Availability analysis of a cooking oil production line (original) (raw)
Related papers
International Journal of Scientific Research in Science, Engineering and Technology, 2018
In the performance analysis of production systems by using the traditional methods of engineering the knowledge of machine reliability factors is assumed to be precisely known. The current study entitled performance evaluation of food industry in India. To analyze and determine the availability of plant a case study has been undertaken from Moga Nestle food private limited industry in India. Various studies evaluating the performance of automated production systems with the help of modeling and simulation and analytical methods have always given priority to steady state performance as compared to transient performance. Production systems in which such kind of situations arises include systems with dysfunctional states and deadlocks, not stable queuing systems. This research work presents an approach for analyzing the performance of unreliable manufacturing systems that take care of uncertain machine factor estimates. The method that is being proposed is on the basis of Markov chain ...
Periodica Polytechnica Social and Management Sciences, 2022
The purpose of this article is to choose a maintenance procedure for the critical equipment of a forging production line with five machines. The research method is quantitative modelling and simulation. The main research technique includes retrieving time between failure and time to repair data and find the most likely distribution that has produced the data. The most likely failure rate function helps to define the maintenance strategy. The study includes two kinds of maintenance policies, reactive and anticipatory. Reactive policies include emergency and corrective procedures. Anticipatory policies include predictive and preventive ones combined with a total productive maintenance management approach. The most suitable combination for the first three machines is emergency and corrective choice. For the other machines, a combination of total productive maintenance and a predictive approach is optimal. The study encompasses the case of a serial production manufacturing line and maxi...
Automated Production Line Reliability Analysis of the Crankshaft Manufacturing Process
Advances in Science and Technology Research Journal, 2022
The producer focuses on producing parts that match the customer's requirements during manufacturing the automotive engine parts. One of the essential automotive engine parts is a crankshaft used to translate movement from the pistons to the car axle. The crankshaft is a complex shape and diffi cult to produce accurate dimensions during the machining processes. Many machines are used to create the crankshaft. Therefore, many defects happen during the machining process, reducing reliability and increasing the manufacturing process's production cost. This paper focuses on analyzing failure data and reliability of the crankshaft production line that occurs during the manufacturing process of one year. The common failure associated with the manufacturing process were ring screw, unbalanced crankshaft, broken drill screw, hub machining error, mains part machining error, and setup error. The paper aimed to determine and analyze the best failure fi t between the distribution methods, such as Weibull, normal, lognormal, and exponential. Also, the reliability, hazard rate, surviving quantity, and failure density were calculated to evaluate the current situation and predict the reliability of the production line. Results proved the skewness of the data was positive equal to 3.33; the last months had the highest production failure rate, which is 53.8%, the normal method had a proper distribution of data depending on the Anderson-Darling (adj) values which is 1.367 when it compared with other methods, the normal method had the best fi tting result depended on failure percentage, from 1% to 95% of the crankshafts production are expected to fail between 47.2676 and 1149.85 months respectively. The reliability of the production line decreased with manufacturing time increased. To reduce the failure and increase reliability, the maintenance system must be supported, analyze the sources that cause failure and downtime of the production line, continue the employee training system on an ongoing basis, and support the production line with modern technology. All analytical results and suggestions could be valuable to the production line to improve reliability and reduce the manufacturing process's failure.
Accurate modeling of repair time in two-machine production lines
2008 IEEE International Conference on Emerging Technologies and Factory Automation, 2008
In this paper, the repair process for fault-tolerant industrial production lines is investigated. The type of failure may significantly affect the repair time because of travel time, customs, … A modified Markov model is developed that takes into account these different repair times. The availability of a two-machine production line is then calculated using this model and is compared to the availability obtained from conventional models for the same production line. A case study shows that the results from the two models may vary significantly indicating that a more accurate description of the repair process was indeed necessary.
Reliability Analysis for Refinery Plants
The Journal of King Mongkut's University of Technology North Bangkok
One of the main problems in compiling Preventive Maintenance (PM) plans for large-scale industries consisting of many subunits in the production system is the criterion used in the selection of machines and equipment to be maintained. The criteria most used are the failure frequency of each machine and equipment and the maintenance cycle time specified in the manual, as preventive maintenance is considered to be mainly time-based. In some cases, Failure Mode and Effect Analysis (FMEA) technique is applied in the selection of machines and equipment by considering individual and independent machines in the subsystems. The overall mechanisms of the machines and equipment were not considered, which may affect the production capacity. Therefore, this research applies reliability engineering techniques to find the overall reliability of machines and equipment in the production system of oil refinery models and determine the machine in the subsystem that affects that overall reliability. This in turn causes optimal preventive maintenance planning for machines and equipment in order to achieve the maximum efficiency for the oil refinery process. This research begins by studying the procedures of oil refinery models, then creating a reliability block diagram of the subsystems to find the reliability of the machines and equipment within each subsystem. Afterwards, the overall reliability of the production system will be determined, which leads to arranging the reliability of machines and equipment in the subsystems in ascending order. This develops into the preventive maintenance planning process so that the refinery process achieves its maximum efficiency.
2020
Markov chains models are widely applied in industrial engineering. In this paper, Markov chains are used to achieve modeling scenarios for the maintenance parameters and performance evaluation of production systems. The Markov chains are built with the queuing theory and are well-known for their power of representation by given a small computing effort. This paper is focused on reveal some random behaviors with the help of modeling scenarios. Our contribution consists in the development of two modeling scenarios. In the first scenario, the availability of a system with the setup phase and scraps is calculated, then the machine behavior in a period of a month is evaluated using failure and repair rates generated by Linear Congruential Generator. In the second scenario, the availability of a system with the setup phase without scraps is designed and evaluated.
Continuous-Time Markov Chain Model of Repairable Machine: Application to Asalaya Sugar Factory
This study aims at dealing with continuous time Markov chain model application on the fault time of two machines (Mill troup-Boiler), important machine in Asalaya Sugar Factory in season (January/2019-Decmber/2019), which affiliated to Sudanese Sugar Company.The study conduces that the failure time of machines follows Exponential distribution estimation fault distribution.Failure time represent transition matrix in the Continuous time Markov chain. The probability of the machine in operating state is greater than the probability of the machine in a fail state.The high probability of the machine in operating state and the mean time of a machine stay estimated by 4 hours in state (1) (operating state) meanwhile the machine stay in state (0) (fail state) estimated by one hour which indicates the efficiency of the maintenance unit, it is clear that, the probability of available time to repair machines when it fault approximately (0.80), this indicates that the machines has high availability.
Failure data analysis for preventive maintenance scheduling of a bottling company production system
Oke Adekola Olayinka, 2021
Equipment breakdown adds to the cost of production and considerably affect the overall equipment efficiency in automated lines due to unplanned downtime. Preventive maintenance with appropriate actions has been considered to enhance products quality, equipment reliability and minimize the probability of system brake down or failure. To this end, this study conducted a reliability status of nine packaging facilities, from the perspective of existing failure data of production system in the Nigerian multinational bottling plant. Failure data of the production system were stratified and analyzed to achieve the failure interval of each of the facilities and the subsystems. Stratification of failure data resulted to an established input format that fitted the Pareto chart analysis, Weibull Distributions and Reliability/Failure Time analysis. The results showed that the facility with minimum value of reliability was filler machine. A standby filler system was therefore recommended in order to prevent unnecessary idleness of the other facilities especially when the production target is high. The study concluded that, analysis of downtime in a production/manufacturing system assisted in predicting the likely failure interval and hence a preventive maintenance scheduled was proposed.
Reliability analysis of an automated pizza production line
Journal of Food Engineering, 2005
We present a statistical analysis of failure data of an automated pizza production line, covering a period of four years. The analysis includes the computation of descriptive statistics of the failure data, the identification of the most important failures, the computation of the parameters of the theoretical distributions that best fit the failure data, and the investigation of the existence of autocorrelations and cross correlations in the failure data. The analysis is meant to guide food product machine manufacturers and bread & bakery products manufactures improve the design and operation of their production lines. It can also be valuable to reliability analysts and manufacturing systems analysts, who wish to model and analyze real manufacturing systems.