{'fraction_non_alphanumeric': 0.017517136329017517, 'fraction_numerical': 0.002284843869002285, 'mean_word_length': 5.603015075376884, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
PdM: A predictive maintenance modeling tool implemented as R-package and web-application

In the current manufacturing world, maintenance has a critical role to play in improving companies' competitiveness. Among the available maintenance strategies, predictive maintenance seems to be the most promising because failures are predicted and a timely reaction is possible. Therefore, in this paper, we propose the PdM package to build predictive maintenance models for proactive decision support based on machine learning algorithms. The proposed package implemented as a package for R and it provides several major functionalities that attempt to streamline the process for creating predictive maintenance models. The PdM package also provides interactive graphical user interface (web-application), that enables the user to conduct all steps of the predictive maintenance building workflows from his browser without using code. The main aim of the proposed tool is to allow for exploring different machine learning algorithms when solving the problem of remaining useful life prediction for complex multi-component systems. For illustrations, the proposed tool is applied to the Turbofan Engine Degradation Simulation data set FD001 from NASA for the estimation of the turbofan engine remaining useful life (RUL).