Preload Loss Detection in a Ball Screw System Using Interacting Models

Ball screw preload is an important factor in maintaining repeatably, rigidity, and in reducing or eliminating backlash in feed drive systems. Ball screw feeds drives are used in computer numerical control (CNC) machine tools to manufacture high-quality, precision parts. Many fault detection and condition monitoring (CM) methods have been proposed for measuring and detecting loss of preload, however, most of these methods require external sensors. Ideally, sensors, measurements, and methods integral to a CNC machine tool could be used to eliminate the extra cost and complexity of external sensors. A sensor-less method of estimating levels of preload using the mode probability of interacting multiple models (IMMs) is proposed. This method calculates a weighted sum which utilizes the mode probability of models representing different levels of preload, along with an activation function and weighing factor, to estimate the current level of preload. Unlike many other methods used for detecting levels of preload, this method requires only a system model and data collected by the CNC systems, while requiring no external sensors. The proposed method was shown to be robust and able to accurately and quickly predict preload levels under many different testing conditions. This method demonstrated a high degree of prediction accuracy (95%) which is comparable to, or better than other methods in the literature. In addition to being a novel method for preload detection, this work is also a novel implementation of IMM for fault detection, as it has not yet been applied to fault detection in feed drives

A CNN-based strategy to automate contour detection of the hip and proximal femur using DXA hip images from longitudinal databases (CLSA and CaMos)

Hip fractures contribute significantly to mortality in older adults. New methods to identify those at risk use dual-energy X-ray absorptiometry (DXA) images and advanced image processing. However, DXA images have an overlapping femur and pelvis and may contain boundary lines, making automation challenging. Herein, a 5-layer U-net convolutional neural network (CNN) was developed to segment the femur from hip DXA images. Images were used from the Canadian Longitudinal Study on Aging (CLSA, N=104) and Canadian Multicentre Osteoporosis Study (CaMos, N=105) databases for training, with manual contour drawing defining the ‘true output’ of each image. An algorithm was then developed to mask each hip image, and trained to predict subsequent masks. The CNN was tested with 44 additional CLSA images and 42 CaMos images. This proposed approach had an accuracy and intersection over union (IoU) of 97% and 0.57, and 93% and 0.51, for CaMos and CLSA scans, respectively. Furthermore, a series of augmentation techniques was applied to increase the data size, with accuracies of 96% and 94%, and IoU of 0.53 and 0.50. Overall, our strategy automatically determined the contour of the proximal femur using various clinical DXA images, a key step to automate fracture risk assessment in clinical practice

The conservation status of the world’s reptiles

Effective and targeted conservation action requires detailed information about species, their distribution, systematics and ecology as well as the distribution of threat processes which affect them. Knowledge of reptilian diversity remains surprisingly disparate, and innovative means of gaining rapid insight into the status of reptiles are needed in order to highlight urgent conservation cases and inform environmental policy with appropriate biodiversity information in a timely manner. We present the first ever global analysis of extinction risk in reptiles, based on a random representative sample of 1500 species (16% of all currently known species). To our knowledge, our results provide the first analysis of the global conservation status and distribution patterns of reptiles and the threats affecting them, highlighting conservation priorities and knowledge gaps which need to be addressed urgently to ensure the continued survival of the world’s reptiles. Nearly one in five reptilian species are threatened with extinction, with another one in five species classed as Data Deficient. The proportion of threatened reptile species is highest in freshwater environments, tropical regions and on oceanic islands, while data deficiency was highest in tropical areas, such as Central Africa and Southeast Asia, and among fossorial reptiles. Our results emphasise the need for research attention to be focussed on tropical areas which are experiencing the most dramatic rates of habitat loss, on fossorial reptiles for which there is a chronic lack of data, and on certain taxa such as snakes for which extinction risk may currently be underestimated due to lack of population information. Conservation actions specifically need to mitigate the effects of human-induced habitat loss and harvesting, which are the predominant threats to reptiles