Purpose
The purpose of this paper is to analyze the changes in its importance due to the maintenance and repair of components.
Design/methodology/approach
In this paper, a concept of time-varying importance measure is proposed to solve the problem of component importance change caused by maintenance. When the system is broken-down, the probability difference between the component works well after repairing and the component break down before repairing is solved, this difference is measured as an index of time-varying importance method. Then, the approach has been verified by the CNC machine tool.
Findings
The paper provides a method to analyze the importance of changes of components in the system due to maintenance. The time-varying importance measure can evaluate the component importance anytime during its whole life span, and it has the ability to find out the most responsible component for a system failure in the actual production process. What is more, it provides guidance for the next maintenance work.
Originality/value
The proposed method can guide the next maintenance time according to the change of component performance caused by each maintenance activity of the manufacturing system, and avoid the waste of resources caused by repeated maintenance.
Welding defect detection in a radiographic image is an important topic in the field of industrial non‐destructive testing. To improve the accuracy of welding defect segmentation, a local image enhancement approach is proposed. In this algorithm, the requirement of contrast enhancement is considered when extracting the weld seam and segmenting the weld defect. The whole defect detection is conducted by three procedures: image enhancement, welding seam extraction, and defect segmentation. Firstly, a method for determining the Localised Pixel Inhomogeneity Factor (LPIF) is proposed. Then, based on the results of LPIF, the Otsu method is applied to segment the welding seam and defects are, identified by region growing algorithm. The authors compared LPIF with histogram equalisation, adaptive histogram equalisation, and contrast‐limited adaptive histogram equalisation algorithms and assessed its performance by using indicators such as image contrast, image definition, and edge intensity. Moreover, the authors compared the segmentation results of the enhanced defect images with the original image to further study the method's effect on weld defect segmentation. More than 70 images containing various types of defects are tested. The experimental results demonstrate that the quality of enhanced defect images is improved significantly, and has a high relative segmentation accuracy of more than 92%.
Importance analysis deals with the investigation of influence of individual system component on system operation. This paper mainly focuses on dynamic important analysis of components in a multistate system. Assuming that failure probabilities of system components are independent, a new time integral-based importance measure approach (TIIM) is proposed to measure the loss of system performance that is caused by each individual component. Reversely the importance of a component can be evaluated according to the magnitude of performance loss of the system caused by it. Moreover, the dynamic varying curve of importance of a component with time can be described by calculating criticality of the component at each state. On the other hand, in the proposed approach, the importance probability curve of a component is fitted by using the failure data from all components of system excluding that of the component itself so as to solve the problem of inaccurate fitting caused by small sample data. The approach has been verified by probability analysis of failure data of CNC machines.
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