Fault detection concerns with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location.
Industrial systems are increasingly equipped with sensors that collect information about the items that they produce.
Being able to manage all this information to implement solutions in real time allows industry to increase benefits through a more accurate maintenance process.
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.
Detect whether an item is working well or not by comparing the data received from it with the historical data.
Identify the causes of fault. This process should consider trends in health history and operational context.
Predict the state of the item in the future to detect any possible fault in advance.
Elaborate maintenance plans taking into account the previous predictions to reduce fault.
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 classification model based on the information provided by a dataset that contains fault and correct items.
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.