ARAMIS Training Services is the qualified ARAMIS response to the needs of technical and managerial training of our Customers. His goal is to assist Client companies to improve the skills and technical expertise of their employees.
The courses have a duration ranging from 1 to 3 days, and are provided in two different modes:
- Standard: pre-defined content and held at the ARAMIS premises.
- Custom: held at customer sites (suggested for more than three attendees). The course content can be defined according to the clients’ needs.
M01 - RAM ANALYSIS
Duration
2 days
Program and objectives
The goal of this course is to provide the attendees with the basic concepts, methodological competences and quantitative modelling tools used for Reliability, Availability and Maintainability (RAM) analysis. The course covers the following topics:
- Basic concepts: failure time distribution, reliability, hazard function, bath-tub curve.
- Probability distributions commonly used in RAM analyses.
- Reliability of simple systems.
- Reliability Block Diagram (RBD).
- Fault Tree Analysis (FTA).
- Availability and Maintainability.
Who Should Take the Course
Individuals involved in the performance of system RAM analysis, 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 fundamentals of RAM analysis and its use for achieving improvements in systems or processes performance.
M02 - RELIABILITY CENTERED MAINTENANCE
Duration
1 day
Program and objectives
The course presents the Reliability Centered Maintenance (RCM) technique, which is commonly used in industry to set proper maintenance policies by a systemic process of system analyses for better understanding of the relationships among scheduled maintenance and reliability. The course provides an overview of the RCM process and details the steps required to implement it. The attendees will understand the fundamentals of RCM, its benefits and how it can be applied in day-to-day operations. Successful completion of this course will foster the attendees understanding of RCM concepts and processes, and enable participation in or supervising of RCM analyses.
Exercise section
The course includes the development of a scaled-down RCM case study as hand-on case study.
Who Should Take the Course
Individuals responsible for or involved in the organization and implementation of maintenance processes, 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 fundamentals of RCM and its use for achieving reliability improvements. Basic knowledge of the FMEA is advantageous for a quick understanding of the course contents.
M03 - RISK-BASED MAINTENANCE
Duration
1 day
Program and objectives
The goal of this course is to provide the attendees with the methodological competences and the quantitative tools required to implement or supervise the development of Risk-Based Maintenance (RBM) strategies. The topics covered in the course are:
- The formation and concepts of Safety and Risk.
- Reverse Fault Tree Analysis.
- Analytic Hierarchy Process.
- RBM applications.
Exercise section
The course provides examples of successful applications of RBM to industrial systems.
Who Should Take the Course
Individuals responsible for or involved in the organization and implementation of maintenance processes, 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 fundamentals of RBM and its use for achieving plant performance improvements through better maintenance.
M04 - ON-CONDITION AND PREDICTIVE MAINTENANCE
Duration
3 days
Program and objectives
The goal of this course is to provide the attendees with the concepts, methodological competences and modeling tools for Prognostics and system Health Management (PHM), for on-condition and predictive maintenance of industrial components and systems. The course covers the following topics:
- Fault Detection
- Fault Detection: applications to industrial case studies
- Fault Diagnostics
- Fault Diagnostics: applications to industrial case studies
- Fault Prognostics: methods for fault prognostics
- Applications to fault prognostics
Exercise section
The course includes some exercise sections with computers about fault detection, diagnostics and prognostics.
Seminar
The course includes an industrial seminar: “Maintenance in electrical power plants; Monitoring the conditions of industrial equipment in practice”.
Who Should Take the Course
Individuals responsible for and involved in the development of maintenance policies and its improvement by PHM forward on-condition and predictive methods, 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 fundamentals of PHM and its use for improving the system performance through a more effective and efficient maintenance setting.
M05 - MAINTENANCE ENGINEERING
Duration
2 days
Program and objectives
The goal of this course is to provide the attendees with the methodological competences and the modeling tools used for setting proper maintenance policies on industrial components and systems. The course trains in the concepts and procedures at the basis of the safe and economic operation of industrial systems. The course covers the following topics:
Maintenance concepts.
Importance of maintenance in industry.
Maintenance approaches: Corrective (corrective); Preventive (Planned, On Condition, Predictive, Prescriptive).
Computerized Maintenance Management Systems.
Maintenance Costs.
Maintainability.
Maintenance strategic planning: Reliability Centered Maintenance, Risk-based maintenance (basic concepts)
Exercise section
The application of different maintenance approaches to a practical case study will be discussed.
Who Should Take the Course
Individuals responsible for and involved in the optimization of maintenance policies, 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 fundamentals of Maintenance Engineering.
RA01 - ESTIMATION OF RELIABILITY PARAMETERS FROM EXPERIMENTAL DATA
Duration
2 days
Program and objectives
Information about the life time distribution of a component can be inferred from ‘life test’, whereby a number of identical units of the component are operated and their lifetimes recorded. The knowledge of the stochastic behavior of the component allows estimating the probability of having a failure at future time instants, and, on this basis, making maintenance decisions.
The course covers the following topics:
- Complete and censored datasets.
- Maximum Likelihood and Method of moments estimation techniques.
- Confidence intervals.
- Probability plots.
- Goodness of fit tests.
- Practical case studies: Exponential distribution, Weibull distribution.
- Multi-state system parameter estimation: case studies.
- Bayesian estimation.
Exercise section
The course includes some exercise sessions with computers.
Who Should Take the Course
Individuals responsible for and involved in the characterization of the statistical behavior of operated components, 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 basics of statistics in support of reliability engineering.
RA02 - MARKOV MODELS FOR RELIABILITY ANALYSES
Duration
1 day
Program and objectives
This course introduces the basics of the Markov approach to system modeling for reliability and availability analysis. In this approach, the stochastic process of evolution of the system in time is described through the definition of the system states, the possible transitions among these states and their rates of occurrence. The various system states are defined in terms of the states of the components comprising the system. The components are not restricted to having only two possible states but rather may have a number of different states such as functioning, in standby, degraded, partially failed, completely failed, under maintenance, etc.; the various failure modes of a component may also be defined as states. The transitions between the states occur randomly in time, because caused by various mechanisms and activities such as failures, repairs, replacements and switching operations, which are random in nature. Under specified conditions, the stochastic process of the system evolution may be described as a so-called Markov process, which is mathematically defined by a system of probability equations that can be solved either analytically or numerically.
Exercise session
An example of the technique application on a practical case study will be presented.
Who Should Take the Course
Individuals responsible for and involved in investigating advanced modeling techniques of system behaviors, 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 fundamentals of the technique.
RA03 - MONTE CARLO SIMULATIONS FOR RELIABILITY AND AVAILABILITY ANALYSES
Duration
1 day
Program and objectives
This course gives an introduction to the theory of Monte Carlo simulation for reliability and availability analysis. The presentation is kept at an intuitive and practical level. The Monte Carlo simulation method is shown to offer a powerful tool which can be of great value in the analysis of complex systems, due to its inherent capability of achieving a closer adherence to reality in the representation of the system stochastic behavior.
Exercise section
The course includes some exercise sessions with computers.
Who Should Take the Course
Individuals involved in and responsible for reliability, availability and maintainability (RAM) analyses of 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 Monte Carlo method.
RA04 - EVOLUTIONARY ALGORITHMS FOR THE OPTIMIZATION OF RELIABILITY, DESIGN AND PRODUCTION AVAILABILITY
Duration
1 day
Program and objectives
This course illustrates the use of Evolutionary Algorithms (EAS) such as the Genetic Algorithms and the Differential Evolution within the area of RAMS (Reliability, Availability, Maintainability and Safety) optimization. The methodological concepts behind the operation of the EAs are presented. The steps of the algorithm are sketched to some details for both the traditional breeding procedure as well as for more sophisticated breeding procedures. The necessity of affine transforming the fitness function, object of the optimization, is discussed in detail, together with the transformation itself. Finally, two examples of application are illustrated with regards to problems of reliability allocation and periodic inspection and maintenance.
Exercise section
The course includes some exercise sessions with computers.
Who Should Take the Course
Individuals involved in and responsible for solving optimization problems, 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 Evolutionary Algorithms.
RA05 - PETRI NETS FOR RELIABILITY AND AVAILABILITY ANALYSIS
Duration
1 day
Program and objectives
Petri nets serve mainly as the pictorial representation of dynamic processes and systems, as they allow a deep understanding of the connections among the sub-systems and components. They are widely used in reliability engineering, especially in dynamical contexts.
Exercise section
The course includes some exercise sessions with computers.
Who Should Take the Course
Individuals involved in and responsible for solving optimization problems, 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 Petri Nets
RV01 - QUANTITATIVE RISK ANALYSIS
Duration
1 day
Program and objectives
The objective is to provide the students with the adequate methodological knowledge and quantitative modeling capabilities for carrying out risk analysis with the required scientific rigor. The expertise offered is that needed by the professional and conscientious safety and reliability analyst and manager. The course covers the following topics:
Risk: Qualitative and quantitative definitions, assessment and acceptability.
Probabilistic risk assessment: identification and quantification of accidental sequences (fault and event tree analyses); risk curves and matrices.
Risk analysis as a tool in support of regulatory licensing and operating requirements (Environmental Impact Assessment, Performance assessment, etc.)
Exercise section
Quantitative exercise classes are carried out in support to the comprehension of the material covered in class.
Who Should Take the Course
Individuals involved in and responsible for 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 Risk and its utility in managing a plant.
RV02 - DEPENDENT FAILURES
Duration
1/2 day
Program and objectives
The course explains the concept of dependent failures (e.g. common causes) and illustrates the approaches used to model their effects on system reliability. This is quite relevant in reliability and risk analyses since in spite of the fact that all modern technological systems are highly redundant, they still fail because of dependent failures which can defeat the redundant system protective barriers and thus contribute significantly to risk. Quantification of such contribution is necessary to avoid gross underestimation of risk.
Who Should Take the Course
Individuals involved in and responsible for performing reliability and risk analyses of the operated 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 dependent failure issue.
RV03 - IMPORTANCE MEASURES
Duration
½ day
Program and objectives
The course is devoted to the presentation of the concept of importance measures and their role in reliability and risk analyses. From a broad perspective, importance measures aim at quantifying the contribution of components to the system performance, e.g. its reliability, availability or safety. For example, in system engineering applications, such as aerospace and transportation, the impact of components is considered on the system unreliability or, for renewal systems such as the manufacturing production and power generation ones, on the system unavailability. Information about the importance of the components constituting a system, with respect to its safety and availability, is of great practical aid to system designers, operators and managers: indeed, the identification of which components mostly determine the overall system behavior allows one to trace system bottlenecks and provides guidelines for effective actions of system improvement, in design, operation and management.
Exercise section
The course includes some exercise sessions on practical case studies.
Who Should Take the Course
Individuals involved in and responsible for maintenance process optimization, 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 Importance Measures and their utility in effectively improving the overall system reliability and risk.
RV04 - BAYESIAN BELIEF NETWORK FOR RISK ANALYSIS
Duration
1 day
Program and objectives
A Bayesian Belief Network is a probabilistic graphical model, which is mainly seen as a tool allowing the analyst to exploit different information, deterministic or probabilistic, emerging from the real world, under the condition of complex relations between a large number of variables. BBN applications comprise a large range of risk analysis studies, such as: the classification of components and subsystems of a nuclear plant based on safety performance assessment, the assessment of integrated fire prevention and protection systems, prediction, and uncertainty analysis, fault diagnostics. The Bayesian approach provides also an aid for decision-making as a tool for improving the qualitative analysis throughout numerical procedures and to find a suitable reliability framework for dynamic systems.
Exercise section
The course includes some exercise sessions on practical case studies.
Who Should Take the Course
Individuals involved in and responsible for maintenance process optimization, 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 BBN and their utility in effectively improving the overall system reliability and risk.
RV05 - VULNERABILITY AND RESILIENCE ANALYSIS OF CRITICAL INFRASTRUCTURES
Duration
1 day
Program and objectives
The aim of this course is of presenting the state of knowledge in the evolving field of vulnerability and resilience analysis of critical infrastructure, which are complex physical-engineered networks systems. The targeted audience encompasses students of natural and engineering sciences at MS or PhD level, researchers in the field, non-routine practitioners in industries and agencies as well as executives responsible for critical infrastructure design, operation and protection. The course is structured around a vulnerability and resilience analysis of example system. At first the course introduces critical infrastructures in a top-down manner and defines the key terms including the concept of vulnerability followed by elaborations on characteristics of critical infrastructure such as complexity and dimensions of interdependencies, and on challenges to methods. Approaches for vulnerability assessment are categorized and outlined in more general terms before some methods for screening analysis and in-depth analysis are presented in detail. The methods are selected according to their eligibility to meet the challenges posed by the basic characteristics of the types of infrastructures and the objectives of the analysis defined beforehand. Selected methods, e.g. complex network theory, probabilistic techniques, and object oriented modeling, will be introduced in terms of basic approach, algorithm and measures, and explained by means of illustrative examples; strengths and weaknesses will be assessed separately and finally comparatively.
Exercise section
Illustrative examples are used to explain the theoretical concepts
Who Should Take the Course
Individuals involved in and responsible for critical infrastructure management. Engineers, maintenance professionals, plant/facility managers and operators will benefit from the knowledge the course provides on the fundamentals of vulnerability and resilience analysis
T01 - FAILURE MODE, EFFECTS, AND CRITICALITY ANALYSIS (FMECA)
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.
T02 - HAZARD AND OPERABILITY ANALYSIS – HAZOP
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.
T03 - FAULT TREE ANALYSIS (FTA)
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.
T04 - EVENT TREE ANALYSIS (ETA)
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.
T05 - GOAL TREE & SUCCESS TREE FOR SYSTEM RISK ANALYSIS
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.
T06 - ENSEMBLE MODELS FOR FAILURE DETECTION
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.
T07 - CLUSTERING ALGORITHMS FOR FAULT DIAGNOSIS
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.
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.
T08 - PARTICLE FILTERING FOR FAILURE PREDICTION
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.
T09 - NEURAL NETWORKS
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 several extensions (e.g., Encoders, Recurrent NN,…) 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.
