**2.4 Applying corrective actions: alternatives**

Within the various alternatives available to eliminate these changes in the behavior of a given parameter, the best is sought from the technical and economic point of view and, most importantly, that can be applied in real time, either in on mode online or offline [15]. This depends on the severity of the failure, for example whether it is a failure that is likely to happen and the serious consequences or symptoms of an incipient failure.

This optimized solution is found by applying intelligent optimization techniques such as:

**139**

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

An ES is capable of processing non-numerical information, presenting conclusions on a certain subject as long as it is properly oriented and "fed". Another common feature in expert systems is the existence of an uncertain reasoning mechanism that allows one to present uncertainty about domain knowledge. In other words, ES employ human knowledge to solve problems that require the experience of one or more specialists. Within the application of expert systems it is necessary to count on the participation of a robust database, where the knowledge base will be stored

The knowledge base is a permanent but specific element of an expert system. This is where the information of an expert system is stored, that is, the facts and rules. Information stored in a particular domain makes the system an expert in that

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

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

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 knowl-

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

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

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 black-

Basically the inference engine is divided into the following steps:

board, and existing rules will only be evaluated after the most recent ones.

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

(expert knowledge to solve numerous problems).

• Neural networks;

• Fuzzy logic.

*2.4.1 Knowledge base*

domain.

your goals.

on the application.

solution is reached.

by a context tree.

• Select and search;

• Evaluate and Search.

*2.4.3 Inference machine*

edge base to arrive at a solution.

*2.4.2 Blackboard*

• Expert systems;

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


*Fault Detection, Diagnosis and Prognosis*

device is in a process of failure.

• Binary thresholds;

• Diffuse thresholds.

applied.

analytical symptoms.

• Periodic signs;

• Stochastic signals.

symptoms of an incipient failure.

• Expert systems;

in order to achieve its goal, namely:

detecting changes in the behavior of variables.

distinguish frequencies or ARMA models.

• Non-stationary periodic signals;

**2.4 Applying corrective actions: alternatives**

2.Fault diagnosis methods—root cause identification.

limits are the maximum values these variables can reach. When the values of the variables reach these limits, it can be inferred that a variable is presenting abnormal changes. If these changes are continuous or discrete, it can be concluded that the

The fault detection threshold verification technique is based on two procedures

In most situations the binary decision between "normal" and "disturbance" is sometimes artificial, because there is rarely a marked difference between these two states. Therefore, the diffuse threshold procedure is a more realistic alternative for

Identify the impact a failure has on the performance of an element or device. This is strongly related to the severity of the failure. The important part of the diagnostic step is that the system is able to identify the root cause of a problem. Many measured signals show oscillations that are either harmonic or stochastic in nature or both [9]. If changes to these signals are related to actuator, process and sensor failures, signal model-based failure detection methods may be

Assuming special mathematical models for signal measurement, appropriate characteristics can be determined. Comparison with observed characteristics for normal behavior provides changes in these characteristics which are considered as

The signal model can be divided into non-parametric models, such as frequency spectrum or correlation functions, and into parametric models as amplitudes to

At this stage usually the signals that are analyzed for being frequent in different

Within the various alternatives available to eliminate these changes in the behavior of a given parameter, the best is sought from the technical and economic point of view and, most importantly, that can be applied in real time, either in on mode online or offline [15]. This depends on the severity of the failure, for example whether it is a failure that is likely to happen and the serious consequences or

This optimized solution is found by applying intelligent optimization techniques

production processes are focused on the following types of signals:

**138**

such as:

An ES is capable of processing non-numerical information, presenting conclusions on a certain subject as long as it is properly oriented and "fed". Another common feature in expert systems is the existence of an uncertain reasoning mechanism that allows one to present uncertainty about domain knowledge. In other words, ES employ human knowledge to solve problems that require the experience of one or more specialists. Within the application of expert systems it is necessary to count on the participation of a robust database, where the knowledge base will be stored (expert knowledge to solve numerous problems).
