Statistical process control (SPC) requires statistical methodologies that detect changes in the pattern of data over time. The common methodologies, such as Shewhart, cumulative sum (cusum), and exponentially weighted moving average (EWMA) charting, require the in-control values of the process parameters, but these are rarely known accurately. Using estimated parameters, the run length behavior...

Abstract The Moving Average method is the most common filter in Digital Signal Processing, optimal for reducing unstable value fluctuations due to interference by maintaining accurate readings. Therefore it often used signal coding context of time (Smith, 1999). kind moving average Exponentially Weighted (EWMA) which applied a sequence random variables, calculating weighted applying weight that...

Rapidly obtaining accurate dense disparity maps has been the focus of stereo matching research. At present, approaches that achieve superior require a large amount computation, which is not suitable for practical applications. To address this issue, paper proposes an efficient local method based on adaptive exponentially weighted moving average filter and simple linear iterative clustering segm...

In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary,multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpo...

Fault detection is a necessary component to perform ongoing monitoring of photovoltaic plants and helps in their safety, maintainability, productivity with the desired performance. In this study, an innovative technique introduced by amalgamating Latent Variable Regression (LVR) methods, namely Principal Component (PCR) Partial Least Square (PLS), Triple Exponentially Weighted Moving Average (T...

Article history: Received 28 March 2010 Received in revised form 14 February 2012 Accepted 16 April 2012 Available online 25 April 2012

Exponentially weighted moving average control charts and neural networks were used for oestrus detection in dairy cows. The analysis involved 373 cows, each with one verified oestrus event. Model inputs were the traits activity, measured by pedometer, and the period (days) since last oestrus. In total 10,386 records were available, which were partitioned into training and validation subsets to ...

Abstract A single exponentially weighted moving average (EWMA) chart is effectively used for monitoring the process mean and variance simultaneously. In this paper, three single EWMA-type control charts including sum of square EWMA (SS-EWMA), maximum EWMA (MaxEWMA), and EWMA-semicircle (EWMA-SC) charts are compared under Lorenzen and Vance’s cost model integrating Taguchi’s loss function. The o...