This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies.

Optimising the Preventive Maintenance Interval Using a Semi-Markov Process, Z-Transform, and Finite Planning Horizon

We propose a method for data provision, validation, and service expansion for the spread of a lifelog-based digital healthcare platform. The platform is an operational cloud-based platform, implemented in 2020, that has launched a tool that can validate and de-identify personal information in a data acquisition system dedicated to a center. The data acquired by the platform can be processed into products of statistical analysis and artificial intelligence (AI)-based deep learning modules. Application programming interfaces (APIs) have been developed to open data and can be linked in a programmatic manner. As a standardized policy, a series of procedures were performed from data collection to external sharing. The proposed platform collected 321.42 GB of data for 146 types of data. The reliability and consistency of the data were evaluated by an information system audit institution, with a defects ratio of approximately 0.03%. We presented definitions and examples of APIs developed in 17 functional units for data opening. In addition, the suitability of the de-identification tool was confirmed by evaluating the reduced risk of re-identification using quasi-identifiers. We presented specific methods for data verification, personal information de-identification, and service provision to ensure the sustainability of future digital healthcare platforms for precision medicine. The platform can contribute to the diffusion of the platform by linking data with external organizations and research environments in safe zones based on data reliability.

/pdf/diffusion-of-a-lifelog-based-digital-healthcare-platform-for-3b61ax4h.pdf

Diffusion of a Lifelog-Based Digital Healthcare Platform for Future Precision Medicine: Data Provision and Verification Study

During the design of maintenance plans for an asset, the frequency calculation of preventive interventions is a maintenance engineer's essential task. This document presents a tool (mathematical formula) to calculate the preventive maintenance interval. The tool considers the maintenance costs and the income generated for the company by its use. Managers can benefit from its simple application to gain efficiency. Typically, business projects are executed over a period. The tool calculates the optimal interval for any given project duration. It also allows the analysis of the size of the preventive interval when the income received changes. This change may be due to asset impairment or changes in the market price of the business product. For the development of the tool, a semi-Markovian model is designed with four states representing the operation and maintenance of the asset. The asset evolves by transiting between states and accumulating costs and income in the form of returns. From the model, a system of difference equations is developed whose variable is the expected accumulated return at any transition, and the z-transform is used to solve it. Deriving its mathematical expression, the formula for the preventive interval that maximizes the expected accumulated return is obtained. Both formulae are used in a case study. Taking advantage of the degraded state, it is shown that higher income increases the interval size while lower-income decreases it. The limits of these variations are also established. Simulation and numerical methods are used to validate analytic results. 

Finite time preventive maintenance optimization by using a Semi-Markov process with a degraded state. A case study for diesel engines in mining

This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies. 

The problem in this study is the occurrence of damage to the Oven, Mixer, Bread Sicer, Proofer, Despositor, Breadline, Vacuum sealer machines. XYZ has not yet scheduled planned maintenance on production machines. The purpose of this study is to determine the reliability of the machine in operating according to the desired function for a certain period of time at XYZ and to design machine maintenance scheduling at XYZ. This research is a quantitative research with the method used is Reability Centered Maintenance (RCM) and Maintenance Value Stream Mapping (MVSM). The data used is secondary secondary data obtained from XYZ, namely data on damage to Oven, Mixer, Brread Slicer, Proofer, Despositor, Breadline, Vacuum sealer machines in 2021. The results of this study state that the reliability of the machine operates according to its function states that Ovens had an average operating time of about 584 hours between 5 failures, breadslicer machines had an average time of about 1460 between 2 failures, proofer machines had an average time of about 1460 hours between 2 failures, breadline machines had an average time of about 1460 hours between 2 failures, vacuum sealers have an average time of about 2920 hours between 1 failure, and deposit machines have an average time of 2920 hours between 1 failure. Designing proper machine maintenance scheduling for XYZ, namely Mixer maintenance or replacement every 30 days, Depositors every 60 days, ovens every 90 days, proofers every 120 days, breadline siever 60 days, bread silicer every 60 days and vacuum sealer every 30 days.

/pdf/machine-maintenance-scheduling-design-using-reability-3qllx0of.pdf

Machine Maintenance Scheduling Design Using Reability Centered Maintenance (RCM) method and Maintenance Value Stream Mapping (MVSM) at XYZ

Maintenance has a very decisive role in the production activities of a company that concerns the smoothness and congestion of production, delays, and the volume of production and production efficiency. The use of semi-automatic machines at PT. Y influences engine reliability. The reliability of the engine depends on the period of use, the older the machine’s age, its function also decreases. The problem formulated in this study is the preparation of maintenance schedules with due regard to the reliability of critical components on the sterilizer machine. The development of the program is based on the reliability engineering approach, which covers the distribution test of engine damage time interval data, calculation of the mean time between failure (MTBF) of the engine, and the reliability of the engine (reliability) when it is repaired. Secondary data obtained from preventive maintenance at PT. Y.

Design of preventive maintenance system at PT. Y with reliability engineering approach

Quality control is a system of maintaining the quality of products on an ongoing basis. In the industrial world, quality control is essential to meet customer demand. The problem formulated in this study is the assessment of the Defect of the Cutter Case produced by PT. The object in this study is the Cutter Case product. The number of samples used was 450 units. The Cutter Case will be inspected for the number of defective products and defect criteria that are met for each product unit, and the length and width of the product surface. Rules for the type of defect studied were melting, non-jagging, and bent. The method used in solving problems in this study is the Six Sigma method, namely DMAIC (Define, Measure, Analyse, Improve, Control).

Quality control of cutter case at PT. X with six sigma approach

Analysis of Drivers and Barriers The Implementation of Sustainability Supply Chain Management (SSCM) in PT. ABC

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Design of preventive maintenance system at PT. Y with reliability engineering approach

Quality control of cutter case at PT. X with six sigma approach

Analysis of Drivers and Barriers The Implementation of Sustainability Supply Chain Management (SSCM) in PT. ABC