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Tác giả hoặc Nhóm tác giả: Nguyen Dang Trinh, Duong Quoc Bao, Eric Zamai, Muhammad Kashif Shahzad
Nơi đăng: SAGE journals, Journal of Risk and Reliability; Số: 10.1177/1748006X15623089;Từ->đến trang: **;Năm: 2016
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
Present manufacturing systems are equipped with sensors that provide basis for real time monitoring and diagnosis; however, placement of these sensors is constrained by its structure and the functions they perform. Moreover, sensors cannot be placed across all components in the equipment due to big data challenges. This results in non-observable components that limit our ability to support effective real time monitoring and fault diagnosis initiatives. Consequently,product quality drifts found during inspection often result in unscheduled breakdown of all equipment involved in respective production operation. This situation becomes more complex for automated manufacturing lines, where success depends on our ability to capitalize maximum production capacities. This paper proposes a methodology that exploits historical data over unobserved equipment components to reduce search space of potential faulty components followed by more accurate diagnosis of failures and causes. The purpose is to improve the effectiveness and efficiency of real time monitoring of potential faulty components and causes diagnoses. In the proposed approach, we use Logical Diagnosis approach to reduce the search space of suspected equipment in the production flow, which is then formulated as a Bayesian network. The proposed approach computes the risk priority for suspected equipment with corresponding factors such as human factor and recipe, using joint and conditional probabilities. The objective is to quickly and accurately localize the possible fault origins in real time and support effective corrective maintenance decisions. The key advantages offered by this approach are (i) reduced unscheduled equipment breakdown duration, and (ii) stable production capacities, required for success in highly competitive and automated manufacturing systems. Moreover, this is a generic methodology and can be deployed on fully or semi-automated manufacturing systems.
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