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Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance interventions before faults occur. In the recent literature, several data-driven methods for RUL prediction have been proposed. However, most of them are based on traditional (connectivist) neural networks, such as convolutional neural networks, and alternative mechanisms have barely been explored. Here, we tackle the RUL prediction problem for the first time by using a membrane computing paradigm, namely that of Spiking Neural P (in short, SN P) systems. First, we show how SN P systems can be adapted to handle the RUL prediction problem. Then, we propose the use of a neuro-evolutionary algorithm to optimize the structure and parameters of the SN P systems. Our results on two datasets, namely the CMAPSS and new CMAPSS benchmarks from NASA, are fairly comparable with those obtained by much more complex deep networks, showing a reasonable compromise between performance and number of trainable parameters, which in turn correlates with memory consumption and computing time.

Membrane computing is a discipline that aims to perform computation by mimicking nature at the cellular level. Spiking Neural P (in short, SN P) systems are a subset of membrane computing methodologies that combine spiking neurons with membrane computing techniques, where "P" means that the system is intrinsically parallel. While these methodologies are very powerful, being able to simulate a Turing machine with only few neurons, their design is time-consuming and it can only be handled by experts in the field, that have an in-depth knowledge of such systems. In this work, we use the Neuroevolution of Augmenting Topologies (NEAT) algorithm, usually employed to evolve multi-layer perceptrons and recurrent neural networks, to evolve SN P systems. Unlike existing approaches for the automatic design of SN P systems, NEAT provides high flexibility in the type of SN P systems, removing the need to specify a great part of the system. To test the proposed method, we evolve Spiking Neural P systems as policies for two classic control tasks from OpenAI Gym. The experimental results show that our method is able to generate efficient (yet extremely simple) Spiking Neural P systems that can solve the two tasks. A further analysis shows that the evolved systems act on the environment by performing a kind of "if-then-else" reasoning.