*Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems DOI: http://dx.doi.org/10.5772/intechopen.89168*

see details in [27, 36]. If virtual risks are computed by formulas (1) and (3) for all points Tgiven from 0 to ∞, the calculated values form a trajectory of the PDF. The mathematical expectation of this PDF means the mean residual time to the next state "Abnormality." It defines MTBLI from this PDF. This output of probabilistic modeling can be transmitted to interested workers. Requirements to AIS operation quality are: quality measures of used information by AIS should meet admissible level recommended in **Table 1**.

Thus, the answer on the first question "How responsible worker can know a residual time before the next parameters abnormalities?" is: the calculated mean residual time to the next state "Abnormality" (MTBLI for "red" range on **Figure 12**) can be transmitted in real time to responsible worker immediately after parameter value cross the border from "Working range inside of norm," "Out of working range, but inside of norm" (from "green" to "yellow" range on **Figures 12** and **13**). It is possible as a result of implementation of the proposed approach—see example of implementation in [27, 36].

To answer the second question, let the next input be formed from data monitored.

Let for every system component, a frequency of occurrence of the latent or obvious threats is equal to once a month and the mean activation time of threats is about 1 day. The system diagnostics are used once for work shift 8 h, a mean duration of the system control is about 10 min, and the mean recovery time of the lost integrity of object equals to 1 day. The workers (they may be robotics, skilled mechanics, technologists, engineers, etc.) are supported by capabilities of an AIS and a remote monitoring systems allowing estimating in real time the mean residual time before the next parameters abnormalities considering the results of probabilistic modeling. Formally they operate as parallel elements with hot reservation (structure on **Figure 4**, right). Owing to AIS support workers are capable to revealing signs of a critical situation after their occurrence. Workers can commit errors on the average not more often once a year (it is proper to skilled workers).

To answer the question we do Steps 1–4 (from **Figure 8**) and use formulas (1)– (3) for solving the problem for complex structure, see **Figure 13**. Here, risks to lose system integrity means risks of "failure" for every subsystem which can be detailed to the level of every separate critical parameter of equipment.

The fragments of built PDF on **Figure 13** show: risk of "failure" increases from 0.000003 for a year to 0.0004 for 10 years and to 0.0013 for 20 years. Thus, the mean time between neighboring losses of integrity (MTBLI) equals to 283 years.

These are some estimations for example assumptions.

Thus, the answer on second question "What risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to skilled workers?" is: risks to lose system integrity may be 0.000003 for a year, 0.0004 for 10 years and 0.0013 for 20 years, herewith (MTBLI) is equal to 283 years. These are the Optimistic estimations for dangerous coal intelligent manufacturing that make sense to take over a desired level of AIS operation effectiveness.

New knowledge for accumulating and improving K-base is as follows:


To answer the first question, the ranges of possible values of conditions are established: "Working range inside of norm," "Out of working range, but inside of norm," and "Abnormality" for each separate critical parameter of equipment. It is interpreted similarly by light signals—"green," "yellow," and "red," as it is reflected on **Figure 12**. Some examples of parameters may include compression, capacity, air temperature (out, in, at machinery room), voltage, etc. The information from Example 6.2 and additional time data of enterprise procedures are used by AIS as input for using formulas (1) and (3) and Steps 1–4 (from **Figure 8**) in real time of company operation activity. Here, risks to lose the system integrity during the given period Tgiven means risks to be at least once in state "Abnormality" within Tgiven. The functions of modeling is performed on special servers (centralized or mapped);

*An example of a coal company with AISs that transformed all system components to the level which is proper to*

**Figure 13.**

**22**

*skilled workers.*

*Probability, Combinatorics and Control*

3.Expected term in average 283 years and more is admissible systemic aim for providing safe company operation.

capabilities of AIS used for a security service of floating oil and gas platform on its operation stage. The difference from previous example is in more degree of uncertainties (because of high complexity) that allows to transform all system components to the level which is proper to medium-level workers of floating oil and gas platform. The same approach, structure, and formulas for probabilistic modeling

*Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems*

Let a floating oil and gas platform is also decomposed on nine subsystems. Every subsystem is enumerated on **Figure 14**, and operates as parallel elements with hot

And let input for modeling is the same as in Example 6.3. Only one difference is because of complexity characteristics are proper to medium-level workers of floating oil and gas platform. For this example, it means workers and AIS can commit errors more often in comparison with skilled workers, for one element it is equal to

For planning possibilities of functions performance by AIS in this example, the

What risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to medium-level workers (realistic view on dangerous oil and gas

To answer the question, we do Steps 1–4 (from **Figure 8**) and use formulas (1)– (3) for solving the problem for complex structure, see structure on **Figure 13**. Here, risks to lose the system integrity mean risks of "failure" for every subsystem. The fragments of built PDF on **Figure 14** show: from 0.0009 for a year to 0.0844 for 10 years and 0.25 for 20 years. Thus, MTBLI equals to 24 years. It is 11.4 times less

Thus, the answer on question is: risks to lose system integrity may be 0.0009 for a year, 0.0844 for 10 years and 0.25 for 20 years; herewith, mean time between neighboring losses of integrity is equal to 24 years. These are the realistic estima-

1.The input (used for modeling) characterize possible complex conditions for functions performance by AIS used for a security service of floating oil and gas

2.The probability of "success" on levels 0.9991 for a year, 0.9156 for 10 years and 0.75 for 20 years or risk of "failure" on levels 0.0009 for a year, 0.0844 for 10 years and 0.25 for 20 years (with possible consequences) and expected term in average 24 years as estimation of mean time between neighboring losses of

integrity are realistic view on dangerous floating oil and gas platform

3.For analyzed project new research to improve characteristics for the security service of floating oil and gas platform for decreasing risks with the proof of its

New knowledge for accumulating and improving K-base is as follows:

Components are: 1—a construction of platform; 2—an AIS on platform for robotics monitoring and control; 3—an underwater communication modem; 4—a remote controlled unmanned underwater robotic vehicle; 5—a sonar beacon; 6—an autonomous unmanned underwater robotic vehicle; 7—non-boarding robotic boat, a spray of the sorbent; 8—non-boarding robotic boat, a pollution collector; and 9—

are used.

reservation.

an unmanned aerial vehicle.

intelligent manufacturing)?

platform.

**25**

1 time a month instead of once a year.

*DOI: http://dx.doi.org/10.5772/intechopen.89168*

often against the results of Example 6.3.

intelligent manufacturing.

probabilistic modeling is being to answer the question:

These are some estimations for example assumptions.

tions for dangerous oil and gas intelligent manufacturing.

efficiency on the basis of modeling is required.

4.Analyzed project of AISs operation effectiveness (that transform all system components to the level which is proper to skilled workers of coal company) can be added to K-base history as a precedent of "success."
