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Maintenance management with application Chapter | 13 349

This tool served as a basis for the resolution of the real problem of preship-
ment of cargo to satisfy the rationalized methods of just in time of the ther-
mal plant on the operational conditions of the equipment.
A system based on fuzzy logic, as shown in Fig. 13.1, can have its action
schematized by the following constituent elements: fuzzifier; rules, or knowl-
edge base; inference, or logical decision-making, and defuzzifier [12].
In the first part the development of the fuzzy rules and of the whole pro-
cedure 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 preshipment of cargo to satisfy
the rationalized methods of just in time of the thermal plant on the opera-
tional conditions of the equipment [13,14].
The interfuzz aims to model the mode of reasoning, trying to imitate the
ability to make decisions in an environment of uncertainty and imprecision.
In this way, fuzzy logic is an intelligent technology, which provides a mech-
anism to manipulate imprecise information—concepts of small, high, good,
very hot, cold—and that allows one to infer an approximate answer to a
question based on an inexact, incomplete, or not fully reliable knowledge.
Development of a computational tool to support the cargo dispatch
according to the location of motors and generators for thermal energy ana-
lyzes the main thermoelectric generation variables for the entire predictive
maintenance process.
All variables are inserted considering the intervals determined in the rules
of inference as shown below.
The computational interface was useful for the search of some prese-
lected characteristics to enable its implementation. Tables 13.6 13.12 show
such characteristics and the respective purposes.
In this context, the following groups of information and data are
abstracted: the input values, called crisp, the linguistic variables, and the fuz-
zy variables. The fuzzy logic is justified in the solution of this case study in

TABLE 13.6 Manufacturer vibration levels.

Class 1—[N] 2—[P] 3—[A] 4—[C] Critical
Normal Permissible Alert (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