Integration of FMECA and statistical snalysis for predictive maintenance

[EN] The estimation of time-to-failure of machines is of utmost importance in the Manufacturing Industry. As the world is moving towards Industry 4.0, it is high time that we progress from the traditional methods, where we wait for a breakdown to occur, to the prognostics based methods. It is the need of the era to be aware of any incident before it occurs. This study provides application of Statistical-based Predictive maintenance. A BOPP Production line has been considered as a case study for this research. Since the inception of the line in 2013, it is evident that 60% of breakdowns are due to lack of maintenance and timely replacement of bearings. Therefore, the research is based on the application of FMECA (Failure Modes, Effects and Criticality Analysis) to determine which bearing in the production line is most prone to failure and determination of which statistical model best fits the failure data of the most critical bearing. The result provides the best distribution fit for the failure data and the fit can be utilized for further study on RUL (Remaining Useful Life) of the bearing through Bayesian Inference.The author would like to express great appreciation to Dr. Tariq Mairaj for his valuable suggestions. I would also like to extend my thanks to TriPack Films, QVISE Pvt. Ltd., NUST PNEC and PNEC NDT Lab for offering me the resources. Finally, I wish to thank my parents, siblings, Engr. Iqra Johim and Dr. Hiba Rehman & her family for their support and encouragement throughout the study.Ghani, R. (2021). Integration of FMECA and statistical snalysis for predictive maintenance. Journal of Applied Research in Technology & Engineering. 2(1):33-37. https://doi.org/10.4995/jarte.2021.14737OJS333721Becker, W.T., Shipley, R.J. (2002). Failure Analysis and Prevention. In W. T. Becker, & R. J. https://doi.org/10.31399/asm.hb.v11.9781627081801Carlson, C.S. (2014). Understanding and Applying the Fundamentals of FMEAs. 2014 Annual Reliability and Maintainability Symposium. Tucson: IEEE.Carlson, C.S. (2016). Understanding and Applying the Fundamentals of FMEAs. Reliability and Maintainability Symposium.Carnero, M. (2006). An evaluation system of the setting up of predictive maintenance programmes. Reliability Engineering and System Safety, 91, 945-963. https://doi.org/10.1016/j.ress.2005.09.003Merovci, F., Elbatal, I. (2015). Weibull Rayleigh Distribution: Theory and Applications. Applied Mathematics & Information Sciences, 9(5), 1-11.Mobley, R.K. (2002). An Introduction to Predictive Maintenance. Woburn, Massachusetts, USA: Elsevier Science. https://doi.org/10.1016/B978-075067531-4/50006-3Muller, C. (2003). Reliability Analysis of the 4.5 Roller Bearing. Monterey, California: Naval Postgraduate School.Rao, B. (1996). Handbook of Condition Monitoring. Oxford: Elsevier Advanced Technology.Sahoo, T., Sarkar, P.K., Sarkar, A.K. (2014). Maintenance optimization for critical equipments in process industries based on FMECA method. International Journal of Engineering and Innovative Technology, 3(10), 107-112.Susto, G.A., Beghi, A., Luca, C.D. (2012). A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques. IEEE Transactions on Semiconductor Manufacturing, 25(4), 638-649. https://doi.org/10.1109/TSM.2012.220913

Maintenance strategy focused on the specific consumption of diesel generators in sub-saharan countries: Case of National Electricity Company of Burkina Faso

Sub-Saharan countries would mainly use thermal power plant whose Specific Consumption (SC) was relatively higher than the reference values provided by the manufacturers, which would contribute to the increase in electricity production costs. The aim of this study would be to propose a maintenance strategy which would aim to keep the SC according to the age of the generator at acceptable proportions according to the reference values provided by the manufacturers. The Ishikawa and Pareto diagrams were used to identify and analyze the causes of the variation in the SC of two large plants of the National Electricity Company of Burkina Faso. The results showed four major causes representing about 20% of the common causes which are 80% of the increase in SC in the thermal power plant of Kossodo and Komsilga, it would be : the poor quality of the fuels, lack of spare parts, inadequate maintenance practice, and poor fuel supply policy

Maintenance strategy focused on the specific consumption of diesel generators in sub-saharan countries: Case of National Electricity Company of Burkina Faso

Sub-Saharan countries would mainly use thermal power plant whose Specific Consumption (SC) was relatively higher than the reference values provided by the manufacturers, which would contribute to the increase in electricity production costs. The aim of this study would be to propose a maintenance strategy which would aim to keep the SC according to the age of the generator at acceptable proportions according to the reference values provided by the manufacturers. The Ishikawa and Pareto diagrams were used to identify and analyze the causes of the variation in the SC of two large plants of the National Electricity Company of Burkina Faso. The results showed four major causes representing about 20% of the common causes which are 80% of the increase in SC in the thermal power plant of Kossodo and Komsilga, it would be : the poor quality of the fuels, lack of spare parts, inadequate maintenance practice, and poor fuel supply policy

Automobile maintenance modelling using gcForest

Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost

An industry 4.0-enabled low cost predictive maintenance approach for SMEs: a use case applied to a CNC turning centre

This paper outlines the base concepts, materials and methods used to develop an Industry 4.0 architecture focused on predictive maintenance, while relying on low-cost principles to be affordable by Small Manufacturing Enterprises. The result of this research work was a low-cost, easy-to-develop cyber-physical system architecture that measures the temperature and vibration variables of a machining process in a Haas CNC turning centre, while storing such data in the cloud where Recursive Partitioning and Regression Tree model technique is run for predicting the rejection of machined parts based on a quality threshold. Machining quality is predicted based on temperature and/or vibration machining data and evaluated against average surface roughness of each machined part, demonstrating promising predictive accuracy

Critical Maintenance of Thermal Power Plant Using the Combination of Failure Mode Effect Analysis and AHP Approches

The electricity power generation plays the important role of every business or industrial, since it must be supplied to cove with the full consumption on demand sites. To keep with constant operating point of electric power generation of thermal process, the maintenance is the most crucial technique for preserving the deterioration or damage of equipments. In this research the thermal power plant of Electric Generation Authority of Thailand (EGAT) is selected to develop the maintenance system. Historical maintenance data of each unit of thermal plant must be retrieved. The data are classified and identified in four levels such as units, systems, equipments, and component. The data is characterized to database manner by using SQLserver and Visual C# 2005 is used for implementing the user program interfacing. The criteria level applies the combination of Failure Mode Effect Analysis (FMEA) and AHP approaches to find the critical ranking priority of failure mode relating to three criteria such as maintenance cost, man per hour working, line priority. In summary of this research, we analyze and develop the software for maintenance priority and management for thermal power plant. The developed software can help the maintenance team for making decision in spare part management and it is friendly-user to pursuit the maintenance policy focused on critical maintaining equipments in overall system

Best practices for public universities building maintenance

A university’s main function is to produce quality graduates, and in doing so, university buildings do play significant roles and are considered to be vital assets and resources. Hence, to prolong a university building lifecycle to ensure all university activities to continue performing at an optimum level, building maintenance management is central. Even though several previous studies have been conducted in this area, university buildings maintenance is still viewed as insignificant. The objectives of this research are to identify the current practices in building maintenance management for public university buildings, to identify the factors that contribute to public universities building maintenance costs and to determine the best practices for effective building maintenance management for public university buildings. Five public universities namely Universiti Teknologi Malaysia (UTM), Universiti Malaya (UM), Universiti Kebangsaan Malaysia (UKM), Universiti Malaysia Sarawak (UNIMAS) and Universiti Teknikal Malaysia (UTeM) were chosen as case studies. The data was collected through interviewing ten building maintenance experts. Subsequently, the data was then analysed using single-case analysis and cross-case analysis. Based on the findings, current practices employed by the university maintenance department are prioritize maintenance, planned maintenance, preventive maintenance and corrective maintenance. Furthermore, based on the findings, factors contributing to building maintenance costs are fund allocation, technology, mechanical and electrical factors, aging of buildings, environmental factors and vandalism. Best practices, as suggested by the participants involved in this study, are preventive maintenance, prioritize maintenance, and planned maintenance

Predictive Maintenance: Monitoring Tools and Equipment

Due to low service quality of maintenance management, high maintenance cost becomes a common issue in building industry of Malaysia. Lack of preventive measure is the problem that resulting poor maintenance performance. So, condition-based maintenance is introduced to improve the maintenance performance. Monitoring tools and equipment is seen as an important factor to ensure the efficiency of condition-based maintenance. So, this paper aims to determine the aspects of monitoring tools and equipment to be concerned in building maintenance, as well as to establish the relationship between the aspects and maintenance cost performance. A quantitative approach is adopted and performed through questionnaire survey. Furthermore, descriptive analysis and correlation analysis are used to analyse the research data. The literature review determines three aspects of monitoring tools and equipment to be considered in maintenance management. Furthermore, the research result demonstrates that the budget allocation for acquisition of monitoring tools and equipment, capability to operate the tools and equipment, as well as availability of the tools and equipment are significantly correlated to the maintenance cost variance. The research recommends the maintenance management to convince the clients or organisation to acquire advanced monitoring tools and equipment for implementation of condition-based maintenance. Besides that, provision of training is encouraged to ensure that the maintenance personnel are able to utilise the tools and equipment

Designing Predictive Maintenance for Agricultural Machines

The Digital Transformation alters business models in all fields of application, but not all industries transform at the same speed. While recent innovations in smart products, big data, and machine learning have profoundly transformed business models in the high-tech sector, less digitalized industries—like agriculture—have only begun to capitalize on these technologies. Inspired by predictive maintenance strategies for industrial equipment, the purpose of this paper is to design, implement, and evaluate a predictive maintenance method for agricultural machines that predicts future defects of a machine’s components, based on a data-driven analysis of service records. An evaluation with 3,407 real-world service records proves that the method predicts damaged parts with a mean accuracy of 86.34%. The artifact is an exaptation of previous design knowledge from high-tech industries to agriculture—a sector in which machines move through rough territory and adverse weather conditions, are utilized extensively for short periods, and do not provide sensor data to service providers. Deployed on a platform, the prediction method enables co-creating a predictive maintenance service that helps farmers to avoid resources shortages during harvest seasons, while service providers can plan and conduct maintenance service preemptively and with increased efficiency