*2.4.2 Blackboard*

Communication of information between expert systems is done by a mechanism called a blackboard. A blackboard is a place within computer memory where information stored in an expert system is "pinned" so that any other expert system can use them if you need the information contained therein to achieve your goals.

The blackboard is a structure that contains information that can be examined by cooperative expert systems. What these systems do with this information depends on the application.

Still, a blackboard, draft, or working memory (temporary memory) has a useful life during the course of a query and is linked to a concrete query. It is an area of memory used to make evaluations of the rules that are retrieved from the knowledge base to arrive at a solution.

Information is recorded and erased in an inference process until the desired solution is reached.

#### *2.4.3 Inference machine*

Inference engine or inference engine is a permanent element that can even be reused by various expert systems. It is the party responsible for seeking knowledge base rules to be evaluated, directing the inference process [13]. Knowledge must be prepared for good interpretation and objects must be in a certain order, represented by a context tree.

Basically the inference engine is divided into the following steps:


Summarizing the above tasks, it can be said that the rules necessary to reach a goal must be sought in the knowledge base. These rules will be placed on the blackboard, and existing rules will only be evaluated after the most recent ones.

The evaluation order on the blackboard follows a stack-like structure to achieve the most recent goal. The rule will continue to be evaluated as long as the assumption conditions are true, otherwise the rule will be dropped, the set goal will be unstacked and a new rule will be loaded.

When a value of a parameter in a given context is not known and is not in the stack structures, one should then look for new information in the knowledge base, search for new rules, or ask the user directly.

### *2.4.4 Neural networks*

An artificial neural network is made up of several processing units whose operation is quite simple. These units are usually connected by communication channels that are associated with a certain weight. Units perform operations only on their local data, which is input received by their connections. The intelligent behavior of an Artificial Neural Network comes from interactions between network processing units.

Neural networks allow optimized selection of a particular solution alternative for a given event or change. The neural network is used in this process to evaluate the results of the expert system, that is, the final solution should be selected as the best of all presented, the neural network allows to establish this solution.

#### *2.4.5 Fuzzy logic*

Fuzzy logic is based on fuzzy set theory. Traditionally, a logical proposition has two extremes: either it is completely true or it is completely false. However, in Fuzzy logic, a premise varies in degree of truth from 0 to 1, which leads to being partially true or partially false.

Fuzzy logic is the logic that supports the modes of reasoning that are approximate rather than exact. Fuzzy systems modeling and control are techniques for rigorously handling qualitative information. Derived from the concept of fuzzy sets, fuzzy logic forms the basis for the development of process modeling and control methods and algorithms, reducing the complexity of design and implementation, making it the solution to control problems hitherto intractable classic techniques.

In classical and modern control theories, the first step in implementing process control is to derive the mathematical model that describes the process. The procedure requires knowing in detail the process to be controlled, which is not always feasible if the process is too complex. Existing control theories apply to a wide variety of systems where the process is well defined.

However, all of these techniques are not capable of solving real problems whose mathematical modeling is impractical. For example, in many situations a considerable amount of essential information is only known a priori qualitatively. Similarly, performance criteria are only available in linguistic terms. This picture leads to inaccuracies and inaccuracies that make it impossible to use most of the theories used so far.

Fuzzy modeling and control theory are techniques for rigorously handling qualitative information. It assesses how imprecision and uncertainty should be managed and, in so doing, become powerful enough to properly manipulate knowledge. This technology considers the relationship between inputs and outputs, aggregating various process and control parameters. This allows processes considered complex to be reconsidered so that the resulting control systems provide a more accurate result as well as stable and robust performance. The sheer simplicity of implementing fuzzy control systems can reduce the complexity of a project to a point where previously intractable problems are now solvable.

**141**

*Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems*

The intelligent monitoring and supervision system becomes autonomous to decide, but this decision should indicate the best action that should be taken to mitigate or eliminate a particular change. This intervention must be in real time and in online mode. It should show the type of application that will be performed, the point where the intervention will be made, the components that will be reached and their intervention time. This solution can be given by the following

Depending on the situation, it can be in real time and in Online mode, meaning the maintenance team can make the necessary adjustments without shutting down the equipment and reducing its availability in the shortest possible time. The smart system should provide recommendations for making these correctives without compromising equipment operation. This is accomplished through expert system intervention. This system will decide on what type of maintenance

Depending on the situation, it can be in real time and in Online mode, meaning the maintenance team can make the necessary adjustments without shutting down

The intelligent system must have the ability to make this decision, supported by

The final and ultimate solution will demonstrate the versatility, autonomy and efficiency of monitoring and supervision systems when they work and are structured as intelligent systems. Errors in procedures with these systems will be

Safe (high availability and reliability) and efficient operation of production systems, balanced and timely investments, reduced operating and energy costs. Following is a typical output (report) of the software developed for the implementation of the methodology, especially the part related to data evaluation, fault diagnosis, root cause finding and determination, action decision making to be

Following is an output of the software developed to implement the proposed intelligent system framework [1, 15]. All the tools presented in this chapter have

the equipment and reducing its availability in the shortest possible time.

The smart system should provide recommendations for corrective action without compromising equipment operation. This is accomplished through expert

*DOI: http://dx.doi.org/10.5772/intechopen.90158*

*2.5.2 Predictive and proactive maintenance*

**2.5 Solution**

procedure:

to perform.

system intervention.

**2.6 Innovation**

**2.7 Benefits**

been included:

*2.5.3 Element or device replacement*

technical and economic criteria (losses).

minimal to ensure safety and accuracy.

performed and execution of these actions [3, 4].

*2.5.1 Maintenance*

*Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems DOI: http://dx.doi.org/10.5772/intechopen.90158*
