In this paper we propose a low-cost high-speed imaging line scan system. We replace an expensive industrial line scan camera and illumination with a custom-built set-up of cheap off-the-shelf components, yielding a measurement system with comparative quality while costing about 20 times less. We use a low-cost linear (1D) image sensor, cheap optics including a LED-based or LASER-based lighting and an embedded platform to process the images. A step-by-step method to design such a custom high speed imaging system and select proper components is proposed. Simulations allowing to predict the final image quality to be obtained by the set-up has been developed. Finally, we applied our method in a lab, closely representing the reallife cases. Our results shows that our simulations are very accurate and that our low-cost line scan set-up acquired image quality compared to the high-end commercial vision system, for a fraction of the price.
Electrical connection anomalies are widely known as problems in industrial applications. In machinery these can occur in a printed circuit board, for instance in a control unit, in the interfacing connectors, or even in the inter-connections within a sensor or an actuator. Different methods have been proposed in the last decades to detect these anomalies. Most of these methods are working on non-powered systems for instance by measuring reflections of a high speed pulses (reflectometry principle). However, the most challenging connection anomalies have intermittent nature, often in applications where vibration stresses are dominant (e.g. automotive sector). In these cases, detecting these anomalies in a live (powered) system is needed. Although some methods exist which allow such a detection, they are expensive, limiting the targeted applications (e.g. aeronautics). We developed low-cost methods to detect connections anomalies in live systems. These methods use measured signals, for instance across a sensor, not only to detect a fault in its connections but also to localize the position of such a fault. The used ‘diagnostic’ systems will trigger a warning at a very early stage of the connection anomaly. The proposed methods have been applied and validated on different industrial cases and proved to be able to localize within 30 cm accuracy and to detect connection degradation before corrupting the operation of the system.
Quantifying accurate reliability at (sub-)system level is not an easy task. Despite the availability of different tools allowing reliability estimation, e.g. reliability handbooks as MIL217-F, the accuracy of the obtained results is not guaranteed. For instance, the data used in these handbooks are outdated, referring to old technologies and assuming stresses that are not always realistic. Other methods exist which should allow a more accurate reliability estimation e.g. the physics of failure prognostics. However, for an industrial end user, following such an approach at (sub) system level is too expensive. Typical steps to obtain reliability data of one component following physics of failure prognostic approach would require (i) understanding a given failure mechanism and developing its corresponding physics of failure model, (ii) identifying stress accelerators of this failure mechanism, and (iii) planning and implementing an accelerated life test to collect failure data in order to validate the model. A typical accelerated life test would require failures of components collected during the test time (in the order of months) at different stress levels. Another approach to get more accurate reliability at (sub-)system level is collecting and analyzing field data. However, this would require a complete process within an organization, by tracking the products in the field and collecting failure information for many years.In order to overcome these limitations for companies, we propose a methodology allowing to obtain quick and accurate estimations of the (sub-) system reliability by combiningcomponent’s reliability information from different sources, e.g. using physics-of-failure models for some critical components where test data are historically available, and / or using reliability prediction handbook for proven in use components, and / or using field data if available.
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