An ANFIS model for forecasting risk by Overall Equipment Effectiveness parameter in Total Productive Maintenance

2013 ◽  
pp. 245-252
Keyword(s):  
Total Productive Maintenance ◽  
Anfis Model ◽  
Effectiveness Parameter

scholarly journals Application of the Concept of Total Productive Maintenance in Automatic Bottle Filling Machines

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Yon Pradana ◽  
Rosleini Ria Putri Zendrato ◽  
Bagus Ismail Adhi Wicaksana
Keyword(s):  
Total Productive Maintenance ◽  
Effect Analysis ◽  
Engine Efficiency ◽  
Standard Operating ◽  
Working Groups ◽  
Fmea Method ◽  
Maintenance Schedules ◽  
Filling Machines ◽  
Effectiveness Parameter ◽  
Small Working

Guwatirta Sejahtera is one of the companies engaged in the bottled drinking water industry. Problems that occur at PT. Guwatirta Sejahtera is the absence of routine maintenance on the machine so that damage, especially on automatic bottle filling machines, often occurs. This study applies a total productive maintenance method to determine the level of damage and identify the source of the problem as a basis for making recommendations for improvement in reducing the level of damage that often occurs in automatic bottle filling machines. The stage of the study begins by determining the engine efficiency and six big losses using the Overall Equipment Effectiveness parameter (OEE). Then proceed with identifying the source of the problem using the Failure Mode and Effect Analysis (FMEA) method. Recommendations for improvement include making: a daily inspection checklist, small working groups, Standard Operating Procedures for operating the machine and routine maintenance schedules. Based on the estimated calculation if the recommendation for improvement is applied, the value of the original engine efficiency of 84.26% increased to 98.97% and the initial maintenance costs of Rp.81,232,369.48 decreased to Rp.75,355,764.39 per year

Total Productive Maintenance

2012 ◽  
Vol 2 (3) ◽  
pp. 152-153 ◽  
Author(s):  
Samandar Singh ◽  
◽  
Prof. D. S. Kumani Prof. D. S. Kumani ◽  
Ved Parkash
Keyword(s):  
Total Productive Maintenance

IMPLEMENTATION OF TOTAL PRODUCTIVE MAINTENANCE TO ACHIEVE SUSTAINABLE IMPROVEMENT

2019 ◽  
Vol 1 (2) ◽  
pp. 1-6
Author(s):  
Rachmasari Pramita Wardhani
Keyword(s):  
Total Productive Maintenance ◽  
Sustainable Improvement

Dalam dunia industry, khususnya perusahaan manufaktur dalam banyak kasus membutuhkan penanganan kerja yang dilakukan menggunakan alat alat berat untuk mendukung  produktivitas kinerja perusahaan. Hal ini membutuhkan perencanaan program yang bagus dalam menangani perawatan peralatan mesin mesin tersebut sehingga dapat berkjalan sesuai fungsinya. TPM atau dikenal dengan total productive maintenance atau perawatan produktif total adalah suatu system perawatan dan pemberdayaan peningkatan integritas produksi , keamanan dan system mutu melalui peralatan mesin, peralatan perlengkapan, process dan pekerja yang menambah nilai dari suatu bisnis di perusahaan. Total productive maintenance (TPM) merupakan suatu bagian dari manajemen system perusahaan dalam menjaga bagian perawatan dalam mencapai peningkatan secara berkesinambungan untuk memperoleh performansi yang baik. Dengan mengimplementasikan konsep dari TPM dengan perencanaan pada perawatan yang harus dapat dicapai dan kemampuan kerja dari mesin secara kontinu, mengoptimasi biaya perawatan , mengurangi inventori, meningkatkan kehandalan, dan kemampuan kerja dari mesin.  

scholarly journals The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 345
Author(s):  
Janusz Sowinski
Keyword(s):  
Load Forecasting ◽  
Large Set ◽  
Exogenous Variables ◽  
Anfis Model ◽  
Load Forecast ◽  
Forecasting Models ◽  
Power Company ◽  
Ex Post ◽  
Daily Load ◽  
Selection Of

Forecasting of daily loads is crucial for the Distribution System Operators (DSO). Contemporary short-term load forecasting models (STLF) are very well recognized and described in numerous articles. One of such models is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which requires a large set of historical data. A well-recognized issue both for the ANFIS and other daily load forecasting models is the selection of exogenous variables. This article attempts to verify the statement that an appropriate selection of exogenous variables of the ANFIS model affects the accuracy of the forecasts obtained ex post. This proposal seems to be a return to the roots of the Polish econometrics school and the use of the Hellwig method to select exogenous variables of the ANFIS model. In this context, it is also worth asking whether the use of the Hellwig method in conjunction with the ANFIS model makes it possible to investigate the significance of weather variables on the profile of the daily load in an energy company. The functioning of the ANFIS model was tested for some consumers exhibiting high load randomness located within the area under supervision of the examined power company. The load curves featuring seasonal variability and weekly similarity are suitable for forecasting with the ANFIS model. The Hellwig method has been used to select exogenous variables in the ANFIS model. The optimal set of variables has been determined on the basis of integral indicators of information capacity H. Including an additional variable, i.e., air temperature, has also been taken into consideration. Some results of ex post daily load forecast are presented.

scholarly journals Optimization and Prediction of the Drying and Quality of Turnip Slices by Convective-Infrared Dryer under Various Pretreatments by RSM and ANFIS Methods

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 284
Author(s):  
Ebrahim Taghinezhad ◽  
Mohammad Kaveh ◽  
Antoni Szumny
Keyword(s):  
Energy Efficiency ◽  
Color Difference ◽  
Moisture Diffusivity ◽  
Effective Moisture Diffusivity ◽  
Diffusivity Coefficient ◽  
Drying Process ◽  
Anfis Model ◽  
Drying Time ◽  
Effective Moisture ◽  
Response Variables

Drying can prolong the shelf life of a product by reducing microbial activities while facilitating its transportation and storage by decreasing the product weight and volume. The quality factors of the drying process are among the important issues in the drying of food and agricultural products. In this study, the effects of several independent variables such as the temperature of the drying air (50, 60, and 70 °C) and the thickness of the samples (2, 4, and 6 mm) were studied on the response variables including the quality indices (color difference and shrinkage) and drying factors (drying time, effective moisture diffusivity coefficient, specific energy consumption (SEC), energy efficiency and dryer efficiency) of the turnip slices dried by a hybrid convective-infrared (HCIR) dryer. Before drying, the samples were treated by three pretreatments: microwave (360 W for 2.5 min), ultrasonic (at 30 °C for 10 min) and blanching (at 90 °C for 2 min). The statistical analyses of the data and optimization of the drying process were achieved by the response surface method (RSM) and the response variables were predicted by the adaptive neuro-fuzzy inference system (ANFIS) model. The results indicated that an increase in the dryer temperature and a decline in the thickness of the sample can enhance the evaporation rate of the samples which will decrease the drying time (40–20 min), SEC (from 168.98 to 21.57 MJ/kg), color difference (from 50.59 to 15.38) and shrinkage (from 67.84% to 24.28%) while increasing the effective moisture diffusivity coefficient (from 1.007 × 10−9 to 8.11 × 10−9 m2/s), energy efficiency (from 0.89% to 15.23%) and dryer efficiency (from 2.11% to 21.2%). Compared to ultrasonic and blanching, microwave pretreatment increased the energy and drying efficiency; while the variations in the color and shrinkage were the lowest in the ultrasonic pretreatment. The optimal condition involved the temperature of 70 °C and sample thickness of 2 mm with the desirability above 0.89. The ANFIS model also managed to predict the response variables with R2 > 0.96.

scholarly journals The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits

2018 ◽  
Vol 8 (7) ◽  
pp. 1153 ◽  
Author(s):  
José Díaz-Reza ◽  
Jorge García-Alcaraz ◽  
Liliana Avelar-Sosa ◽  
José Mendoza-Fong ◽  
Juan Sáenz Diez-Muro ◽  
...  
Keyword(s):  
Least Squares ◽  
Structural Equation Model ◽  
Preventive Maintenance ◽  
Latent Variables ◽  
Structural Equation ◽  
Manufacturing Industry ◽  
Implementation Strategies ◽  
Equation Model ◽  
Total Productive Maintenance

The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance.

Sign in / Sign up

Export Citation Format

Share Document