Due to the intricacy of white-box models, black-box models are becoming increasingly popular due to their simplicity and speed. Rather than being based on fundamental physical equations, black-box models are derived from experimental datasets and diagnose faults via machine learning algorithms, which classify the experimental data into different health states. However, this limits their genericity compared to their white-box counterparts, whose model parameters can be easily adjusted to reflect other PEFCs; instead, black-box models require a large historical dataset from the exact/similar PEFC to function effectively. One of the most widely used black-box models is the artificial neural network (ANN), consisting of nodes or 'neurons' organised into layers with the nonlinear activation function, through which information can pass and be classified .