Application of Feature Selection Method to Error Factor Extraction of Multifunction Peripheral Multifunction peripheral (MFP) manufacturers provide customers with remote maintenance services, such as supplies provision and automatic firmware updates, to lower customer burdens and to avoid device downtime. Such remote services are required for maintenance so that Japanese machine manufacturers can deliver products to foreign markets, because service bases in overseas locales must cover broader geographical areas than those in Japan. When MFP devices experience a fault, they generally alert users of an error. Although some faults can be solved remotely, there are faults that require an engineer to perform onsite actions. To repair them onsite efficiently, online investigation and preassessment of fault factors will be effective. In this paper, we apply the Group Lasso regularization method for logistic regression to select features determined as error factors. We evaluate the engine on two kinds of error examples: those frequently causing alerts in MFP models in the past, and those causing alerts due to part wear. This engine is expected to help engineers determine causal factors of errors.

Analysis of the applicability of fault detection and failure prediction based on unsupervised learning and Monte Carlo simulations for real devices in the industrial automobile production Machine learning and statisticalbased methods have been widely used to estimate the Remaining Useful Life of devices. However, in most of the cases, the transferability of these methods to production environments is complicated. The majority of these algorithms have been tested by using simulated data, or by the construction of testbeds. The complication is generated by some real conditions in such environments which are not considered during the algorithm development. This research addresses those drawbacks by considering real data delivered by a production device in order to construct the right condition monitoring parameter. This parameter will be obtained by analyzing the correlation among available data, and failure on the critical element on the machine. Moreover, the benefits and drawbacks of several datadriven approaches are analyzed, and a method is proposed based on the combination of the best properties of each one.