scholarly journals Optimization of Maintenance Activity Using the World-Class Maintenance System in Skidder Forest Operations

2019 ◽  
Vol 11 (10) ◽  
pp. 162 ◽  
Author(s):  
Carlos Cézar Cavassin Diniz ◽  
Diellen Lydia Rothbarth ◽  
Eduardo da Silva Lopes ◽  
Gabriel de Magalhães Miranda ◽  
Henrique Soares Koehler ◽  
...  
Keyword(s):  
Eucalyptus Grandis ◽  
Production Costs ◽  
Maintenance Cost ◽  
Maturation Stage ◽  
Hydraulic Oil ◽  
Maintenance System ◽  
Time Between Failures ◽  
World Class ◽  
Maintenance Activities ◽  
Proactive Maintenance

The objective of this paper was to evaluate technical and economically the use of a world-class maintenance system (WCM) in the forest skidding operations. The study was performed in a forest company located in the state of Paraná, inside forest plantations of Pinus taeda and Eucalyptus grandis. For the purpose of analysis, the mechanical availability, hydraulic oil consumption, average time between failures, average repair time, proactive maintenance index and maintenance costs were evaluated during 18 months, considering the stages of implantation, maturation and stabilization of the WCM system. As a result, there was an increase in the percentage of mechanical availability and reduction of 47.0% in the consumption of hydraulic oil from Skidder in the maturation stage. Also, the average time between failures and repairs increased in the maturation stage, caused by a quality improvement of maintenance activities. Moreover, in the maturation stage there was an increase of 45.0% in the proactive maintenance index. Additionally, it was verified that the hourly maintenance cost was reduced by 8.0% between the maturation and stabilization stages, underlining the WCM system’s potential to improve maintenance activities in the forest skidding operation. These results show that the WCM system can contribute to safety in wood harvesting operations, increasing the Skidder mechanical availability and a reducing the production costs.

scholarly journals Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform

2021 ◽  
Vol 11 (21) ◽  
pp. 10307
Author(s):  
Artur Pollak ◽  
Sebastian Temich ◽  
Wojciech Ptasiński ◽  
Jacek Kucharczyk ◽  
Damian Gąsiorek
Keyword(s):  
Continuous Production ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Maintenance Cost ◽  
Belt Drive ◽  
Manufacturing Companies ◽  
Maintenance System ◽  
Remaining Service Life ◽  
Reduced Costs ◽  
Belt Transmission

Continuous production maintenance cost is among one of the highest operational expenses for manufacturing companies. Proper planning of maintenance interventions results in optimized equipment use, higher product quality, and reduced costs. For a belt drive usefulness, it is important that it is properly stretched and has no defects. However, manual condition assessment requires a production line stop, which in turn causes production to stop with associated consequences. Continuous fault diagnosis for anomalies is a fundamental step in estimating a component’s remaining service life and then obtaining a reliable predictive maintenance system that reduces production costs. The presented work presents an approach to anomaly detection based on the vibrations obtained from the operation of the belt transmission.

scholarly journals ANALYSIS OF INDICATORS AND COST OF WORLD CLASS MAINTENANCE (WCM) IN FOREST MACHINES

FLORESTA ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 533
Author(s):  
Carlos Cezar Cavassin Diniz ◽  
Eduardo Da Silva Lopes ◽  
Gabriel De Magalhães Miranda ◽  
Henrique Soares Koehler ◽  
Eduardo Kremer Custodio de Souza
Keyword(s):  
Maintenance Costs ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
World Class ◽  
The Mean ◽  
Mean Time To Repair ◽  
Mean Time ◽  
Proactive Maintenance

The study was carried out at a forest company located in the Paraná State, Brazil, with the feller buncher, skidder and harvester. The following indicators were evaluated: mechanical availability, mean time between failures, mean time to repair, proactive maintenance index and maintenance costs, based on data obtained over a period of 18 months, contemplating the stages of implantation, maturation and stabilization of the WCM. The results showed an increase in the mechanical availability of the cutting and skidding machines from the implantation stage. The mean time between failures increased from the implantation stage, from 31.59 hours to 37.01 hours in the stabilization stage. As for the mean time to repairs, skidder and harvester presented an increase of 25.9% and 18.9% respectively; however, this increase in time represented an improvement in the quality of maintenance services, reflected in the results of mean time between failures. There was also a 31% increase in the proactive index of the machines studied, resulting in 9% reduction in maintenance costs between the deployment and stabilization stages.

Reliability Analysis Using Deep Learning

2018 ◽  
Author(s):  
Chong Chen ◽  
Ying Liu ◽  
Xianfang Sun ◽  
Shixuan Wang ◽  
Carla Di Cairano-Gilfedder ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Reliability Analysis ◽  
General Principle ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Empirical Results ◽  
Time Between Failures ◽  
Learning Techniques ◽  
Deep Learning Model

Over the last few decades, reliability analysis has gained more and more attention as it can be beneficial in lowering the maintenance cost. Time between failures (TBF) is an essential topic in reliability analysis. If the TBF can be accurately predicted, preventive maintenance can be scheduled in advance in order to avoid critical failures. The purpose of this paper is to research the TBF using deep learning techniques. Deep learning, as a tool capable of capturing the highly complex and nonlinearly patterns, can be a useful tool for TBF prediction. The general principle of how to design deep learning model was introduced. By using a sizeable amount of automobile TBF dataset, we conduct an experiential study on TBF prediction by deep learning and several data mining approaches. The empirical results show the merits of deep learning in performance but comes with cost of high computational load.

System of identification and monitoring of technical state of land transport-processing machines

2021 ◽  
Vol 0 (5) ◽  
pp. 44-49
Author(s):  
V. A. Zorin ◽  
◽  
N. I. Baurova ◽  
P.V. Stepanov ◽  
M. M. Styskin ◽  
...  
Keyword(s):  
Proper Time ◽  
Technical State ◽  
Physical Models ◽  
Specific Class ◽  
Diagnostic Data ◽  
Technological Processes ◽  
Maintenance System ◽  
Intermediate States ◽  
Reliability Control ◽  
Proactive Maintenance

Formation problems of a system for identification, monitoring of the technical state and proactive maintenance of airfield land transport-processing machines by using diagnostic data are discussed. The main goal of development and implementation the proactive maintenance system is reliability control and operation safety of transport-processing machines. For the solution of this problem it is necessary to identify and prevent damages in proper time. It requires to assign the specific class of intermediate states named the pre- emergency state class. Logical and physical models describing changes in machine states, technological processes, geolocation, event visualization are discussed. The implementation results of the system of identification, monitoring of the technical state and remote proactive maintenance of airfield land transport-processing machines by using diagnostic data are presented.

Computer Based Expert System for Rotating Machinery: Preventive and Predictive Maintenance

1992 ◽  
Author(s):  
El-Adawi S. El-Mitwally ◽  
M. A. Rayan ◽  
N. H. Mostafa ◽  
Yehia M. Enab
Keyword(s):  
Expert System ◽  
Preventive Maintenance ◽  
Rotating Machinery ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Operating Cycle ◽  
Maintenance System ◽  
Computer Based ◽  
Points Of View ◽  
Parameters Of Interest

Abstract At the present time, the maintenance of the equipment becomes an essential task for any production system. This task is becoming more important from both the quantity and the quality points of view, particularly in developing countries. Initiating a maintenance system controlled by the computer will be valuable and effective. The developed expert system is a combination of an intelligent inference engine matched with a database of information. This system will enable the operator to spot instantaneously the parameters of interest. The expert maintenance system will be designed to perform preventive maintenance tasks and detects faults/failure during the operating cycle. Predictive maintenance enables the operator to minimize the shut down time of faulty equipment and hence increases the productivity. Furthermore, the system will minimize the probable human faults and reduce production costs.

scholarly journals Adaptive decision support for suggesting a machine tool maintenance strategy

2018 ◽  
Vol 24 (3) ◽  
pp. 376-399 ◽  
Author(s):  
Abubaker Shagluf ◽  
Simon Parkinson ◽  
Andrew Peter Longstaff ◽  
Simon Fletcher
Keyword(s):  
Decision Support ◽  
Machine Tool ◽  
Cost Estimation ◽  
Expert Knowledge ◽  
Production Costs ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Content Type ◽  
Machine Tool Accuracy

Purpose The purpose of this paper is to produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach A maintenance cost estimation model is utilised within the research and development of this decision support system (DSS). An empirical-based methodology is pursued and validated through case study analysis. Findings A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case study, a 28 per cent reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value The paper presents an adaptive DSS to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique.

Dairying in the Antipodes: recent past, near prospects

2013 ◽  
Vol 53 (9) ◽  
pp. 882 ◽  
Author(s):  
Dave Clark ◽  
Bill Malcolm ◽  
Joe Jacobs
Keyword(s):  
New Zealand ◽  
Comparative Advantage ◽  
Milk Fat ◽  
Exchange Rate Volatility ◽  
World Market ◽  
Production Costs ◽  
World Population ◽  
Total Production ◽  
Dairy Farmers ◽  
World Class

The majority of dairy farmers and processors in Australia and New Zealand are considered world class due to their ability to produce dairy products at a cost that is competitive on the world market without requirement for subsidy. International and domestic forces beyond the farm influence the international competitiveness of Antipodean dairy systems, as much or more than, the within-farm characteristics of the systems. Critical external forces include: world population growth, protein demand from increasingly wealthy developing countries, dairy supply from domestic and international producers, international dairy prices and exchange rate volatility. Within farm, the keys to persistent profitability, business survival, and growth will continue to be management ability and labour skill as well as the relationship between milksolids (milk fat + milk protein) produced per system and total production costs. Domestic forces will include competition for resources such as land, water, quality labour and capital, and public expectation that farms will meet the costs of community environmental and welfare objectives. Public and industry investment in research, development and extension in innovations that increase productivity is essential if dairying is to remain competitive. The operation of the comparative advantage principle determines which industries thrive, or decline, in an economy. New Zealand dairying has a strong comparative advantage over alternative pastoral industries which will continue. In Australia, the comparative advantage of dairy farming over alternative activities is less clear-cut. History shows that the best farmers and processors handle risks such as market and climate volatility and other challenges better than others, and their prospects are positive. However, world class performers in the future dairy industry will certainly not be all, or even the majority, of the current population of dairy farmers.

scholarly journals PSO Based Optimization of Testing and Maintenance Cost in NPPs

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Qiang Chou ◽  
Daochuan Ge ◽  
Ruoxing Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Nuclear Power ◽  
Operating Cost ◽  
Technical Specification ◽  
Maintenance Cost ◽  
Risk Level ◽  
Candidate Solution ◽  
Maintenance Activities ◽  
Maintenance Requirements ◽  
Constrained Multiobjective Optimization

Testing and maintenance activities of safety equipment have drawn much attention in Nuclear Power Plant (NPP) to risk and cost control. The testing and maintenance activities are often implemented in compliance with the technical specification and maintenance requirements. Technical specification and maintenance-related parameters, that is, allowed outage time (AOT), maintenance period and duration, and so forth, in NPP are associated with controlling risk level and operating cost which need to be minimized. The above problems can be formulated by a constrained multiobjective optimization model, which is widely used in many other engineering problems. Particle swarm optimizations (PSOs) have proved their capability to solve these kinds of problems. In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Numerical results have demonstrated the efficiency of our proposed algorithm.

scholarly journals Potential Use of Eucalyptus Seedling in Recycling of Fish Farming Wastewater in Agriculture

2019 ◽  
pp. 1-11
Author(s):  
Dalva Paulus ◽  
Ezequiel Toffoli ◽  
Celso Eduardo Pereira Ramos ◽  
Claúdia de Andrade Moura ◽  
Dislaine Becker ◽  
...  
Keyword(s):  
Nutrient Solution ◽  
Quality Index ◽  
Eucalyptus Grandis ◽  
Leaf Tissue ◽  
Biomass Accumulation ◽  
Fish Farming ◽  
Production Costs ◽  
Nutrient Sources ◽  
Nutrient Contents ◽  
University Of Technology

Aims: The reuse of fish wastewater in agricultural activities such as the production of seedlings in commercial nurseries has great potential to minimize production costs and to reduce environmental impacts due to the inappropriate disposal of this waste. The objective of this study was to evaluate the growth, development and quality of Eucalyptus grandis W. Hill ex Maiden seedlings produced with different wastewater concentrations from fish farming. Study Design:  The fertigation treatments were using fish farming (Tilapia) wastewater from tanks (FW), daily nutrient solution (DNS), and the combinations of 50% FW + 50% DNS, and 25% DNS + 75% FW, applied daily. Place and Duration of Study: The experiment was carried out from August to December 2016 at the Federal University of Technology of Paraná, Brazil. Methodology: The macro and micronutrient contents in leaf tissue, seedling height, stem diameter, the largest root length, leaf area, fresh and dry shoot and root mass and Dickson quality index were all evaluated. Results: The nutrient contents present in the leaf tissue were adequate for the nutritional demand of the eucalyptus. The seedling quality index (DQI) indicated that the daily nutrient solution (0.21) and 50% FW + 50% DNS (0.20) generated balanced seedlings regarding height and biomass accumulation. Conclusion: Fertigation of eucalyptus seedlings can be carried out with wastewater from fish farming, but it is necessary to complement the fertilization with other nutrient sources to produce quality seedlings which are suitable for field transplantation.

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