**4.2 The input variable lubricating oil**

The "level of analysis of the lubricating oil," **Tables 8** and **9**, can be presented, for example, with the water content in the oil and solid and non-lubricated particle


**47**

*Source: Authors.*

**Table 9.**

*Maintenance Management with Application of Computational Intelligence Generating…*

Zone A [N] Normal 0.18–2.80 mm/s Commissioned machines should generally operate in

Zone B [P] Permissible 2.80–7.10 mm/s It is acceptable for unrestricted operation for long

Zone C [A] Alert 7.10–18.0 mm/s Unsatisfactory for continuous operations for long

Zone D [C] Critical above 18.0 mm/s It is sufficient to cause damage to the machine at any time

**Class 1-[N] Normal 2-[A] Alert 3-[C] Critical** (Water% volume) (% ≤ 0.2) (0.3) (Above 03) (Micron iron content) (% ≤ 49) (50) (Above 51) (Micron copper content) (% ≤ 1) (20) (Above 21)

A [N] Normal Commissioned machines should generally operate

B [A] Alert Unsatisfactory for continuous operations for long

C [C] Critical It is sufficient to cause damage to the machine at any time

in this area

A B C

this area

periods

periods

**Zone Qualification Operation of machines**

content (iron and copper), the energy sources of the dispatch of load for generation of energy. The levels of analysis of the command type were subdivided into 03

(three) variables, correspondence and information quality [9].

**Zone Qualification Operation of machines**

periods Water% volume 0.3

*DOI: http://dx.doi.org/10.5772/intechopen.82795*

*Source: Authors.*

*Source: Authors.*

**Table 8.** *Lubricating oil.*

*Vibration severity rating relevance function.*

Water% volume % ≤ 0.2 Micron iron content % ≤ 49 Micron copper content % ≤ 19

Micron iron content 50 Micron copper content 20

Water% volume Above 0.3 Micron iron content Above 51 Micron copper content Above 21

*Function of pertinence of the severity according to the oil.*

**Table 7.**

*Maintenance Management with Application of Computational Intelligence Generating… DOI: http://dx.doi.org/10.5772/intechopen.82795*


#### **Table 7.**

*Source: Authors.*

*Maintenance Management*

or not fully reliable information.

inference as shown below.

and respective purposes.

(one) output, all independent of each other.

**4.1 The input variable "vibration analysis"**

ranges on a scale according to **Tables 6–12** is shown below.

and displacement levels measured in the equipment.

**4.2 The input variable lubricating oil**

In the first part the development of the fuzzy rules and of the whole procedure of inference is exposed and in the second part all the tests to evaluate the maintenance and the technical state of the motors. This tool served as the basis for the resolution of the real problem of pre-shipment of cargo to satisfy the rationalized methods of just in time of the thermal plant on the operational conditions of the equipment [14, 15]. The fuzzy system models the style of reasoning, imitating the capability to make decisions in an environment of uncertainty and imprecision. In this way, fuzzy logic is an intelligent technology, which provides a mechanism to manipulate imprecise information—concepts of small, high, good, very hot, cold—and that is able to infer an estimated answer to a question based on an inexact, incomplete knowledge,

The development of a computational tool supports the load dispatch according to the location of motors and generators for thermal energy, analyzing the main thermoelectric generation variables for the entire predictive maintenance process. All variables are inserted considering the intervals determined in the rules of

The computational interface was useful for the search of some preselected characteristics to enable its implementation. **Tables 6–12** show such characteristics

In this context, the following groups of information and data are abstracted: the input values, called crisp, the linguistic variables, and the fuzzy variables. The fuzzy logic is justified in the solution of this case study in function of the input variables with better representation in fuzzy sets. The variables due to the dimension of the universe of study were divided in 04 (three) and 03 (two) inputs and 01

For the determination of each variable, it was convenient to divide them into strips to approximate the actual situation to be checked. The calculation of these

As the first level of variation, "vibration level" in **Tables 6** and **7**, let us consider better variable levels that were subdivided into four variables, normal, permissive, alert, and critical, each corresponding to the classification of vibration, velocity,

The "level of analysis of the lubricating oil," **Tables 8** and **9**, can be presented, for example, with the water content in the oil and solid and non-lubricated particle

**Class 1-[N] Normal 2-[P] Permissible 3-[A] Alert 4-[C] Critical mm/s** (Class I) (0.18–0.71) (0.71–1.80) (1.80–4.50) (Above 4.50) (Class II) (0.18–1.10) (1.10–2.80) (2.80–7.10) (Above 7.10) (Class III) (0.18–1.80) (1.80–4.50) (4.50–11.2) (Above 11.2) (Class IV) (0.18–2.80) (2.80–7.10) (7.10–18.0) (Above 18.0)

A B C D

**46**

*Source: Authors*

*Manufacturer vibration levels.*

**Table 6.**

*Vibration severity rating relevance function.*


#### **Table 8.**

*Lubricating oil.*


#### **Table 9.**

*Function of pertinence of the severity according to the oil.*

content (iron and copper), the energy sources of the dispatch of load for generation of energy. The levels of analysis of the command type were subdivided into 03 (three) variables, correspondence and information quality [9].


#### **Table 10.**

*Thermography to determine hot spots.*


#### **Table 11.**

*Function of pertinence of the classification of thermography.*


#### **Table 12.**

*Variable "engine technical status".*

## **4.3 The input variable "thermography analysis level"**

Thermography analysis is an input variable, **Tables 10** and **11**, that can be used as a tool for load dispatching. The levels of thermographic analysis were just been subdivided in four (4) variables, each one corresponding to the dynamic memory. The use of images in thermal plants is very important for this reason. The infrared radiation is a base of studies on the thermal images, which has a function of capturing this radiation, interpreting and generating a quantitative image of the temperature of the studied body [16].

#### **4.4 Output variable "technical condition of the motor"**

The "estimated technical state of the engine (ETM)" is the output variable of the system, in relation to vibration (oil, water, iron, and copper). **Table 12** describes the

**49**

**Figure 3.**

*Maintenance Management with Application of Computational Intelligence Generating…*

operating state of the generating units. The variable under study, as well as the variable "level," was transferred to a percentage scale of 100% where "GREET" corresponds to the range of maximum values and the variable "BAD" corresponds to the range of minimum values up to zero. This value gives a greater range of possibilities,

The fuzzy inference with the input and output variables was performed employing the MATLAB version 8.0 tool and using a Mamdani model. This model adopts semantic rules for the processing of inferences and is commonly referred to as maximum-minimum inference. Such an inference model applies well to this type of problem since it uses union and intersection operations between sets. All variables are entered considering the intervals determined in the rules of inference. **Figure 3** shows the variables "vibration," "water," thermography, "iron," and "copper" accord-

All variables are entered considering the intervals determined in the rules of inference. **Figures 4–7** show the variables "vibration, water, thermography, iron,

The first input variable is a thermography (**Figure 4**). According to **Tables 6**

The second input variable is water (**Figure 5**) produced by the generating units.

The third input variable is the thermography (**Figure 6**) produced by the gener-

The fourth input variable is iron (**Figure 7**) produced by the generating units.

The fifth input variable is copper (**Figure 8**) produced by the generating units.

After editing the pertinence functions of all variables, the implemented rules are arranged in **Figure 10**, as shown in **Figure 8** for the visualization of the linguistic variables, thus forming antecedents and subsequent ones based on the Fuzzy infer-

To better understand the screen expressed, **Figure 11** shows all the possibilities that the simulation can produce. The movement of the red lines determines the

The motor technical state is a product of the relationship between the input variable and output variable, which compose the pertinence functions expressed in

*DOI: http://dx.doi.org/10.5772/intechopen.82795*

making the case study more precise.

and copper" according to the figures below.

ating units. According to **Tables 10** and **11,** we have

According to **Tables 8** and **9,** we have

According to **Tables 8** and **9**, we have

According to **Tables 8** and **9,** we have

the curves of **Figure 9**.

other rule to be evaluated.

*Mamdani's model. Source: Authors.*

ence rules .

**5. Fuzzy simulation**

ing to **Figure 3**.

and **7,** we have

*Maintenance Management with Application of Computational Intelligence Generating… DOI: http://dx.doi.org/10.5772/intechopen.82795*

operating state of the generating units. The variable under study, as well as the variable "level," was transferred to a percentage scale of 100% where "GREET" corresponds to the range of maximum values and the variable "BAD" corresponds to the range of minimum values up to zero. This value gives a greater range of possibilities, making the case study more precise.
