Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases.

What GPs do and what gets in their way: a mixed-methods study

Background: Though problems that impair task completion – known as operational failures – are an important focus of concern in primary care, they have remained little studied. Aim: To quantify the time general practitioners (GPs) spend on different activities during clinical sessions; to identify the number of operational failures they encountered; and to characterise the nature of operational failures and their impacts for GPs. Design and setting: Mixed-method triangulation study with 61 GPs in 28 NHS general practices. Method: Time-motion methods, ethnographic observations and interviews. Results: Time-motion data on 7679 GP tasks during 238 hours of practice in 61 clinical sessions suggested that operational failures were responsible for around 5.0% (confidence interval 4.5 to 5.4) of all tasks undertaken by GPs and accounted for 3.9% (3.2 to 4.5) of clinical time. However, qualitative data showed that time-motion methods, which depend on pre-programmed categories, substantially underestimated operational failures. Qualitative data also enabled further characterisation of operational failures, extending beyond those measured directly in the time-motion data (eg, interruptions, deficits in equipment/supplies and technology) to include problems linked to GPs’ coordination role and weaknesses in work systems and processes. The impacts of operational failures were highly consequential for GPs’ experiences of work. Conclusion: GPs experience frequent operational failures, disrupting patient care, impairing experiences of work, and imposing burden in an already pressurised system. Better understanding of the nature and impact of operational failures allows identification of targets for improvement and indicates the need for coordinated action to support GPs.

Analysing User Experience of Mobile Banking Applications in Nigeria: A Text Mining Approach

This paper analyses textual data mined from 37,460 reviews written by mobile banking application users in Nigeria over the period November 2012 – July 2020. On a scale of 1 to 5 (5 being the best), the average user rating for the twenty-two apps included in our sample is 3.5; with the apps deployed by non-interest banks having the highest average rating of 4.0 and those by commercial banks with national authorisation having the least rating of 3.4. Results from the sentiment analysis reveal that the share of positive sentiment words (17.8%) in the corpus more than double that of negative sentiment words (7.7%). Furthermore, we find that about 66 per cent of the emotions expressed by the users are associated with ‘trust’, ‘anticipation’, and ‘joy’ while the remaining 34 per cent relate to ‘surprise’, ‘fear’, ‘anger’, and ‘disgust’. These results imply that majority of the users are satisfied with their mobile banking experience. Finally, we find that the main topics contained in the user reviews pertain to (i) feedback on banks’ responsiveness to user complaints (ii) user experience regarding app functionalities and updates, and (iii) operational failures associated with the use of the apps. These results highlight the need for banks to continue to promote awareness of existing functionalities on their apps, educate users on how those solutions could be accessed, and respond to user feedback in a timely and effective manner.

Studying the Normal Operation of Grain Harvesters within the Warranty Period

Introduction. Grain harvesters are used for no more than two months within a year. They must have maximum operating reliability, since even short downtime during the harvesting period result in large crop losses. The purpose of the study is to identify the causes of combine harvester failures within the warranty period. Materials and Methods. Identifying consequences of failures and ensuring the reliability of grain harvesters are based on an improved classification of failures. In the process of studying, there have been proposed the ways to solve the problem of combine harvester downtime based on the analysis of the time for grain harvester troubleshooting. The category of severity of failure consequences was taken into account. Results. Through monitoring in the period from 2018 to 2020, there were found failures of units and systems of grain harvesters with low reliability indexes within the warranty period. Most of the failures (59.2%) were found in Russian-manufactured combines, of which operational failures are 55.9%, structural failures – 26.7%, and production failures – 17.4%. The general patterns of changes in the average time for combine troubleshooting have been determined. A geometric model of a detail for the trouble-free operation of combines (header auger shaft) was created. The finite element analysis (ANSYS) was used to identify parts, which are subject to maximum workload. There have been identified units and parts, which fail to function within the warranty period, because of design and technological defects. Discussion and Conclusion. In order to reduce the time to find the consequences of failures, it is necessary to create a more extensive network of enterprises providing a wide range of services, improve the organization of technical service and expand direct links with the manufacturers of equipment in order to respond quickly and make the necessary design and technological decisions.

Anomalies Detection in records of operational failures using IoT devices and data mining

The industry underwent several transformations initiated by the first Industrial Revolution at the end of the 18th century. Today we are experiencing the Fourth Industrial Revolution, where equipment is capable of processing data and connect to communication networks. Maintenance planning can use large volume of data generated by IoT devices to act preventively . This work aims to propose an architecture that uses an outlier detection algorithm, Local Outlier Factor, to detect anomalies in machine failure records, producing information to support equipment maintenance decisions.

METHODOLOGICAL APPROACH TO ASSESSMENT OF COMBAT DAMAGE ELIMINATION EFFECTIVENESS AND RECOVERY OF OPERATIOANAL CAPABILITIES OF AIR DEFENSE SYSTEMS MEANS OF COMBAT IN COUNTEROFFENSIVE OPERATION

The operational use of armament and military equipment (AME) and the resulting operational failures and combat damage lead to a decrease or loss of combat capability of groupings of troops. That is why the timely and high– quality implementation of a set of measures to keep the AME in operational conditions is connected with the need of solving the problem of logistical support for the timely elimination of failures and combat damage. At the same time, the effectiveness of the air defense systems recovery, taking into account the type of operation being conducted and the existing resource constraints requires a more detailed study. The aim of the article is to form a methodical approach to evaluate the effectiveness of repairing combat damage and restoring the combat effectiveness of air defense systems in a counteroffensive operation in modern conditions. Means of destruction that can be employed to inflict air defense systems in counteroffensive operation are considered. Modeling of the enemy’s use of different types of means of destruction was carried out, the obtained results substantiated the damage to the air defense systems’ means of combat, which can be sustained in a counteroffensive operation. In addition, the labor costs for recovery were calculated. Using the method of assessing the possibilities of combat damage eliminating and restoring the effectiveness of AME specimen, calculations of the recovery effectiveness of air defense system’s means of combat were performed. As indicators that characterize the capabilities of repair and restoration units to eliminate combat damage and restore the operational performance of AME, the average duration of AME specimen restoration and the probability of timely repairs, taking into account resource constraints are used. An analysis of the calculations results of the probability of timely recovery of air defense system was performed. The maximum values of labor costs for restoration and characteristics of repair and restoration units are given. The method of calculating the effectiveness of combat damage eliminating and restoring the combat effectiveness of air defense system in a counteroffensive operation under resource constraints was further developed.

Quantification and Analysis of Uncertainties in Parameters of Rotors Supported by Bearings

Rotating unbalance is one of the most important critical parameters that causes operational failures of rotating machinery. The uneven distribution of mass on the structure of the rotors creates heavy spots, which must be eliminated to avoid generating excessive stress on the rotor bearings. The main objective of this work is to perform the uncertainty analysis on rotating machine systems supported with rolling bearings. A computational procedure is implemented to achieve this objective that can qualitatively represent the main behaviors and parameters of rotating machines. Further, methods of uncertainty quantification are applied to verify the behavior of the system given the probability density functions of the input parameters. One of the most commonly used methods is the Monte Carlo method, which requires thousands of simulations to produce accurate results. This method is used to obtain means and standard deviations of the system responses, given the means and standard deviations of the inputs. In our work, Monte Carlo simulation has been successfully used as a reference stochastic solver to evaluating the variability of the dynamic responses.

Early Detection and Recovery Measures for Smart Grid Cyber-Resilience

The internet of things (IoT) has recently brought major technological advances in many domains, including the smart grid. Despite the simplicity and efficiency that IoT brings, there are also underlying risks that are slowing down its adoption. These risks are caused by the presence of legacy systems inside existing infrastructures that were built with no security in mind. In this chapter, the authors propose a method for early-stage detection of cyber-security incidents and protection against them through applicable security measures. This chapter introduces security techniques such as anomaly detection, threat investigation through a highly automated decision support system (DSS), as well as incident response and recovery for smart grid systems. The introduced framework can be applied to industrial environments such as cyber-threats targeting the production generator as well as the electricity smart meters, etc. The chapter also illustrates the framework's cyber-resilience against zero-day threats and its ability to distinguish between operational failures as well as cyber-security incidents.

Methodology for analyzing reliability of the equipment of electric locomotives 2ES10

The article deals with the analysis of reliability of the equipment of 2ES10 electric locomotives with asynchronous traction motors as a complex system and equipment failures that are often encountered during operation. All submitted failures are divided by type of equipment. Based on the Pareto analysis, the main limiting units of these electric locomotives with the lowest reliability are determined. Based on the Pareto analysis, the frequently failing elements of the main limiting components are determined, which are the mechanical part, wheel pairs, brake equipment, traction drive and traction motors. These elements have a major impact on the safety of train traffic and therefore it is extremely important to constantly assess the condition of these elements. Attention is focused on the analysis of risks associated with the possible formation of operational failures, the system of planned preventive maintenance, methods and tools for assessing reliability, incorrect regulatory and documentation support and fixing operational defects, and their subsequent processing. Such a study makes it possible to improve the safety of the transportation process by careful monitoring of the most vulnerable areas of the rolling stock. This reveals the prefailure state before it occurs. The results of the study can be used as a basis for adjusting the current structure of the repair cycle in terms of reducing inter-repair runs.