and the main failure conditions. Assuming that the failure rate of the system parts is constant, the paper shows how the diesel generator system can be modelled based on Homogeneous Continuous Time Markov Chains. Actual data are given for the verification of the model, while a specific software is used for the steady-states probabilities extraction. The paper aims to show the advantages of Markov modelling in Maritime Risk Assessment compared to conventional techniques, such as Fault Trees and Event Trees, which are usually applied in risk estimation maritime studies.

The failure rate function of non-repairable components can be bathtub shaped. Two change points can be defined for a bathtub failure rate curve, which can be viewed as the partition points between the early use, normal use and wear-out phases. There is practical significance to determine the change points, and several non-parametric methods have been developed for this purpose. This paper presents two parametric methods to determine the change points of the bathtub curve. The proposed methods are based on a bathtub curve model with a finite support. A maximum likelihood method with a constraint is developed to estimate the parameters of this model. The methods are illustrated by three real-world examples.