Economic and economic-statistical designs of an control chart for two-unit series systems with condition-based maintenance

Liu, Liping; Yu, Miaomiao; Ma, Yizhong; Tu, Yiliu

doi:10.1016/j.ejor.2012.11.031

Cited by 49 publications

(18 citation statements)

References 28 publications

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“…Then, the training of this methodology was carried out with the quality technical staff, and, later, the data were collected (D: do). The evaluation was made based on the data collected (C: Check), using the statistical process control, as proposed by Liu et al (2013). Then, an improvement plan was drawn up for billets harvest to improve or standardize the operation (A: action) ( Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…Phase 3: Check (C) -to verify the data collected, we used statistical process control tools, which are described by the individual charts, which monitor the process over time. In addition, the process capacity analysis (Cpk, Cp, Pp and Cpm) was carried out to detect whether it meets the standards established throughout the operation, when the process was considered stable (Liu et al, 2013).…”

Section: Phase 2: Do (D) -mentioning

confidence: 99%

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Failure mode and effect analysis (FMEA) in mechanized harvest of sugarcane billets

et al. 2018

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The quality control is used for the evaluation of processes and products, and it is important to detect and reduce variability and in operations management can be applied in mechanized harvesting of sugarcane billets. In this sense, this study aimed to identify critical quality indicators and, if necessary, to develop a continuous improvement plan for harvest of sugarcane billets. For the survey and definition of critical quality indicators the FMEA (Failure Mode and Effect Analysis) was applied, after the formation of the quality technical team. Evaluations of the collected variables were through control charts of individual values and the process capability indexes. There are 8 critical quality indicators in the billets harvesting process; the cutting height, the damage index, the percentage of non-viable buds and operating speed have greater importance for analysis due to the risk priority index and the easiness of obtaining data for analysis. The development of the improvement plan aims to reduce the variability due to crop billets, thus, making it able to perform the operation within the required quality standards.

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Section: Methodsmentioning

confidence: 99%

Section: Phase 2: Do (D) -mentioning

confidence: 99%

Failure mode and effect analysis (FMEA) in mechanized harvest of sugarcane billets

et al. 2018

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“…Liu and Yu [37] studied the economic and economic-statistical designs of X control chart for a two-identical unit system. In addition, monitoring the mean value and the variance of quality characteristic simultaneously is also worth investigating for the future integrated model.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated model of statistical process control and maintenance based on the delayed monitoring

Yin

Zhang

Zhu

et al. 2015

Reliability Engineering & System Safety

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“…Xiang [22] proposed a model for a production process that deteriorates according to a discrete-time Markov chain and further provided a breakthrough in designing an efficient solution algorithm in obtaining analytical results. Liu et al [23] used a five-state continuous time Markov chain for a two-unit series system to find the optimal control chart parameters by minimizing the average maintenance costs. Zhang et al [24] proposed a delayed maintenance policy, estimated the state probabilities during the delayed period by Bayesian theory, and used Markov method to model the monitoring-maintenance process.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Joint Economic Design of CUSUM Control Chart and Age-Based Imperfect Preventive Maintenance Policy

Chen

Pan

2018

Mathematical Problems in Engineering

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The close relationship between statistical process control and maintenance has attracted lots of researchers to focus on the jointly economic design of control chart (a main tool of statistical process control) and preventive maintenance policy, and much progress has been made in this field. However, in the existing literatures, the X- chart is used most, and other charts are rarely considered. In this paper, the economic design of CUSUM chart and age-based imperfect preventive maintenance policy is presented. The process is considered as a multiphase system, and a recursive algorithm is used to model each phase. Besides, a sampling policy under the non-Markovian deterioration assumption is employed, and an age-based imperfect preventive maintenance policy is used. An optimization model with the objective of minimizing the expected cost per unit time is constructed to obtain the near-optimal solution of decision variables: the age of the machine for maintenance, the number of age-based maintenances, sample size, sampling intervals, and the decision interval coefficient and reference value coefficient of CUSUM chart. The solution procedure of the model is provided. Also, sensitivity analysis is performed on the decision variables for each of the various parameters.

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Economic and economic-statistical designs of an control chart for two-unit series systems with condition-based maintenance

Cited by 49 publications

References 28 publications

Failure mode and effect analysis (FMEA) in mechanized harvest of sugarcane billets

Failure mode and effect analysis (FMEA) in mechanized harvest of sugarcane billets

An integrated model of statistical process control and maintenance based on the delayed monitoring

Joint Economic Design of CUSUM Control Chart and Age-Based Imperfect Preventive Maintenance Policy

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