Nothing Special   »   [go: up one dir, main page]

Singh et al., 2013 - Google Patents

Performance of CUSUM and EWMA charts for serial correlation

Singh et al., 2013

Document ID
5194254099125703235
Author
Singh S
Prajapati D
Publication year
Publication venue
The TQM Journal

External Links

Snippet

Purpose–The purpose of this paper is to study the effect of correlation on the performance of CUSUM and EWMA charts. The performance of the CUSUM and EWMA charts is measured in terms of average run lengths (ARLs) for the positively correlated data. The ARLs at …
Continue reading at www.emerald.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Serdio et al. Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations
Ainur et al. Sample size and non-normality effects on goodness of fit measures in structural equation models.
Kazemzadeh et al. Phase I monitoring of polynomial profiles
Psarakis et al. SPC procedures for monitoring autocorrelated processes
Abujiya et al. Enhancing the performance of combined Shewhart‐EWMA charts
Singh et al. Performance of CUSUM and EWMA charts for serial correlation
Dorj et al. A bayesian hidden markov model-based approach for anomaly detection in electronic systems
Park et al. A new multivariate EWMA control chart via multiple testing
Lee et al. Time-adaptive support vector data description for nonstationary process monitoring
Saghir et al. Monitoring process variation using modified EWMA
Niaki et al. Statistical monitoring of autocorrelated simple linear profiles based on principal components analysis
Bersimis et al. Methods for interpreting the out‐of‐control signal of multivariate control charts: A comparison study
Zarandi et al. A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts
Nidsunkid et al. The effects of violations of the multivariate normality assumption in multivariate Shewhart and MEWMA control charts
Ghute et al. A nonparametric signed-rank control chart for bivariate process location
Kaw et al. Improved methodology and set-point design for diagnosis of model-plant mismatch in control loops using plant-model ratio
Guh Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach
Izadbakhsh et al. Monitoring multinomial logistic profiles in Phase I using log-linear models
Shamsuzzaman et al. Design of EWMA control chart for minimizing the proportion of defective units
Zideh et al. Physics-informed convolutional autoencoder for cyber anomaly detection in power distribution grids
Lee et al. Multiple-fault diagnosis under uncertain conditions by the quantification of qualitative relations
Prajapati Effectiveness of conventional CUSUM control chart for correlated observations
Safarihamid et al. A joint-entropy approach to time-series classification
Kovářík et al. Implementing control charts to corporate financial management
Prajapati et al. Determination of level of correlation for products of pharmaceutical industry by using modified X-bar chart