Joint optimisation of x bar control chart &
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This document discusses joint optimization of statistical process control (SPC) and preventive maintenance. It describes different types of control charts that can be used for SPC, including X-bar charts. Preventive maintenance aims to avoid equipment failures by conducting maintenance activities regularly. The document presents a model to optimize SPC sampling intervals, sample size, control limits and preventive maintenance schedules to minimize total operational costs. The model utilizes information from quality control charts to inform maintenance decisions.
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The document discusses intelligent maintenance strategies using Siemens' SIMATIC PCS 7 Maintenance Station software. It describes how the software provides a centralized interface for maintenance personnel to monitor asset status across an entire automated plant and process chain. Key information on components is standardized and organized hierarchically for easy access. This allows maintenance to move from a reactive to proactive approach of preventing issues before failures occur to improve productivity and availability.
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Control charts monitor process variation over time to distinguish between common and special causes. Information from control charts helps improve processes by addressing special causes. Control charts aim to prevent errors like treating common variation as special causes or vice versa. When a process is in control, its control chart will not indicate any out-of-control conditions and it will contain only common causes. If common variation is too large, the process must be altered. Different control charts like p, R, x-bar, C, and capability indices like Cp, CPL, CPU, Cpk are used to monitor different quality characteristics.
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This document provides an overview of control charts, which are statistical process control tools used to monitor manufacturing processes. It discusses the key elements of control charts, including center lines, control limits, data collection sections and how points inside or outside the control limits indicate whether a process is in or out of statistical control. The document also describes different types of control charts for variables and attributes data and their functions in improving process performance over time. Specifically, it focuses on X-bar and R charts, which are used when measurements are collected in subgroups, and how statistical data is required to construct these charts, including determining the number of subgroups.
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Production equipment maintenance planning at Commercial Metals Company Steel Texas is currently done weekly without the benefit of equipment performance data. A group of consultants was tasked with 1) creating a simulation model of the shredding process that incorporates equipment failure modes and costs of downtime and 2) providing recommendations for preventive maintenance planning. The consultants analyzed downtime data, identified the most frequent and problematic sources of scheduled and unscheduled downtimes, and used this information to construct a prioritization plan intended to help with preventive maintenance planning and reduce costs from equipment failures.
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Control charts are statistical tools used in statistical process control to monitor and control a process. They help determine if a process is stable and predictably meeting specifications. There are two main types of control charts - attributes control charts which monitor defects, and variables control charts which monitor measured variables. Variables charts include X-bar and R charts which monitor process average and variability over time. Control charts are useful for quality improvement and ensuring a process stays in statistical control.
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This chapter discusses statistical quality control and control charts. It covers the following key points:
1. Statistical process control uses tools like control charts to reduce variability and identify assignable causes of variation.
2. Control charts monitor a process over time and detect when the process moves out of the state of statistical control.
3. There are variables and attributes control charts. Variables charts like X-bar and R charts are for continuous data, while attributes charts like P and U charts are for discrete data.
4. Rational subgrouping aims to maximize differences between subgroups while minimizing within-subgroup differences to better detect assignable causes.
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Measures of corrective maintenance are: Mean Corrective Maintenance Time , Median Active Corrective Maintenance Time, Maximum Active Corrective Maintenance Time.
Then different models : a system that can either be in up (operating) or down (failed) state; a system that can either be operating normally or failed in two mutually exclusive failure modes; a system that can either be operating normally, operating in degradation mode, or failed completely; a two identical-unit redundant (parallel) system. At least one unit must operate normally for system success.
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Control charts monitor process variation over time to distinguish between common and special causes. Information from control charts helps improve processes by addressing special causes. Control charts aim to prevent errors like treating common variation as special causes or vice versa. When a process is in control, its control chart will not indicate any out-of-control conditions and it will contain only common causes. If common variation is too large, the process must be altered. Different control charts like p, R, x-bar, C, and capability indices like Cp, CPL, CPU, Cpk are used to monitor different quality characteristics.
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This chapter discusses statistical quality control and control charts. It covers the following key points:
1. Statistical process control uses tools like control charts to reduce variability and identify assignable causes of variation.
2. Control charts monitor a process over time and detect when the process moves out of the state of statistical control.
3. There are variables and attributes control charts. Variables charts like X-bar and R charts are for continuous data, while attributes charts like P and U charts are for discrete data.
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Measures of corrective maintenance are: Mean Corrective Maintenance Time , Median Active Corrective Maintenance Time, Maximum Active Corrective Maintenance Time.
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Abstract The deployment of statistical process control (SPC) in manufacturing environments is a prominent global phenomenon. Statistical Process Control is largely used in industries for monitoring the process parameters. It is a standard method for visualizing and controlling processes on the basis of measurements of randomly selected samples. The decisions about what needs to be improved, the possible methods to improve it, and the steps to take after getting results from the charts are all made by humans and based on wisdom and experience. The statistical process control described in this paper gives the details about the SPC, its advantages and limitation, applications and information regarding the control charts. Keywords: Statistical Process Control, Control chart, 5M’s, Capability Indices.
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Statistical Process Control (SPC) is a method of using statistical analysis to monitor and control a process. SPC helps determine whether a process is stable or unpredictable by comparing data to control limits on charts. There are control charts for variables (data that can be measured numerically) and attributes (data classified into categories). The document discusses types of control charts like p charts for proportions and u charts for defects per unit. It also covers process capability indices, which measure how well a process produces outputs within specifications. The goal of SPC is to detect non-routine variations and make processes as consistent as possible through continuous improvement.
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Statistical process control (SPC) techniques apply statistical methods to measure and analyze variation in manufacturing processes. SPC uses control charts to distinguish between common cause variation inherent to the process and special cause variation that can be assigned to a specific reason. Control charts monitor process data over time against statistical control limits. Process capability analysis compares process variation to product specifications to determine if the process is capable of meeting specifications. Key metrics like Cp, Cpk and Cpm indices quantify a process's capability relative to the specifications. For a process to have a valid capability analysis, it must meet assumptions of statistical control, normality, sufficient representative data, and independence of measurements.
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statistical quality control, the use of statistical methods in the monitoring and maintaining of the quality of products and services. One method, referred to as acceptance sampling, can be used when a decision must be made to accept or reject a group of parts or items based on the quality found in a sample. A second method, referred to as statistical process control, uses graphical displays known as control charts to determine whether a process should be continued or should be adjusted to achieve the desired quality.Statistical process control (SPC) or statistical quality control (SQC) is the application of statistical methods to monitor and control the quality of a production process. This helps to ensure that the process operates efficiently, producing more specification-conforming products with less waste scrap. SPC can be applied to any process where the "conforming product" (product meeting specifications) output can be measured. Key tools used in SPC include run charts, control charts, a focus on continuous improvement, and the design of experiments. An example of a process where SPC is applied is manufacturing lines.
SPC must be practiced in two phases: The first phase is the initial establishment of the process, and the second phase is the regular production use of the process. In the second phase, a decision of the period to be examined must be made, depending upon the change in 5M&E conditions (Man, Machine, Material, Method, Movement, Environment) and wear rate of parts used in the manufacturing process (machine parts, jigs, and fixtures).
An advantage of SPC over other methods of quality control, such as "inspection," is that it emphasizes early detection and prevention of problems, rather than the correction of problems after they have occurred.
In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product. SPC makes it less likely the finished product will need to be reworked or scrapped.
Statistical process control was pioneered by Walter A. Shewhart at Bell Laboratories in the early 1920s. Shewhart developed the control chart in 1924 and the concept of a state of statistical control. Statistical control is equivalent to the concept of exchangeability.
Statistical process control is appropriate to support any repetitive process, and has been implemented in many settings where for example ISO 9000 quality management systems are used, including financial auditing and accounting, IT operations, health care processes, and clerical processes such as loan arrangement and administration, customer billing etc. Despite criticism of its use in design and development, it is well-placed to manage semi-automated data governance of high-volume data processing operations, for example in an enterprise data warehouse, or an enterprise data quality management system.In manufacturing, quality is defined as conformance to specification. However, no two products or characteristics are ever exactly the same.
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This document discusses statistical process control and quality improvement techniques. It defines quality and explains that quality improvement aims to reduce variability in processes. It describes seven major statistical process control tools including control charts. Control charts graph process data over time and are used to distinguish between chance variation and assignable causes of variation. The document outlines the basic principles of control charts, including how to establish control limits and select rational subgroups. It also covers analysis of patterns on control charts and specific chart types like X-bar and R charts for variables data and P, U, and C charts for attributes data. Finally, it discusses process capability, measurement systems capability, and control chart performance in terms of average run length.
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The document discusses Statistical Process Control (SPC), which uses statistical techniques to monitor and control manufacturing processes by distinguishing between normal variation due to common causes and abnormal variation due to special causes. SPC involves collecting process data, analyzing it using control charts, and taking corrective action if special causes of variation are detected to ensure processes are stable and operating as intended. The goal of SPC is to improve quality, reduce waste, and help manufacturers meet customer requirements.
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Control charts are statistical tools used to determine whether a manufacturing or business process is stable and predictable or experiencing unpredictable variation. Walter Shewhart invented control charts in the 1920s at Bell Labs to monitor telephony processes. Control charts plot process data over time along with an average line and upper and lower control limits set at 3 standard deviations from the average. As long as data points remain within the control limits, the process is considered in a state of statistical control and predictable. Points outside the limits suggest an unpredictable special cause of variation that requires investigation. Control charts allow detection of changes in a process's natural variation that may require adjusting process parameters.
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Introduction to control charts
Control charts are used to monitor process variables over time in various industries and organizations. They tell us when a process is out of control by showing data points outside the control limits. When this occurs, those closest to the process must find and eliminate the special cause of variation to prevent it from happening again. Control charts have basic components like a centerline and upper and lower control limits. They are constructed by selecting a process, collecting data, calculating statistics and control limits, and plotting the results over time. Control charts come in two types - variables charts for continuous measurements and attributes charts for counting items. Common and special causes can lead to variations monitored by these charts.
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Joint optimisation of x bar control chart &
- 1. JOINT OPTIMISATION OF X BAR CONTROL CHART & PREVENTIVE MAINTENANCE (YISHA XIANG*) - SANTOSH PIDGE
- 2. Control chart A control chart is graphically representation of the collected information. It detects the variation in processing. Types of control cahrt, 1) x -chart 2) R -chart 3) 6- chart
- 3. Statistical process control A. Shewhart developed the control chart In 1924 and concept of A state statistical Control. Extend the quality of the product by using statistics. Statistics process control help to determine the amount of variation. To make sure process in a state of control.
- 4. Variation in Quality: Some sort of variation the two atoms is bound to be there. In fact is an integral part of any manufacturing process. This difference in characteristics known as variation. This variation may be due to substandard quality of raw material, carelessness on the part of operator, fault in machinery systems etc.
- 5. Types Of Variations A. Variation due to “chance causes” B. Variation due “assinable causes”
- 6. Variation due to chance causes/ common causes This Variation is NOT due to defect in machine, raw material or any other factors . Behave in random manner. Negligible but investable. The process is said to be under the state of statistical control.
- 7. Variation due to assignable causes : Difference in quality of raw material Difference in machines Difference in operator Difference of time
- 9. Specification and control limits : No items in the world can be a true copy of another item. It is not expressed in absolute values but in terms of a range . For Eg. The diameter of a pen is expected by its manufacture not as 7mm but as 7mm±0.05 Thus, the diameter of pen produced by the manufacture can vary from 6.95mm to 7.05mm.
- 10. Setting Control Chart :
- 11. How Control Limits are useful..
- 12. X- Bar Control Chart
- 13. Preventive Maintenance: The primary goal of maintenance is to avoid or mitigate the consequences of failure of equipment. This may be by preventing the failure before it actually occurs which Planned Maintenance and Condition Based Maintenance help to achieve Maintenance activities include partial or complete overhauls at specified periods, oil changes, lubrication, minor adjustments, and so on. In addition, workers can record equipment deterioration so they know to replace or repair worn parts before they cause system failure. For eg . servicing of our vehicle
- 14. Corrective Maintenance : Corrective maintenance is a maintenance task performed to identify, isolate, and rectify a fault so that the failed equipment, machine, or system can be restored to an operational condition within the tolerances or limits established for in-service operations. for eg. Replacement of component after failure
- 15. Conclusion: ●This paper presents an integrated model for the joint optimization of statistical process control (sampling intervals, sample size and control limit) and preventive maintenance Information from quality control charts is utilized to facilitate maintenance decisions. ●It is assumed that preventive maintenance is imperfect, meaning that preventive maintenance improves the process from its current state to any non-worse state with given probabilities. ●A cost model is formulated for the simultaneous optimization of statistical process control and maintenance activities with the objective of minimizing the total expected operational cost rate
- 16. References ●Alardhi, M., Labib, A.W., 2008. Preventive maintenance scheduling of multicogeneration plants using integer programming. Journal of the Operational Research Society 59, 503–509. ●Ben-Daya, M., 1999. Integrated production maintenance and quality model for imperfect processes. IIE Transactions 31 (6), 491–501.