The use of simulation models to generate data corresponding to faults in machines

There are a number of benefits from simulating faults in machines, the first being to be able to produce sufficient representative signals to train automated fault recognition algorithms such as artificial neural networks, as it is not economically viable to experience the number of actual failures required. A second is to able to produce signals from faults of different sizes and locations in order to test and compare diagnostic algorithms and to develop diagnostic and prognostic procedures and features. Finally, simulations can be very helpful in gaining a physical understanding of the complex (often nonlinear) interactions which sometimes arise in machines.