Introduction -
Artificial intelligence is an emerging technology that studies and develops the theory, technology and application systems for simulating and extending human intelligence, involving disciplines such as psychology, cognitive science, thinking science, information science, systems science and bio science.
In fact, artificial intelligence is the simulation of the process of data interaction of human thinking then produce a smart machine. This intelligent machine can be the same as human thinking to respond and deal with the problem.
Due to advancement in science and technology, conventional mechanical engineering is constantly changing to electronic mechanical engineering.
Artificial intelligence technology is applied under the premise of the development of computer technology, which improved the computer technology through the analysis of it to achieve and realization of intelligent technology. when intelligent technology being applied in mechanical and electrical engineering, it mainly achieved the automation control of mechanical engineering.
Artificial intelligence is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
Example of AI are self driving cars, chess playing by computer etc..
Learning, reasoning and perception are the goal of artificial intelligence.
1. Weak artificial intelligence
2. Strong artificial intelligence
The application of artificial intelligence in mechanical and electrical engineering is not only the use of computer technology but also combined with information technology, psychology, linguistics and other knowledge.
The relationship between artificial intelligence and mechanical engineering
The mechanical engineering has shortcomings such as unstable system, the reason of the problem is the imperfect factor of the electronic information system. Artificial intelligence itself can quickly transfer the information and timely process it, which can effectively make up for this shortcomings.
If the input information is too complex, then in the process of input and output in mechanical engineering, the electronic information system will face a lot of difficulties and resistance. Due to that there will be a chances of mistakes by electronic information system. Then we need to manually solve the problem.
That's why, by combining artificial intelligence and mechanical engineering we can solve the problems and shortcomings of mechanical engineering.
Application of artificial intelligence in mechanical engineering
These days artificial intelligence is generally used in the diagnosis of mechanical engineering failure. Generally, artificial intelligence based fault diagnosis techniques include rule based reasoning (RBR), case based reasoning (CBR) and fault based tree fault diagnosis.
Based on the basic composition and basic principle of the traditional expert system, a mechanical fault diagnosis expert system based on RBR and CBR reasoning is constructed.
The overall shown in above fig. The system includes maneuver case database, fault diagnosis rule database, fault diagnosis database, fault reasoning machine, knowledge processing, fault diagnosis process interpreter, learning system and expert system man machine interface.
The basic working process of the diagnosis system is:
Firstly, the user input the online data monitored by the machine through the man machine interface.
Secondly, the reasoning machine activate the corresponding rules to obtain diagnostics results according to the positive results mechanism. It will provide diagnostic expert advice then retrieve the case in the database through a certain algorithm. Subsequently it gets most similar cases then it calculates the similarity according to the historical case and complete the mechanical fault diagnosis with high efficiency. Finally it will further improve the expert diagnosis system by adding new cases.
Intelligent Diagnostics System For Rotary Machinery
Example- Fan diagnosis system
It is the universal integrated neural network diagnosis system in the fan fault diagnosis application.
The system is composed of two parts: fan and motor. According to the type of monitoring parameters, the main system is divided into five subsystems: vibration, temperature, noise, oil and performance, in which the fault diagnosis and decision system is the core of the whole intelligence system.
Intelligent Diagnosis System For Reciprocating Machinery
Example - Diesel Engine diagnosis system
The reciprocating machinery has a set of high speed reciprocating motion quality, its kinematics and dynamic morphology is much more complex than the rotating machinery. So, fault diagnosis is more difficult.
Diesel engine failure can be divided into performance failure and mechanical failure. The diagnosis of performance faults can be achieved using a sub neural network, with performance parameter as input such as power, speed, cylinder pressure, water temperature and so on. The mechanical fault is diagnosed by two sub neural network, and the integrated neural network diagnosis system is formed by using the commonly used vibro acoustic signal and oil analysis information as input.
Machine learning -
Machine learning mainly focuses on how the computer simulates human learning behavior reorganizes the existing knowledge structure with the knowledge and skills learned and continuously improves its performance. Machine learning is the core of artificial intelligence and it is the only way for computers to have its own intelligence, but can not be used for deductive reasoning.
Expert System -
Expert system explores the general way of thinking into the use of specialized knowledge to solve specific problems.
fig. - Basic structure of expert system
A basic expert system consists of knowledge base and database, reasoning machine, interpretation mechanism, knowledge acquisition and user interface, as shown in above fig.
Deep learning -
Deep learning is a new field of machine learning. Depth learning refers to artificial intelligence beginning to learn, train it, self - master concepts, and recognizes sounds, images and other data from untagged data. This approach is close to the human brain. Deep learning is mainly to built a deep structure to learn multi level representation, not specifically refers to a machine learning algorithm or model but a technology.
First successful AI program was written by Christopher Strachey in 1951.
The first AI program ran in United States in 1952 by Arthur Samuel for the prototype of IBM 701.
excellent
ReplyDeletego ahead
ReplyDeleteVery helpful
ReplyDelete