An enhanced double homogeneously weighted moving average control chart to monitor process location with application in automobile field

Anwar, Syed Masroor; Aslam, Muhammad; Zaman, Babar; Riaz, Muhammad

doi:10.1002/qre.2966

Cited by 18 publications

(15 citation statements)

References 42 publications

(60 reference statements)

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“…Research that considers measurement errors in adaptive synthetic charts is also suggested, as well as higher‐order autoregressive models AR(

p

p&gt;1

, and multivariate statistical process control such as the chart developed by Xue and Qiu 41 and Bersimis et al 42 . In addition, this methodology can be extended to combinations of charts, such as the one developed by Haq and Munir, 43 and memory control charts, that are widely used to detect small and moderate changes 44 …”

Section: Discussionmentioning

confidence: 99%

Statistical design of an adaptive synthetic X¯$\bar{X}$ control chart for autocorrelated processes

Quintero‐Arteaga

Peñabaena‐Niebles

Vélez

et al. 2022

Quality & Reliability Eng

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This paper aims to design a variable parameters synthetic X¯$\bar{X}$ control chart for first‐order AR(1) autocorrelated data following a Gaussian process. To improve the statistical performance in detecting small changes and maintaining a low false alarm rate, the variable parameters X¯$\bar{X}$ control chart was combined with the Conforming Run Length (CRL) sub‐chart to determine when to implement tight or relaxed control and intervene to identify an assignable cause. The statistical design of the proposed chart was performed under a discrete‐time Markov chain approach and a non‐linear programming mathematical model to obtain, using a genetic algorithm (GA), the values of the design parameters that minimise the average time to signal the out‐of‐control state (ATS1$\text{ATS}_{1}$). A sensitivity analysis was performed to examine the behaviour of the ATS1$\text{ATS}_{1}$ in the face on changes in the parameters. Also, a performance evaluation was conducted to compare the proposed chart with other adaptive synthetic charts, the synthetic X¯$\bar{X}$ control chart and the chart developed by Costa and Machado. Our results indicate that this proposal is faster to detect small changes in the process mean considering the autocorrelation of the data.

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“…Research that considers measurement errors in adaptive synthetic charts is also suggested, as well as higher‐order autoregressive models AR(

p

p&gt;1

Section: Discussionmentioning

confidence: 99%

Statistical design of an adaptive synthetic X¯$\bar{X}$ control chart for autocorrelated processes

Quintero‐Arteaga

Peñabaena‐Niebles

Vélez

et al. 2022

Quality & Reliability Eng

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“…The data comprise 30 samples, each size n = 5 given in Table 7. In order to implement the proposed HEWMA1 and HEWMA2 control along with CH and CEWMA charts, following [23,48], an upward shift of size δ = 1.25 is introduced artificially after sample number 16 [49,50]. At ARL 0 = 200, the smoothing parameter values λ 1 = 0.1, λ 2 = 0.05 are used, which provides the width of the the HEWMA1, and HEWMA2, CH and CEWMA charts, respectively, given as L + HEWMA1 = 1.365, L + HEWMA2 = 1.399, L + CH = 1.303 and L + CEWMA = 2.198.…”

Section: Important Points Of the Studymentioning

confidence: 99%

New Hybrid EWMA Charts for Efficient Process Dispersion Monitoring with Application in Automobile Industry

Liu¹,

Khan²,

Rasheed³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The EWMA charts are the well-known memory-type charts used for monitoring the small-to-intermediate shifts in the process parameters (location and/or dispersion). The hybrid EWMA (HEWMA) charts are enhanced version of the EWMA charts, which effectively monitor the process parameters. This paper aims to develop two new uppersided HEWMA charts for monitoring shifts in process variance, i.e., HEWMA1 and HEWMA2 charts. The design structures of the proposed HEWMA1 and HEWMA2 charts are based on the concept of integrating the features of two EWMA charts. The HEWMA1 and HEWMA2 charts plotting statistics are developed using one EWMA statistic as input for the other EWMA statistic. A Monte Carlo simulations method is used as a computational technique to determine the numerical results for the performance characteristics, such as average run length (ARL), median run length, and standard deviation run length (SDRL) for assessing the performance of the proposed HEWMA1 and HEWMA2 charts. In addition, to evaluate the overall performance of the proposed HEWMA1 and HEWMA2 charts, other numerical measures consisting of the extra quadratic loss (EQL), relative average run length (RARL), and performance comparison index (PCI) are also computed. The proposed HEWMA1 and HEWMA2 charts are compared to some existing charts, such as CH, CEWMA, HEWMA, AEWMA HHW1, HHW2, AIB-EWMA-I, and AIB-EWMA-II charts, on the basis aforementioned numerical measures. The comparison reveals that the proposed HEWMA1 and HEWMA2 charts achieve better detection ability against the existing charts. In the end, a real-life data application is also provided to enhance the implementation of the proposed HEWMA1 and HEWMA2 charts practically.

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“…p as the time series, where p was the length of the time series. Hence, the MA of period q at time l was calculated using Equation ( 8) [35][36][37][38][39]:…”

Section: Moving Averagementioning

confidence: 99%

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Hamdi

Hamam

2022

Sensors

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Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.

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An enhanced double homogeneously weighted moving average control chart to monitor process location with application in automobile field

Cited by 18 publications

References 42 publications

Statistical design of an adaptive synthetic X¯$\bar{X}$ control chart for autocorrelated processes

Statistical design of an adaptive synthetic X¯$\bar{X}$ control chart for autocorrelated processes

New Hybrid EWMA Charts for Efficient Process Dispersion Monitoring with Application in Automobile Industry

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

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