Stochastic model of the gearbox pair vibration

The model of vibration signal of gearbox pair in the form of periodically correlated non-stationary random process is considered. It is shown that hidden periodicities in biperiodic correlated random process mean and covariance function, characterizing the vibrations of gearbox pair can be detected using the component and least square methods. Seven particular cases of the bi-rhythmic hidden periodicity for different modulation modes are analyzed.

NEW APPROACH FOR DETECTING SLIGHT SHIFT ON PROCESS MEAN IN CHIP RESISTOR PRODUCTION

This article highlights an alternative approach to identify a slight shift of the process mean for resistor production. Commonly, the industries use exponentially weighted moving average (EWMA) or classic I-MR charts for this kind of product. The parametric control chart consists of few underlying assumptions, especially observations that come from a normal distribution. A misleading conclusion was mainly made when non-normal distributed data were analyzed using a parametric control chart. A chip resistor manufacturing company provided the data for the study for future quality monitoring purposes. This study aims to determine a more appropriate analysis method according to the characteristics of the chip resistor data distribution. This article discusses the results of implementing one of the nonparametric methods that are still rarely known. The company’s current I-MR, corrective I-MR, parametric EWMA, and NPEWMA-SR control charts are used and compared in the analysis part. In the comparison, the I-MR control chart cannot detect a slight shift in the process mean. In contrast, the parametric EWMA control chart is not robust for data from a non-normal population. Since the data was not naturally from a normally distributed population, the nonparametric control chart is more appropriate, and the NPEWMA-SR control chart is suggested.

An Enhanced Performance to Monitor Process Mean with Modified Exponentially Weighted Moving Average – Sign Control Chart

The purpose of this research is to enhance performance for detecting a change in process mean by combining modified exponentially weighted moving average and sign control charts. This is nonparametric control chart which effective alternatives to the parametric control chart so called MEWMA-Sign. The nonparametric control chart can serve when process observations is deviated from normal distribution assumption. Generally, the performance of control charts are widely measured by average run length (ARL) divided into two cases; in control ARL (ARL0) and out of control ARL (ARL1). In this paper, the performance comparison is investigated when processes are non-normal distributions. The performance of the MEWMA-Sign is compared EWMA-Sign control chart by considering from a minimum value of ARL1. The numerical results found that the MEWMASign performs better than EWMA-Sign in order to detect a very small shift of mean process. Additionally, the real application of the MEWMA-Sign and EWMA-Sign are presented.