Florent Brissaud

Reliability analysis for new technology-based transmitters

The reliability analysis of new technology-based transmitters has to deal with specific issues: various interactions between both material elements and functions, undefined behaviours under faulty conditions, several transmitted data, and little reliability feedback. To handle these particularities, a “3-step” model is proposed, based on goal tree–success tree (GTST) approaches to represent both the functional and material aspects, and includes the faults and failures as a third part for supporting reliability analyses. The behavioural aspects are provided by relationship matrices, also denoted master logic diagrams (MLD), with stochastic values which represent direct relationships between system elements. Relationship analyses are then proposed to assess the effect of any fault or failure on any material element or function. Taking these relationships into account, the probabilities of malfunction and failure modes are evaluated according to time. Furthermore, uncertainty analyses tend to show that even if the input data and system behaviour are not well known, these previous results can be obtained in a relatively precise way. An illustration is provided by a case study on an infrared gas transmitter. These properties make the proposed model and corresponding reliability analyses especially suitable for intelligent transmitters (or “smart sensors”).

Handling parameter and model uncertainties by continuous gates in fault tree analyses

Abstract Fault tree analyses are widely used in probabilistic risk assessments (PRA) to model and evaluate safety system reliability. Coherent results can then be obtained by expressing probabilities according to input information. However, if uncertainties in parameters (e.g. failure rates) and models (e.g. relationships between events) lead to high uncertainty in results, the latter may not be robust enough to be helpful. It is therefore necessary to perform uncertainty analyses and, in order to handle both parameter and model uncertainties in a fault tree framework, a continuous gate denoted the ‘C-gate’ is proposed. By acting on ‘weights’, it is then allowed to continuously graduate model part structures between parallel and series. C-gates can also be translated into equivalent fault trees, using fictitious events, so that classical reliability tools are used to perform reliability evaluations with both parameter (failure rates) and model (through the weights) uncertainty analyses. An application on a new technology-based transmitter shows that results can be obtained with relatively low uncertainty and, in some cases, even with lower variances than any of the inputs. These properties are discussed and tend to demonstrate that the lack of knowledge on the structures of some parts of the model can be handled and partially compensated for by the proposed fault tree approach to perform PRA.

Modelling failure rates according to time and influencing factors

Reliability of systems often depends on their age (aging or burn-in period), on intrinsic factors (dimensioning, quality of components, material, etc.) and on conditions of use (environment, load rate, stress, etc.). Modelling of failure rates as a function of time and influencing factors is therefore proposed. The latter combines a Weibull distribution and a Cox proportional hazards model. A rather flexible representation of the system life phases is then possible, as well as consideration of a large variety of qualitative or quantitative, precise or approximate, influencing factors. The approach which is presented uses feedback data to define the model parameters by maximum likelihood estimations. An application on safety valves provides an illustration of this approach and an evaluation of the model performances.

Probability of failure of safety-critical systems subject to partial tests

A set of general formulas is proposed for the probability of failure on demand (PFD) assessment of MooN architecture (i.e. k-out-of-n) systems subject to proof tests. The proof tests can be partial or full. The partial tests (e.g. visual inspections, partial stroke testing) are able to detect only some system failures and leave the others latent, whereas the full tests refer to overhauls which restore the system to an as good as new condition. Partial tests may occur at different time instants (periodic or not), up to the full test. The system performances which are investigated are the system availability according to time, the PFD average in each partial test time interval, and the total PFD average calculated on the full test time interval. Following the given expressions, parameter estimations are proposed to assess the system failure rates and the partial test effectiveness according to feedback data from previous test policies. Subsequently, an optimization of the partial test strategy is presented. In the 2oo6 system given as example, an improvement of about 10% of the total PFD average has been obtained, just by a better (non-periodic) distribution of the same number of partial tests, in the full test time interval.

Dependability Issues for Intelligent Transmitters and Reliability Pattern Proposal

Abstract New technologies make way for “intelligent” transmitters by integrating new functionalities: error measurement corrections, self-adjustment, self-diagnosis for measurement and transmitter status, online reconfiguration, and digital bidirectional communication. Industrialists are taking advantage of more accurate measurements, cost reductions and facilities. For industrial risk prevention, new dependability issues are arising. Functionalities such as self-diagnosis and digital communication seem to be in favour of control systems availability. On the other hand, the high amount of electronics and programmable units implies new failure causes and modes which are usually not well known. In this paper, dependability issues for intelligent transmitters are discussed and a reliability model is proposed. By using a Goal Tree – Success Tree (GTST) technique, both functional and material aspects of an intelligent transmitter pattern are included. Material-material, material-function, and function-function relationships are then demonstrated in Master Logic Diagrams (MLD). These results are proposed as support for further case studies. For example, the impact of any material failure on any function, and the reliability of the main functions, can be assessed using this kind of model. Other dependability tools can take advantage of this reliability pattern, for example when the behavioural aspects of complex systems are undetermined.