A Variable Control Chart Based on Process Capability Index Under Generalized Multiple Dependent State Sampling

This paper proposed a process capability index-based control chart under the new extended form of multiple-dependent state sampling (MDS) named generalized MDS (GMDS). The scheme is based on inner and outer control limits and utilizes the previous state of the samples. The performance comparisons of the proposed chart with the existing charts are made by using out-of-control ARL. The simulation study showed the superiority of the proposed chart over the existing PCI-based control charts under Shewhart and MDS schemes. An empirical illustration is also given to demonstrate the application of the proposed chart.11Ysciescopu

A Control Chart for Gamma Distribution using Multiple Dependent State Sampling

In this article, a control chart based on multiple dependent (or deferred) state sampling for the gamma distributed quality characteristic is proposed using the gamma to normal transformation. The proposed control chart has two pairs of control limits, which can be determined by considering the in-control average run length (ARL). The shift in the scale parameter of a gamma distribution is considered and the out-of-control ARL is evaluated. The performance of the proposed chart has been shown for different levels of the parameters of the proposed control chart. It is also shown that the proposed chart is better than the Shewhart chart in terms of ARLs. A case study with a real data has been included for the practical usage of the proposed scheme. ? 2017 KIIE.11Yscopuskc

A Time Truncated Moving Average Chart for the Weibull Distribution

A control chart of monitoring the number of failures is proposed with a moving average scheme, when the life of an item follows a Weibull distribution. A specified number of items are put on a time truncated life test and the number of failures is observed. The proposed control chart has been evaluated by the average run lengths (ARLs) under different parameter settings. The control constant and the test time multiplier are to be determined by considering the in-control ARL. It is observed that the proposed control chart is more efficient in detecting a shift in the process as compared with the existing time truncated control chart. ? 2013 IEEE.11Ysciescopu

A New S-2 Control Chart Using Multiple Dependent State Repetitive Sampling

The combined application of multiple dependent state sampling and the repetitive group sampling (RGS) is an efficient sampling scheme for industrial process monitoring as it combines the advantages of both the sampling schemes. In this paper, a new variance control chart has been proposed, when the interesting quality characteristic follows the normal distribution using the combination of these two efficient sampling schemes called multiple dependent state repetitive sampling. The control chart coefficients and parameters have been estimated through simulation for the in-control process by considering the target in-control average run lengths under different process settings. The efficiency of the proposed chart has been determined by computing the out-of-control ARL for different shift levels. The advantages of the proposed monitoring scheme have been discussed and compared with the existing RGS scheme and the single sampling scheme. A simulated example and a real industrial data have been included to demonstrate the application of the proposed monitoring scheme. It has been observed that the proposed chart is a valuable addition to the toolkit of the quality monitoring personnel.11Ysciescopu

EXTREME RANKED REPETITIVE SAMPLING CONTROL CHARTS

ABSTRACT In this paper, we proposed a new ranked data control chart using repetitive sampling criterion to increase the performance of detecting any shift in mean process. For the comparisons target, the average run length (ARL) of the proposed control chart based on repetitive extreme ranked set sampling computed using exact and estimated parameters. The results showed that the ARL affected negatively by the parameter estimation. Moreover, the performances of the proposed control chart is evaluated and compared with similar control chart that obtained by using different sampling schemes such as the simple random sampling, ranked set sampling, extreme ranked set sampling and repetitive ranked set sampling.. The results showed that the ranked data based control chart outperform the classical control chart in terms of the ARL

Mixed control charts using EWMA Statistics

In this paper, two mixed control charts are designed for process monitoring when the quality characteristic of interest follows a normal distribution. The mixed control chart starts with monitoring the number of non-conforming items but switches to monitoring using exponentially weighted moving average (EWMA) statistic or hybrid EWMA statistic when the decision is indeterminate with the attribute data. The average run lengths are calculated to evaluate the performance of the proposed control charts according to the mean shift. The performance of both control charts is compared with each other and with the existing control chart. Simulation study is given to demonstrate the efficiency of the proposed control charts.1150Ysciescopu

Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm

The key characteristics of test and measurement (T&M) manufacturing are short production runs, multi-product families and testing at multi-stations. Classical Shewhart control charts, namely x̄ chart and R chart have been widely used in statistical process control (SPC). Short production runs in T&M render these charts inefficacious as inherent meager data do not warrant meaningful control limits. Measurement errors increase the risks of false acceptance and rejection, thereby leading to consequences such as unnecessary process adjustment and loss of confidence in SPC. Industry practice allows the installation of Guard band, e.g., through Guide to the Expression of Uncertainty in Measurement (GUM) to reduce the width of acceptance limit, as an indirect way to compensate the measurement errors. Past related works which presented standardized observations technique is highly recommended due to its simplicity and practicality. However, the concern is that this technique requires sufficient data to calculate the control limits and it does not deal with the effect of measurement errors. Based on this premise, the research objective is to develop a modified SPC model by considering measurement uncertainty in modified control charts (Z chart and W chart) for short runs T&M process in multi-stations. The implementation of this model involves two phases. Phase I retrospective analysis computes the input parameters, such as the standard deviation of the measurement uncertainty, measurement target and estimate of the population standard deviation. Thereafter, Five-band setting and S–factor are proposed to estimate process standard deviation to maximize the the opportunity to detect assignable causes with low false-reject rate. Lastly, the modified Z chart and W chart are generated in Phase II using standardized observations technique that considers the measurement target and the estimated process standard deviations. Run tests based on Nelson’s rules to interpret the control charts. In terms of validation, three case studies, labeled as Case I, Case II and Case III were conducted with different ratios of standard deviations in measurement uncertainty and population to demonstrate the effectiveness of the proposed model. A complete year’s data samples were collected from products tested at multi-stations in a T&M manufacturing facility at Bayan Lepas, Penang. For Case I with the measurement error is negligible and does not affect the process standard deviation; the results indicate that there were no false alarm points found in all methods. In Case II with the measurement error may noticeably affect the process standard deviation, and the results show that the model with Five-band setting and S-factor reduced the false alarm rate by 100% in comparison to the classical Shewhart method, except for the Five-band setting which has a smaller sustained shift (25% false alarm) was falsely detected in station WH05. In Case III with the measurement error is relatively larger and appeared to be more significantly affecting the process standard deviation; the results reveal that both proposed methods performed well in modified Z and W charts, which reduced false alarm rate by 50% for station WH05, 0% for station WH06 and 37.5% for station WH07. As a conclusion, the research has proposed and demonstrated the modified SPC model can address the understudied issues caused by short production runs and measurement errors. The model is practical for T&M manufacturing to reduce false alarms and to prevent unnecessary process adjustment

A new CUSUM control chart under uncertainty with applications in petroleum and meteorology

Please read abstract in the article.http://www.plosone.orgpm2022Statistic

Wireless Fault Detection System for an Industrial Robot Based on Statistical Control Chart

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters

Sensor Fusion and Process Monitoring for Ultrasonic Welding of Lithium-ion Batteries.

Ultrasonic metal welding is used for joining lithium-ion batteries of electric vehicles. The quality of the joints is essential to the performance of the entire battery pack. Hence, the ultrasonic welding process that creates the joints must be equipped with online sensing and real-time process monitoring systems. This would help ensure the process to be operated under the normal condition and quickly address quality-related issues. For this purpose, this dissertation develops methods in process monitoring and fault diagnosis using online sensing signals for ultrasonic metal welding. The first part of this dissertation develops a monitoring algorithm that targets near-zero misdetection by integrating univariate control charts and a multivariate control chart using the Mahalanobis distance. The proposed algorithm is capable of monitoring non-normal multivariate observations with adjustable control limits to achieve a near-zero misdetection rate while keeping a low false alarm rate. The proposed algorithm proves to be effective in achieving near-zero misdetection in process monitoring in ultrasonic welding processes. The second part of the dissertation develops a wavelet-based profile monitoring method that is capable of making decisions within a welding cycle and guiding real-time process adjustments. The proposed within-cycle monitoring technique integrates real-time monitoring and within-cycle control opportunity for defect prevention. The optimal decision point for achieving the most benefit in defect prevention is determined through the formulation of an optimization problem. The effectiveness of the proposed method is validated and demonstrated by simulations and case studies. The third part of this dissertation develops a method for effective monitoring and diagnosis of multi-sensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multi-stream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The research presented in this dissertation can be applied to general discrete cyclic manufacturing processes that have online sensing and control capabilities. The results of this dissertation are also applicable or expandable to mission-critical applications when improving product quality and preventing defects are of high interests.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113405/1/graceguo_1.pd