control-chart

Statistical Process Control Charts Library for Humans

A toolkit for statistical process control using SQL

Python module for creating Shewhart statistical process control charts

Anomaly Detection: Nelson Rules for Control Chart - Python implementation

A Grafana panel plugin for monitoring process stability over time. Supports Xbar-R, Xbar-S, and XmR charts with automatic calculation of control limits.

Shewhart Control Chart and CUSUM (CUmulative SUM) Control Chart

Fast Online Changepoint Detection via Functional Pruning CUSUM statistics

Histogram panel with SPC control limits, bell curves, and capability indices.

Statistical Process Monitoring in Julia

control chart pattern recognition with tensorflow, experiment conducted for Summer 2017 NSF REU at New Mexico State University

Social Networks Monitoring

This project is my thesis about computing a more accurate process capability index under dynamic process with mean shift and variance change.

Application designed for visualizing and monitoring control chart data in real-time. It leverages PyQt5 for an interactive and user-friendly interface, allowing users to dynamically explore data trends, and MySQL connector to retrieve data from database.

Process behavior charts (control charts / SPC) for Python — XmR, Xbar-S, histograms, capability, Taguchi loss. Implements Bishop's Variance Analysis System with chart math validated against published reference results.

Univariate Fast Initial Response Statistical Process Control with Taut Strings

Six Sigma Yellow Belt project applying the DMAIC methodology to optimize customer interaction handling time by eliminating process inefficiencies, standardizing workflows and improving operational efficiency.

This repository contains the implementation of a multivariate control chart with dimension reduction techniques, namely Factor Analysis of Mixed Data (FAMD) and Autoencoder. The control chart is designed for detecting network intrusions using network data traffic.

Python-based SPC and process capability analysis for simulated manufacturing quality data.

Within this documentation, this project is conducted to analyze the Fly Ash and Bottom Ash (FABA) contents that will be used as the alternative raw material in cement producing using MEWMV and Spatial Signed Rank MEWMA (SSRM) control chart.