Predictive Maintenance (AI)
Introduction
An expert system (SE) is a computer system that emulates reasoning, acting just as an expert would do in any area of knowledge.[1].
Expert systems are one of the applications of artificial intelligence that aims to simulate human reasoning, in the same way that an expert in an area of specialization would do.[2][3][4].
Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented primarily as if-then rules rather than through conventional procedural code.[5] The first expert systems were created in the 1970s and then proliferated in the 1980s.[6] Expert systems were one of the first forms of truly artificial intelligence (AI) software. successful.[7][8][9][10][11][12][13]
An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies rules to known facts to deduce new facts. Inference engines can also include explanation and debugging skills.
Types of expert systems
There are mainly three types of expert systems:
In each of them, the solution to a given problem is obtained:
Advantages and limitations of expert systems
Advantages
The goal of knowledge-based systems is to make the critical information required for the system to function explicit rather than implicit.[14] In a traditional computer program, logic is embedded in code that can usually only be reviewed by an IT specialist. With an expert system, the goal was to specify the rules in a format that was intuitive and easy to understand, review, and even edit by domain experts rather than IT experts. The benefits of this explicit knowledge representation were rapid development and ease of maintenance.
Ease of maintenance is the most obvious benefit. This was achieved in two ways. First, by eliminating the need to write conventional code, many of the normal problems that can cause even small changes to a system could be avoided with expert systems. Essentially, the logical flow of the program (at least at the highest level) was simply a given for the system to simply invoke the inference engine. This was also a reason for the second benefit: rapid prototyping.") With an expert system shell, it was possible to input a few rules and have a prototype developed in days instead of the months or years typically associated with complex IT projects.