Analysis of acoustic emission data for bearings subject to unbalance

Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance

Evaluating capability of a bivariate zero-inflated poisson process

A zero-inflated Poisson (ZIP) distribution is commonly used for modelling zero-inflated process data with single type of defect, and for developing appropriate tools for instituting statistical process control of manufacturing processes. However, in reality, such manufacturing scenarios are very common where more than one type of defect can occur. For example, occurrences of defects like solder short circuits (shorts) and absence of solder (skips) are very common on printed circuit boards. In literature, different forms of bivariate zero-inflated Poisson (BZIP) distributions are proposed, which can be used for modelling the manufacturing scenarios where two types of defects can occur. Control charts are designed for monitoring for such processes using BZIP models. Although evaluation of capability is an integral part of statistical process control of a manufacturing process, researchers have given very little effort on this aspect of zero-inflated processes. Only a few articles attempted to evaluate the capability of a univariate zero-inflated process and no work is reported on evbaluating capability of a bivariate zero-inflated process. In this paper, a methodology for measuring capability of a bivariate zero-inflated process is presented. The proposed methodology is illustrated using two case studies.&nbsp

On Shewhart Control Charts for Zero-Truncated Negative Binomial Distributions

The negative binomial distribution (NBD) is extensively used for thedescription of data too heterogeneous to be fitted by Poissondistribution. Observed samples, however may be truncated, in thesense that the number of individuals falling into zero class cannot bedetermined, or the observational apparatus becomes active when atleast one event occurs. Chakraborty and Kakoty (1987) andChakraborty and Singh (1990) have constructed CUSUM andShewhart charts for zero-truncated Poisson distribution respectively.Recently, Chakraborty and Khurshid (2011 a, b) have constructedCUSUM charts for zero-truncated binomial distribution and doublytruncated binomial distribution respectively. Apparently, very littlework has specifically addressed control charts for the NBD (see, forexample, Kaminsky et al., 1992; Ma and Zhang, 1995; Hoffman, 2003;Schwertman. 2005).The purpose of this paper is to construct Shewhart control chartsfor zero-truncated negative binomial distribution (ZTNBD). Formulaefor the Average run length (ARL) of the charts are derived and studiedfor different values of the parameters of the distribution. OC curvesare also drawn

A weighted cumulative sum (WCUSUM) to monitor medical outcomes with dependent censoring

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108011/1/sim6139.pd

Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs

The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as supplementary material