The development of sensor technology provides massive data for data-driven fault diagnosis. In recent years, more and more scholars are studying artificial intelligence technology to solve the bottleneck in fault diagnosis. Compared with other classification and prediction problems, fault diagnosis often faces the problem of data scarcity. To overcome the lack of fault data, the transfer learning based on different working condition is gradually introduced into fault diagnosis by scholars. This paper discusses the current mainstream AI-based fault diagnosis methods, and analyzes the advantage of transfer learning for fault diagnosis problem. Then, a transfer component analysis (TCA) based method is proposed to transfer data features between different working conditions. Through the TCA-based method, the fault diagnosis model under the working condition can be established with the help of historical working condition. It effectively alleviates the problem of data scarcity under the condition to be predicted. Different from other fault diagnosis studies, this paper considers the online maintenance process based on TCA. A fault diagnosis framework including online maintenance process is proposed. Finally, a case study of bearing diagnosis from Case Western Reserve University proves the feasibility and effectiveness of the proposed TCA-based method and our fault diagnosis framework.
During the real production system, the scheduling scheme change is mostly changed by dynamic events or new tasks. Due to the different urgency degrees of dynamic events, the corresponding scheduling methods should be adopted to ensure the production efficiency of enterprises. In this paper, an event-driven dynamic workshop scheduling model is established based on Ant Colony System (ACS), and two scheduling methods are designed to deal with dynamic events, namely parallel scheduling and parallel priority scheduling, respectively. The goal of parallel scheduling is to minimize the total makespan, while that of parallel priority scheduling is to minimize the delivery time of dynamic events. Additionally, a selective scheduling strategy is designed to determine the optimal scheduling method according to the urgency degree of dynamic events. Finally, the feasibility of the selective scheduling strategy in solving the dual-objective dynamic job shop scheduling problem (DJSP) is verified by an example experiment on DJSP as well as a large scale problem test set.INDEX TERMS Job shop scheduling, ant colony system, dynamic scheduling, event-driven.
We give a concise tutorial on knowledge discovery with linear mixed model in movie recommendation. The versatility of mixed effects model is well explained. Commonly used methods for parameter estimation, confidence interval estimate and evaluation criteria for model selection are briefly reviewed. Mixed effects models produce sound inference based on a series of rigorous analysis. In particular, we analyze millions of movie rating data with LME4 R package and find solid evidences for a general social behavior: the young tend to be more censorious than senior people when evaluating the same object. Such a social behavior phenomenon can be used in recommender systems and business data analysis. INDEX TERMS Knowledge discovery in database (KDD), linear mixed-effects model (LMM), recommender system (RS), R software.
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