Rotary machine’s fault diagnosis and prognosis using artificial intelligence
| dc.contributor.author | Khencha Mohammed Said, Boudiar Achraf | |
| dc.date.accessioned | 2025-03-12T08:58:42Z | |
| dc.date.available | 2025-03-12T08:58:42Z | |
| dc.date.issued | 2019-06-23 | |
| dc.description.abstract | A neural network is a type of machine-learning algorithm that is modelled on the functioning of the brain. Back in 1943, two neuroscientists by the names of Warren McCulloch and Walter Pitts wrote a paper called “A Logical Calculus of the Ideas Imminent in Nervous Activity”. They expressed the functioning of neurons in the brain mathematically, fundamentally arriving at the “all or nothing principle” which expresses neuron activity in a very binary way – if the message from all the input neurons is sufficient, a neuron fires. If it is insufficient, it does not. This binary on/off behavior didn’t take long to make its way to computer scientists who saw obvious connections to the same “on/off” behavior of bits in the machine. Frank Rosenblatt ran with this “mathematical brain” concept and introduced the Artificial Neural Network, the “Perceptron” in his paper in 1958. The evolvement of artificial intelligence, machine learning, and deep learning has made so many people start asking questions about what exactly the process of machine learning is? We found that data scientists, enthusiasts, and developers are very curious to learn how a neural network works for helping artificial intelligence to perform better. An artificial neural network is like a biological neural network in a brain. A biological neural network works as follows: information flows in, is processed by the neurons, and the results flow out. [1] The basis of the neuron is to react to previously learned patterns. When we are creating the same kind of replication in terms of technology and computer science, we call it an artificial neural network. Just like the biological neuron, information flows in, is processed by an artificial neural network, and results flow out. | |
| dc.identifier.uri | http://dspace.univ-skikda.dz:4000/handle/123456789/4365 | |
| dc.language.iso | en | |
| dc.publisher | UNIVERSITY OF 20 AUGUST 1955 OF SKIKDA | |
| dc.title | Rotary machine’s fault diagnosis and prognosis using artificial intelligence | |
| dc.type | Thesis |