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Generic Data Imputation and Feature Extraction for Signals from Multifunctional Printers

Printer devices evolved into complex machines from both a hardware and software perspective. Nowadays, they are multifunctional and generate a variety of signals. The signals are mainly utilized for post-fact diagnosis of a printer. There is a great opportunity to employ the signals as inputs to decision support systems. Commonly, a decision support system requires a specific format and characteristic for the input data. In this paper, we analyze the characteristics of data generated by Multifunctional Printers, and compare and propose an optimal approach to handle missing signal values. To the best of our knowledge, this is the very first study that prepares a structured dataset for multifunctional printer signals. The proposed approach has been examined on a real-world dataset of signals of printers from Konica Minolta Inc.