SP4.2.8 Does Care quality commission need to change its inspection strategy after COVID-19?

Aim To see if the Care quality commission’s (CQC) overall inspection ratings and specific domain ratings had any association with COVID-19 related deaths. Methods We looked at CQC ratings of the Shelford group of trusts and 10 trusts with maximum number of deaths in the time period from the first wave till 23rd January 2021. We then looked at each of the 5 domains: safe, effective, caring, responsive, and well led, to see if they were in any way indicative of the number of deaths in that trust. Results Among the 19 trusts studied (one trust featured in both groups) only 1 had an overall outstanding rating; 10 were rated good, 8 required improvement. None were rated inadequate in any of the domains, 16 /19 were rated good in effective domain which means “there care, treatment and support achieves good outcomes”. In safe domain 13/ the 19 were rated as requires improvement which means that “a patient may not be protected from avoidable harm”. Conclusion COVID-19 is no respecter of the CQC ratings, even if the trust was rated as well led and had good ratings in all other domains it could still have the highest mortality figures from COVID in the UK. CQC probably needs to add a 6th domain to its future inspections to assess the readiness the trusts for future Pandemics.

Maintenance cost assessment for heterogeneous multi-component systems incorporating perfect inspections and waiting time to maintenance

Most existing research about complex systems maintenance assumes they consist of the same type of components. However, systems can be assembled with heterogeneous components (for example degrading and non-degrading components) that require different maintenance actions. Since industrial systems become more and more complex, more research about the maintenance of systems with heterogeneous components is needed. For this reason, in this paper, a system consisting of two groups of components: degrading and non-degrading components is analyzed. The main novelty of this paper is the evaluation of a maintenance policy at system-level coordinating condition-based maintenance for the degrading components, delay time to the maintenance and an inspection strategy for this heterogeneous system. To that end, an analytic cost model is built using the semi-regenerative processes theory. Furthermore, a safety constraint related to the reliability of the degrading components is imposed. To find the optimal maintenance strategy, meta-heuristic algorithms are used.

Detection and Classification of Defective Hard Candies Based on Image Processing and Convolutional Neural Networks

Defective hard candies are usually produced due to inadequate feeding or insufficient cooling during the candy production process. The human-based inspection strategy needs to be brought up to date with the rapid developments in the confectionery industry. In this paper, a detection and classification method for defective hard candies based on convolutional neural networks (CNNs) is proposed. First, the threshold_li method is used to distinguish between hard candy and background. Second, a segmentation algorithm based on concave point detection and ellipse fitting is used to split the adhesive hard candies. Finally, a classification model based on CNNs is constructed for defective hard candies. According to the types of defective hard candies, 2552 hard candies samples were collected; 70% were used for model training, 15% were used for validation, and 15% were used for testing. Defective hard candy classification models based on CNNs (Alexnet, Googlenet, VGG16, Resnet-18, Resnet34, Resnet50, MobileNetV2, and MnasNet0_5) were constructed and tested. The results show that the classification performances of these deep learning models are similar except MnasNet0_5 with the classification accuracy of 84.28%, and the Resnet50-based classification model is the best (98.71%). This research has certain theoretical reference significance for the intelligent classification of granular products.

InspectTM Computed Tomography for NDT of Subsea Pipelines

Using Computed Tomography (CT) for 3D spatial analysis is a well-established technique primarily used in the fields of medical imaging and precision defect analysis of materials such as aircraft CT technology is gradually evolving for asset integrity within the Oil and Gas sector for two main applications: in-line inspection (ILI) verification and non-intrusive inspection (NII) of coated subsea pipelines. The CT scanning philosophy for both types of inspection challenges varies significantly and in this paper is considered in terms of alignment of inspection deliverables with scan definition. This paper explores the redesign of the traditional CT approach to optimize scanning for high-density target volumes by implementing a ground-up approach to all hardware and mathematics. The advanced algorithms used in the InspeCTTM technology enables a novel technique for image reconstruction; allowing for cleaner images than are possible using traditional CT whilst using less data and hence smaller radioactive sources. In addition, this paper demonstrates a selection of important findings from the InspeCTTM system qualification to API Standard 1163. To maximize the effectiveness of the subsea campaign, use of automated analysis is demonstrated to provide consistency in interpretation and reduce the considerable time demands of high-volume data analysis enabling any remediation decisions or change to inspection strategy to be actioned at the time of inspection rather on a future campaign.

Inspection planning by defect prediction models and inspection strategy maps

Designing appropriate quality-inspections in manufacturing processes has always been a challenge to maintain competitiveness in the market. Recent studies have been focused on the design of appropriate in-process inspection strategies for assembly processes based on probabilistic models. Despite this general interest, a practical tool allowing for the assessment of the adequacy of alternative inspection strategies is still lacking. This paper proposes a general framework to assess the effectiveness and cost of inspection strategies. In detail, defect probabilities obtained by prediction models and inspection variables are combined to define a pair of indicators for developing an inspection strategy map. Such a map acts as an analysis tool, enabling positioning assessment and benchmarking of the strategies adopted by manufacturing companies, but also as a design tool to achieve the desired targets. The approach can assist designers of manufacturing processes, and particularly low-volume productions, in the early stages of inspection planning.