RAMS Analysis Techniques Courses

Duration

1 day

Program and objectives

This course explains the principles of FMECA. This is a qualitative method, of inductive nature, which aims at identifying those failure modes of the components which could disable system operation or become initiators of accidents with significant external consequences. The methods developed for performing this analysis consist, in general, in a qualitative analysis of the system and its functions, within a systematic framework of procedures. FMECA strongly relies on the expertise of the designers, analysts and personnel who have designed and are operating and maintaining the system.

Exercise section

The course also includes some exercise sessions on practical case studies.

Who Should Take the Course

Individuals involved in and responsible for performing RAMS and Risk analyses, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of FMECA and its utility in achieving reliability and safety improvements.

Duration

1 day

Program and objectives

The first step into the analysis of the risk of a given system or process is that of identifying the associated hazards, and the consequences they can lead to when they are activated by an accident initiator. HAZard and OPerability analysis (HAZOP) is one of the most commonly used methodologies used to this aim. It is a qualitative methodology which combines deductive aspects (search for causes) and inductive aspects (consequence analysis) with the objective of identifying the initiating events of undesired accident sequences. HAZOP looks at the processes which are undergoing in the plant. Indeed, the method, initially developed for the chemical process industry, proceeds through the compilation of tables which highlight possible process anomalies and their associated causes and consequences.

Who Should Take the Course

Individuals involved in and responsible for RAMS analyses, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of HAZOP and its utility in achieving reliability and safety improvements.

Duration

1 day

Program and objectives

The course provides the principles and methods of Fault Tree Analysis (FTA). For complex multi-component systems, for example such as those employed in the nuclear, chemical, process and aerospace industries, it is important to analyze the possible ways of failure and to quantify the expected frequency of such failures. Often, each such system is unique in the sense that there are no other identical systems (same components interconnected in the same way and operating under the same conditions) for which failure data have been collected: therefore a statistical failure analysis is not possible. Furthermore, it is not only the probabilistic aspects of failure of the system which are of interest, but also the initiating causes and the combination of events which can lead to a particular failure. The engineering way to tackle a problem of this nature, where many events interact to produce other events, is to relate these events using simple logical relationships (intersection, union, etc.) and to methodically build a logical structure which represents the system. In this respect, Fault Tree Analysis is a systematic, deductive technique which allows developing the causal relations leading to a given undesired event. It is deductive in the sense that it starts from a defined system failure event and unfolds backward its causes down to the primary (basic) independent faults. The method can also provide qualitative information on how a particular event can occur and what consequences it leads to, while at the same time allowing the identification of those components which play a major role in determining the defined system failure. Moreover, it can be solved in quantitative terms to provide the probability of events of interest starting from knowledge of the probability of occurrence of the basic events which cause them.

Exercise section

The course also includes some exercise sessions on practical case studies.

Who Should Take the Course

Individuals involved in and responsible for performing RAMS analyses, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of FTA and its utility in achieving reliability and safety improvements.

Duration

1 day

Program and objectives

This course provides the principles and methods for Event Tree Anaylsis (ETA). ETA is an inductive logic method for identifying the various accident sequences which can generate from a single initiating event. The approach is based on the discretization of the real accident evolution in few macroscopic events. The accident sequences which derive are, then, quantified in terms of their probability of occurrence. The events delineating the accident sequences are usually characterized in terms of: i) the intervention (or not) of protection systems which are supposed to take action for the mitigation of the accident (system event tree); ii) the fulfillment (or not) of safety functions (functional event tree); iii) the occurrence or not of physical phenomena (phenomenological event tree). Typically, the functional event trees are an intermediate step to the construction of system event trees: following the accident-initiating event, the safety functions which need to be fulfilled are identified; these will later be substituted by the corresponding safety and protection systems. The system event trees are used to identify the accident sequences developing within the plant and involving the protection and safety systems.

Who Should Take the Course

Individuals involved in and responsible for performing RAMS analyses, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of ETA and its utility in achieving reliability and safety improvements.

Duration

1 day

Program and objectives

Goal Tree-Success Tree (GTST) technique, combined with master logic diagram (MLD) method, is a powerful technique to represent the system functional and structural characteristics, and constitutes a useful support to the FMECA analysis. The idea beyond the GTST technique is that hierarchical structures provide a more effective description of complex systems. In details, two hierarchical trees are built:

  • goal tree (GT), which breaks the system down according to its qualities (i.e. goals and functions);
  • success tree (ST), which decomposes the systems into parts.

MLDs are then used to give a compact representation of the relationships between GT functions and ST objects, or between different types of elements within GT or ST. Then, the combination of GTST and MLD can logically and hierarchically display the functions, sub-functions and the way in which various hardware, software, and people interact with each other to attain these functions.

Who Should Take the Course

Individuals involved in and responsible for developing FMEA analysis on complex systems, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of the technique.

Duration

1 day

Program and objectives

The underlying idea of ensemble algorithms is derived from daily life decision making: in the hope to making a more informed decision, a number of individual opinions are usually sought and then opportunely weighted and combined to elaborate the ultimate decision.

When building RAMS or Risk models, there are random and uncertainty aspects which may lead to substantially varying decisions. Then, combining the outputs of several models can reduce the risk of an unfortunate selection of a poorly performing model.

The course describes some algorithms for which the ensemble approach is particularly beneficial, and the techniques to estimate the performance of the ensemble model.

Who Should Take the Course

Individuals interested in advanced computational techniques in support to RAM and Risk analyses, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of ensemble models and its utility in achieving reliability improvements through better maintenance.

Duration

1 day

Program and objectives

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same cluster are more ‘similar’ to each other than to those in other clusters. It is used in a large variety of RAMS applications.

The clustering algorithms introduced in the course are:

  • K-Means.
  • Fuzzy C-Means.
  • Spectral clustering.
  • AdaBoost.

Who Should Take the Course

Individuals interested in advanced computational techniques in support to Fault diagnostic, maintenance, RAM and Risk analyses, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of clustering.

Duration

1 day

Program and objectives

In recent years, the affordability of on-line monitoring technologies has led to a growing interest in new maintenance paradigms such as Predictive Maintenance (PrM). This is founded on the possibility of monitoring equipment to obtain information on its conditions, which is then used to identify problems at an early stage, and predict their changes over time for estimating the equipment Residual Useful Life (RUL). An accurate estimation of the RUL is of great interest, as it would provide lead time to plan, prepare, and execute the repair or the replacement of the equipment, e.g., by delaying the maintenance to the next planned plant outage.

A number of prognostics approaches have been proposed in the literature in support of PrM. Among these, Particle Filtering (PF) is emerging as a powerful model-driven technique, capable of robustly predicting the future behavior of the probability distribution that describes the uncertainty in the actual degradation state of the equipment (e.g., the crack depth of a mechanical component). From the prediction of the future evolution of the degradation and knowledge of the failure threshold (i.e., the degradation value beyond which the equipment loses its function), one can infer the equipment RUL.

The course provides the attendees with the theoretical basis of the PF technique, and shows practical applications of PF in support to PrM.

Exercise section

The course includes some exercise sessions with computers.

Who Should Take the Course

Individuals interested in advanced PHM techniques, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of sequential Monte Carlo methods.

Duration

2 days

Program and objectives

Artificial Neural Networks (ANN) are computing devices inspired by the function of the nerve cells in the brain. They are composed of many parallel, interconnected computing units; each of these performs a few simple operations and communicates the results to its neighboring units. In contrast to conventional modelling, ANNs can learn the required input/output relationship, possibly nonlinear, by a process of training on many different input/output examples. They are also very effective in learning patterns in data that are noisy, incomplete and which may even contain contradictory examples.

By reason of this flexibility, ANN and their extensions (e.g., Auto Associative NN, Recurrent NN, Infinite Impulse Response Locally Recurrent Neural Network) are used in a variety of reliability engineering applications: diagnostics, prognostics, reliability estimation, etc.

Exercise section

The course includes some exercise sessions with computers.

Who Should Take the Course

Individuals interested in advanced PHM techniques, in different fields of application (military, transportation, manufacturing or production). Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of Neural Networks.

to sign up  for the courses, please contact ats@aramis3d.com