{'fraction_non_alphanumeric': 0.008394543546694649, 'fraction_numerical': 0.0, 'mean_word_length': 5.534246575342466, '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}}
An Intelligent Predictive Engine for Milling Machine Prognostic Monitoring

This paper presents an intelligent predictive engine (IPE) for applications in equipment prognostic monitoring and failure prediction. The IPE is designed and developed with embedded data handling and analysis tools based on multiple regression models and artificial neural networks. A case study for milling machine tool remaining useful lifetime prediction is presented to demonstrate the usability of the IPE in tooling industry. A comparison is made in the case study on the prediction performances of the different models established with the same set of experimental data. Back propagation neural network shows clear better performance over the others for solving the prognostic problem of tool life prediction on the milling machine. The algorithms in the IPE are generic and can be adopted for different application scenarios that require equipment prognostic analysis.