Supervised learning-based collaborative filtering using market basket data for the cold-start problem

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Estimation on Reliability Models of Bearing Failure Data

The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and k-s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter log-normal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the three-parameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present

Shifting artificial data to detect system failures

Multivariate statistical process control (MSPC) is used for simultaneously monitoring several process variables. While small changes to normal operating conditions made by this system may not seriously affect the quality of a product, a system failure will be declared if an observation significantly deviates from the in-control region before defective units are mass-produced. Although a number of research works integrating data-mining algorithms with MSPC have been proposed to effectively manage a large amount of data, this combination may not function for the case of system failures due to the extreme imbalance of data. This research proposes a new approach and employs a classification technique, namely, random forest, which overcomes the class imbalance problem. The proposed method systematically shifts artificial data toward the region of failures to ensure the classifier correctly detects system failures. Numerical experiments show that our method outperforms existing methods in terms of failure detection counts.Ministry of Education, Science and Technology. Grant Number: 2012R1A1A1012153Wiley Online Librar