A damage detection problem is cast in the context of a statistical pattern recognition paradigm. In particular, this paper focuses on applying statistical process control methods referred to as "control charts" to vibration-based damage detection. First, an auto-regressive (AR) model is fitted to the measured time histories from an undamaged structure. Residual errors, which quantify the differ...

The continuous detection and correction of unnatural process behaviours, due to special causes of variations, is a basic task in manufacturing to maintain any process stable and predictable. For this purpose, updated tools for Statistical Process Control have been studied so far, like the use of Artificial Neural Networks for pattern recognition in control charts. In this paper a preliminary st...

This paper formulates Shewhart mean (X-bar) and range (R) control charts for diagnosis and interpretation by artificial neural networks. Neural networks are trained to discriminate between samples from probability distributions considered within control limits and those which have shifted in both location and variance. Neural networks are also trained to recognize samples and predict future poi...

In the previous two Chapters, we proposed fraction nonconforming nonparametric control charts to monitor process location and process variability. It is also shown that performance of proposed control charts are superior to that of the Shewhart X and sign charts. If process is running in an in-control state for a long period, it will reach in steady-state mode. In order to characterize long-ter...

Statistical process control (SPC) has been used to achieve and maintain control of various processes in industry (Stoumbos, Reynolds, Ryan, and Woodall 2000). The control chart is a primary SPC tool to monitor process variability and promote quality improvement by means of detecting process shifts requiring corrective actions. As a graphical monitor, control charts generally contain a centerlin...

Acceptance control charts (ACC), as an effective tool for monitoring highly capable processes, establish control limits based on specification limits when the fluctuation of the process mean is permitted or inevitable. For designing these charts by minimizing economic costs subject to statistical constraints, an economic-statistical model is developed in this paper. However, the parameters of s...

Statistical process control is a very useful method to improve the product quality and reduce reworks and scraps. In a production environment, control charts are the most important tool to determine whether a process is in-control or out-of-control. Control charts are to detect the occurrence of the shifts in a process rapidly so that their causes can be found and the necessary corrective actio...

the classical method of process capability analysis necessarily assumes that collected data are independent; nonetheless, some processes such as biological and chemical processes are autocorrelated and violate the independency assumption. many processes exhibit a certain degree of correlation and can be treated by autoregressive models among which the autoregressive model of order one (ar (1)) ...