What are the five consecutive stages of the intelligent PdM planning model introduced by Mustufa Haider Abidi et al., and what was the purpose of using SVM and RNN in that model?

The five consecutive stages are data cleansing, data normalization, optimal feature selection, precision network decision-making, and prediction. In the model, SVM was used to identify a network that could handle prediction values with different ranges, while RNN was employed to provide predictions throughout a variety of ranges, with the J-SLnO algorithm optimizing the RNN's weights.

Mustufa Haider Abidi et al. introduced an intelligent predictive maintenance planning model consisting of five consecutive stages: data cleansing, data normalization, optimal feature selection, precision network decision-making, and prediction. In their research, after cleaning and normalizing the PdM data, the recommended J-SLnO approach was used for optimal feature selection to reduce redundant data. Because the anticipated values varied and made it difficult for the ML system to generate optimum results, SVM was used to identify a network capable of handling prediction values with different ranges. Then, an RNN was employed to provide predictions across a variety of ranges, and the proposed J-SLnO algorithm was used to optimize the weight of the RNN, forming the J-SLnO-RNN algorithm.