An ultra-high-stability four-axis ultra-high-vacuum sample manipulator

A report on a four-axis ultra-high-stability manipulator developed for use at the Veritas and Species RIXS beamlines at MAX IV Laboratory, Lund, Sweden, is presented. The manipulator consists of a compact, light-weight X–Y table with a stiffened Z tower carrying a platform with a rotary seal to which a manipulator rod holding the sample can be attached. Its design parameters have been optimized to achieve high eigen-frequencies via a light-weight yet stiff construction, to absorb forces without deformations, provide a low center of gravity, and have a compact footprint without compromising access to the manipulator rod. The manipulator system can house a multitude of different, easily exchangeable, manipulator rods that can be tailor-made for specific experimental requirements without having to rebuild the entire sample positioning system. It is shown that the manipulator has its lowest eigen-frequency at 48.5 Hz and that long-term stability is in the few tens of nanometres. Position accuracy is shown to be better than 100 nm. Angular accuracy is in the 500 nrad range with a long-term stability of a few hundred nanoradians.

Friction Torque and Leakage Based Data-Driven Approach for Rotary Seal Prognostics in Manufacturing Industry

Rotary seals are found in many manufacturing equipment and machines used for various applications under a wide range of operating conditions. Rotary seal failure can be catastrophic and can lead to costly downtime and large expenses; so it is extremely important to assess the degradation of rotary seal to avoid fatal breakdown of machineries. Physics-based rotary seal prognostics require direct estimation of different physical parameters to assess the degradation of seals. Data-driven prognostics utilizing sensor technology and computational capabilities can aid in the in-direct estimation of rotary seals’ running condition unlike the physics-based approach. An important aspect of data-driven prognostics is to collect appropriate data in order to reduce the cost and time associated with the data collection, storage and computation. Seals in machineries operate in harsh conditions, especially in the oil field, seals are exposed to harsh environment and aggressive fluids which gradually reduces the elastic modulus and hardness of seals, resulting in lower friction torque and excessive leakage. Therefore, in this study we implement a data-driven prognostics approach which utilizes friction torque and leakage signals along with Multilayer Perceptron as a classifier to compare the performance of the two metrics in classifying the running condition of rotary seals. Friction torque was found to have a better performance than leakage in terms of differentiating the running condition of rotary seals throughout its service life. Although this approach was designed for seals in oil and gas industry, this approach can be implemented in any manufacturing industry with similar applications.

A Data-Driven, Statistical Feature-Based, Neural Network Method for Rotary Seal Prognostics

Failure of the rotary seal is one of the foremost causes of breakdown in rotary machinery, and such a failure can be catastrophic, resulting in costly downtime and large expenses. Assessing the performance degradation of the rotary seal is very important for maintenance decision-making. Although significant progress has been made over the last 5 years to understand the degradation of seals using experimental verification and numerical simulation, there is a research gap on the data-driven-based tools and methods to assess the health condition of rotary seals. In this paper, we propose a data-driven-based performance degradation assessment approach to classify the running/health condition of rotary seals, which was not considered in the previous studies. Statistical time domain features are extracted from friction torque-based degradation signals collected from a rotary setup. Wrapper-based feature selection approach is used to select relevant features, with multilayer perceptron neural network utilized as a classification technique. To validate the proposed methodology, an accelerated aging and testing procedure is developed to capture the performance of rotary seals. The study findings indicate that multilayer perceptron (MLP) classifier built using features related to the amplitude of torque signal has a better classification accuracy for unseen data when compared with logistic regression and random forest classifiers.