Biomedical Machine Maintenance Scheduling

Tissue banks procure approximately 45,000 tissue donations per year, providing nearly 9,000,000 individuals (about half the population of New York) with life-enhancing and life-saving medical procedures. Proper biobank machine maintenance is imperative to this process. Mandatory forms of maintenance are critical to avoid unexpected malfunctions, which can halt operations and render samples unusable. Each machine has a unique reliability rate within the system; although some can quickly be repaired or replaced, many processes rely on limited machinery where even planned downtime can significantly influence the tissue processing. AlloSource, one of the largest tissue manufacturers in the United States, too often schedules these preventive events unnecessarily or inconveniently, resulting in machines breaking down at inopportune times. In response to these inefficiencies we ask, “What is the best consolidated and standardized equipment maintenance schedule that maximizes monthly maintenance events to ensure increased equipment availability while meeting the demand of the biomedical manufacturing network?” We use an optimization model to consider equipment reliability, downtime, availability, and demand to develop a preventive maintenance schedule. Our model focuses on scheduling the maximum number of events the maintenance crew can conduct each month to ensure vital equipment to the allograft process is available, which provides more opportunities for tissue therapies. In doing so, the maintenance crew is also able to complete more events, driving up annual throughput while driving down equipment downtime.

Condition Monitoring to Enable Predictive Maintenance on a Six-Die Nut Manufacturing Machine through Force Data Analysis

Nut fasteners are produced by machines working around the clock. Companies generally operate with a run-to-failure or planned maintenance approach. Even with a planned maintenance schedule, however, undetected damage to the dies and non-die parts occurring between maintenance periods can cause considerable downtime and pervasive damage to the machine. To address this shortcoming, force data from the fourth and sixth dies of a six-die nut manufacturing machine were analysed using correlation to the best health condition on the force profile and on the force shock response spectrum profile. Fault features such as quality adjustments and damage to both die and non-die parts were detectable prior to required maintenance or machine failure. This detection was facilitated by the determination of health thresholds, whereby the force SRS profile generated a longer warning period prior to failure. The analytical approach could benefit the industry by identifying damage that would normally go undetected by operators, thereby reducing downtime, extending die life, enabling “as needed” maintenance, and optimising machine operation.

Availability Projections of Hydroelectric Power Plants through Monte Carlo Simulation

The present work proposes a Monte Carlo Simulation (MCS) to obtain availability projections for Hydroelectric Power Plants (HPP), based mainly on regulatory aspects involving the Availability Factor (AFA). The main purpose of the simulation is to generate scenarios to obtain statistics for risk analysis and decision-making in relation to the HPP. The proposed methodology consists of two steps, firstly, the optimization of the maintenance schedule of the hydroelectric plant is carried out, in order to allocate the mandatory maintenance in the simulation horizon. Then, for the MCS, scenarios of forced shutdowns of the Generating Units (GU) will be generated, which directly influence the operation and, consequently, the availability of the HPP. The scenarios will be inserted into an operation optimization model, which considers the impact of forced shutdown samples on the MCS. The proposed modeling was applied using real data from the Santo Antônio HPP, which is one of the largest hydroelectric plants in Brazil.

Digital Transformation Journey of Field Operations at Abu Dhabi Offshore Field in UAE

Field operations generate large volumes of data from various equipment and associated Meta data such as inspection due dates, maintenance schedule, people on board, etc. The data is often stored in silos with a data guardian for each entity. The objective of this project was to volarize the data by developing engineered KPI's to drive decision making and make data accessible for everyone in the organization to foster cross collaboration. Data analytics and visualization solutions were developed to automate low value-added tasks either using robotic process automation scripts or business intelligence reporting tool. Data was residing either in spreadsheet or native applications. With support of IT, centralized database was established. Scrum agile project management techniques were used to develop digital solutions. A high-level digital road map was created consulting all teams including stake holders. Use cases were identified and captured in lean A3 problem solving format. Each use case clearly identified the benefits to organization, and this was used to prioritize the use cases. A sprint was set-up with agile team and products were developed as per end user's expectation. The constant feedback loop via daily stand-up meetings helped the team deliver value added products. Digital solutions were developed to automate low value-added tasks so employees can focus on improving systems instead of producing reports. By developing engineering KPI's and predictive analytics, technical authority could shift from reactive maintenance to pro-active maintenance. Using linear regression machine learning, early warning digital solution was developed to monitor and notify technical authority to clean strainers. The production team achieved 0.75 full time equivalent (FTE) in time savings by automating reports. By visualizing operations data such as flaring, production profiles; the team minimized flaring leading to 1% OPEX cost saving. Around 10% of chemical budget was saved by monitoring chemical injections at all platforms. Similar cost savings were achieved by visualizing data for other disciplines such as maintenance and HSE teams. By being better informed about wells annuli pressure build-up via email notifications, wells integrity team reduced the associated risk. By forming a multi-disciplinary agile team with business and delivery team, digital team deployed 20+ digital products over a short time frame of 2 years.

A HOLISTIC METHOD FOR THE COMMISSIONING AND OPTIMISATION OF ADAPTIVE ROAD LIGHTING SYSTEMS USING LABORATORY AND FIELD MEASUREMENTS

This paper presents a set of field measurements performed at renovated lighting installations equipped with adaptive road lighting systems. The results revealed issues of over-illumination but also insufficient lighting levels that would not be shown without the field measurements. Since the adaptive road lighting systems can regulate the lighting output and should in parallel ensure adequate lighting and safety levels, the commissioning of such system is challenging. In this scope, we are proposing a holistic scheme for the commissioning and optimization of the operation of Adaptive Road Lighting systems taking into account field measurements, technical parameters of the incorporated equipment, and all aspects that may affect its performance. The proposed scheme can be used as a masterplan from the conception of the road lighting up to the validation field measurements and the long-term optimization and maintenance schedule of the installation.

Development of requirements for logic controls of the speed lines catenary system maintenance

Objective: To develop requirements for the organization of maintenance of the catenary system based on the actual state indicated by the digital diagnostic and monitoring platform in the logic control system for high-speed traffic. Methods: An overview analysis of high-speed catenary system diagnostics and monitoring systems is applied. Results: The systems of monitoring and automated control of catenary system devices in high-speed lines have been studied. The analytical findings as regards the maintenance methods according to the preventive maintenance schedule and based on the actual state monitoring data have been presented. Requirements for diagnostic and monitoring devices 15 of the high-speed lines catenary system have been formulated, aimed at improving the quality attributes of current collection. Practical importance: A structural diagram of quality diagnostics and monitoring for the current collection in the high-speed Current Collector–Catenary system was obtained. The design concept of a prin-cipal model of logic control of the catenary system life cycle has been developed

Analysis of the Machine Tool Equipment as a Segment of Risk Management

Despite the industrial revolutions, optimization of production processes, the use of robotics and other advances of science and technology, the share of machine-tool equipment in the execution of the plan at industrial enterprises is large. Human-machine interaction is also invariable. In this regard, the injury rate of machine operators from year to year remains above average. To manage the employee occupational risks, it is required to carefully analyze the source of the risk — the machine-tool equipment used. To manage the professional risks of a machine operator, it is required to assess the following parameters of the machine-tool complex: traumatic factors in accordance with the current legal requirements, and the data obtained as a result of the equipment practical application; completeness and content of the technical documentation for the machine-tool equipment; equipment life cycle stage, equipment operation period; fulfillment of the scheduled preventive maintenance schedule; the number and nature of emergency equipment failures; ergonomic indicators of the machine. The analysis carried out according to the specified parameters can be presented visually in the form of a diagram reflecting the condition of machine equipment on six elements and on three levels using generally accepted signal colors: green - corresponds, yellow — partially corresponds, red — does not correspond. Thus, the results of the conducted analysis are visualized on a color chart, and mathematically evaluated as a percentage. Comprehensive assessment of the state of machine-tool equipment makes it possible to more efficiently determine the risks for those working on the machine-tool equipment, as well as develop measures for the modernization or replacement of the machine-tool park.

Sterilizer Reliability Analysis Using Reliability Block Diagram Based on Failure Identification Through Fault Tree Analysis

A sterilizer is a pressurized steam vessel used to boil palm oil. The condition of the sterilizer at PT .X often emits steam at the door and body of the stew. Throughout 2020, there were 12 critical components that were frequently damaged, such as ball valve, actuator, exhaust valve, packing door, elbow, condensate nozzle, liner, pipe, condensate valve, strainer valve, pipe flange, and packing flange. Fault Tree Analysis is an analysis tool that graphically translates the combinations of errors that cause system failures. Reliability Block Diagram is a diagramming method for showing how reliability components contribute to the success or failure of a complex system. Based on the results of the failure calculation using fault tree analysis, the probability of failure of the horizontal sterilizer component is the ball valve 12.2%, exhaust valve 10.9% actuator 6%, door packing 0.24%, elbow 0.24%, condensate nozzle 4.8%, liner 8.61%, 0.25% pipe, 0.21% condensate valve, 4.4% filter valve, 0.22% pipe flange and 0.27% packing flange. The reliability value of the horizontal sterilizer from the calculation using the reliability block diagram is 85.69% if it operates for 8 hours, 62.93% if it operates for 27 hours, 39.6% if it operates for 54 hours, 13.34% if it operates for 117 hours. o'clock. o'clock. o'clock. hours and 1.81% when operating for 234 hours. To maintain reliability above 60%, the preventive maintenance schedule is: Every 80 hours of operation a door packing inspection is carried out. Every 234 hours of operation, elbow tubing and flanges are checked. Every 300 hours of operation, a pipe inspection is carried out. Every 450 operational hours an inspection is carried out on the ball valve, condensate nozzle, liner, actuator, and exhaust valve. Every 30 hours of operation, valve condensate, filter valves and packing flanges are checked.