Fault detection concerns with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location.
This is always a big concern in industry production.
Early discovery of system faults may ensure the reliability and safety of industrial systems and reduce the risk of unplanned breakdowns.
Usual fault detection algorithms have limitations due to the growth of complexity of modern systems and the amount of information that is collected.
As industrial systems are increasingly equipped with a lot of sensors that collect information about the state of the items, being able to manage all this information to implement solutions in real time allows industry to increase benefits through a more accurate maintenance process.
Neural networks, due their capacity to deal with large amount of real-time information and to model complex relations between different variables, are a strong tool to find any anomaly that may arise.
Neural Designer uses machine learning to build a predictive model based on the information provided by a dataset that contains the historical functioning of the industrial systems.
Neural Designer can establish which are the variables that characterize a fault, study the dataset attending to a set of statistical indicators, design and select the parameters of the predictive model, train it and check its prediction capacity. Finally, it also allows to export the mathematical expression that defines the trained model to use it in real cases.
The next image shows a representation of a neural network that could be used for this case.
As inputs, it receives information about the functioning of the system, environmental variables and other external variables that may be considered important for the analysis. + As output, the neural network responses with the most likely fault.