Cumulative sum quality control charts design and applications

Includes bibliographical references (pages 165-169).Classical Statistical Process Control Charts are essential in Statistical Control exercises and thus constantly obtained attention for quality improvements. However, the establishment of control charts requires large-sample data (say, no less than I 000 data points). On the other hand, we notice that the small-sample based Grey System Theory Approach is well-established and applied in many areas: social, economic, industrial, military and scientific research fields. In this research, the short time trend curve in terms of GM( I, I) model will be merged into Shewhart and CU SUM two-sided version control charts and establish Grey Predictive Shewhart Control chart and Grey Predictive CUSUM control chart. On the other hand the GM(2, I) model is briefly checked its of how accurate it could be as compared to GM( I, 1) model in control charts. Industrial process data collected from TBF Packaging Machine Company in Taiwan was analyzed in terms of these new developments as an illustrative example for grey quality control charts

Inclined ergometer to enhance FES-assisted indoor rowing exercise performance

Improving the FES-assisted indoor rowing exercise (FES-rowing) performance enables the spinal cord injury (SCI) people to perform hybrid FES-exercise in a higher level of intensity. High level of exercise volume and intensity can play a big role in prevention of cardiovascular disease, type 2 diabetes and obesity which is a significant threat to the health of people with chronic SCI. FES-rowing can be enhanced to achieved the high level exercise through the arrangement of the rowing ergometer. In this paper, the performance of FES-rowing using an adjustable inclined rowing ergometer is investigated. Two different methods to enhance the FES-rowing performance using inclined ergometer are implemented. A model of the adjustable inclined ergometer and humanoid are developed using the Visual Nastran (vN4D) software environment and validated by the experimental work. Fuzzy logic control is implemented to control the knee and elbow trajectories for smooth rowing manoeuvre. The generated level of electrical stimulations for activation of quadriceps and hamstrings muscles are recorded and analysed. The FES-rowing efficiency for both methods have been defined and illustrated. The results show the inclined ergometer with upper body effort is the best performance in enhancing the FES-rowing

Optimizing the Cost of Integrated Model for Fuzzy Failure Weibull Distribution Using Genetic Algorithm

This research applies the fuzzy concept in development of an integrated model (Statistical process control and Maintenance management) with Weibull distribution for Exponentially Weighted Moving Average (EWMA) control chart. Since sample data may contain uncertainties coming from measurement systems and environment conditions, fuzzy number is used to inspect these suspicions. Moreover, the Weibull distribution with fuzzy scale parameters is considers, and the genetic algorithm approach is used to determine the optimal values of six variables that minimize the fuzzy hourly cost. Finally, the fuzzy hourly cost is transformed to crisp number by the centroid defuzzification.This research applies the fuzzy concept in development of an integrated model (Statistical process control and Maintenance management) with Weibull distribution for Exponentially Weighted Moving Average (EWMA) control chart. Since sample data may contain uncertainties coming from measurement systems and environment conditions, fuzzy number is used to inspect these suspicions. Moreover, the Weibull distribution with fuzzy scale parameters is considers, and the genetic algorithm approach is used to determine the optimal values of six variables that minimize the fuzzy hourly cost. Finally, the fuzzy hourly cost is transformed to crisp number by the centroid defuzzification

Integrating Multiobjective Optimization With The Six Sigma Methodology For Online Process Control

Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today’s businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict. Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives. This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model – a well-known inventory management model – that is formally defined and specifies various process costs as objective functions. The proposed iv SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3σ)-based and six sigma (6σ)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense

Multivariate Control Chart based on Neutrosophic Hotelling T2 Statistics and Its Application

Under classical statistics Hotelling 〖 T〗^2 control chart is applied when the observations of quality characteristics are precise, exact, or crips data. However, in reality, under uncertain conditions, the observations are not necessarily precise, exact, or indeterminacy. As a consequence, the classical Hotelling〖 T〗^2control chart is not appropriate to monitor the process for this condition. To tackle this situation, we proposed new Hotelling 〖 T〗^2 monitoring scheme based on a fuzzy neutrosophic concept. Neutrosophic is the generalization of fuzzy. It is used to handle uncertainty using indeterminacy. The combination of statistics based on neutrosophic Hotelling 〖 T〗^2 and classical Hotelling 〖 T〗^2 control chart will be proposed to tackle indeterminacy observations. The proposed Hotelling 〖 T〗^2 statistics, its call neutrosophic Hotelling 〖 T〗^2 (T_N^2 ) control chart. This chart involves the indeterminacy of observations, its call neutrosophic data and will be expressed in the indeterminacy interval. T_N^2 control charts consist T_N^2 lower chart and T_N^2 upper chart. In this paper, the neutrosophic Hotelling T^2will be applied to individual observations of glass production and will be compared by using classical Hotelling T^2 control chart. Based on T_N^2 control charts of glass production, nine points fall outside of 〖UCL〗_N of lower control chart and 24 points outside from 〖UCL〗_N  of upper control chart. Whereas using classical Hotelling T^2 control chart, just one point outside frim UCL. From the comparison, it concluded that the neutrosophic Hotelling T^2 control chart is more suitable for the indeterminacy of observations

An attribute oriented induction based methodology to aid in predictive maintenance: anomaly detection, root cause analysis and remaining useful life

Predictive Maintenance is the maintenance methodology that provides the best performance to industrial organisations in terms of time, equipment effectiveness and economic savings. Thanks to the recent advances in technology, capturing process data from machines and sensors attached to them is no longer a challenging task, and can be used to perform complex analyses to help with maintenance requirements. On the other hand, knowledge of domain experts can be combined with information extracted from the machines’ assets to provide a better understanding of the underlying phenomena. This thesis proposes a methodology to assess the different requirements in relation to Predictive Maintenance. These are (i) Anomaly Detection (AD), (ii) Root Cause Analysis (RCA) and (iii) estimation of Remaining Useful Life (RUL). Multiple machine learning techniques and algorithms can be found in the literature to carry out the calculation of these requirements. In this thesis, the Attribute Oriented Induction (AOI) algorithm has been adopted and adapted to the Predictive Maintenance methodology needs. AOI has the capability of performing RCA, but also possibility to be used as an AD system. With the purpose of performing Predictive Maintenance, a variant, Repetitive Weighted Attribute Oriented Induction (ReWAOI ), has been proposed. ReWAOI has the ability to combine information extracted from the machine with the knowledge of experts in the field to describe its behaviour, and derive the Predictive Maintenance requirements. Through the use of ReWAOI, one-dimensional quantification function from multidimensional data can be obtained. This function is correlated with the evolution of the machine’s wear over time, and thus, the estimation of AD and RUL has been accomplished. In addition, the ReWAOI helps in the description of failure root causes. The proposed contributions of the thesis have been validated in different scenarios, both emulated but also real industrial case studies.Enpresei errendimendu hoberena eskaintzen dien mantentze metodologia Mantentze Prediktiboa da, denbora, ekipamenduen eraginkortasun, eta ekonomia alorretan. Azken urteetan eman diren teknologia aurrerapenei esker, makina eta sensoreetatiko datuen eskuraketa jada ez da erronka, eta manentenimendurako errekerimenduak betetzen laguntzeko analisi konplexuak egiteko erabili daitezke. Bestalde, alorreko jakintsuen ezagutza makinetatik eskuratzen den informazioarekin bateratu daiteke, gertakarien gaineko ulermena hobea izan dadin. Tesi honetan metodologia berri bat proposatzen da, Mantentze Prediktiboarekin lotura duten errekerimenduak betearazten dituena. Ondorengoak dira: (i) Anomalien Detekzioa (AD), (ii) Erro-Kausaren Analisia (RCA), eta (iii) Gainontzeko Bizitza Erabilgarriaren (RUL) estimazioa. Errekerimendu hauen kalkulua burutzeko, ikasketa automatikoko hainbat algoritmo aurkitu daitezke literaturan. Tesi honetan Attribute Oriented Induction (AOI) algoritmoa erabili eta egokitu da Mantentze Prediktiboaren beharretara. AOI-k RCA estimatzeko ahalmena dauka, baina AD kalkulatzeko erabilia izan daiteke baita ere. Mantentze Prediktiboa aplikatzeko helburuarekin, AOI-rentzat aldaera bat proposatu da: Repetitive Weighted Attribute Oriented Induction (ReWAOI ). ReWAOI-k alorreko jakintsuen ezagutza eta makinetatik eskuratutako informazioa bateratzeko ahalmena dauka, makinen portaera deskribatu ahal izateko, eta horrela, Mantentze Prediktiboaren errekerimenduak betetzeko. ReWAOI-ren erabileraren ondorioz, dimentsio bakarreko kuantifikazio funtzioa eskuratu daiteke hainbat dimentsiotako datuetatik. Funztio hau denboran zehar makinak duen higadurarekin erlazionatuta dago, eta beraz, AD eta RUL-aren estimazioak burutu daitezke. Horretaz gain, ReWAOI-k hutsegiteen erro-kausaren deskribapenak eskaintzeko ahalmena dauka. Tesian proposatutako kontribuzioak hainbat erabilpen kasutan balioztatu dira, batzuk emulatuak, eta beste batzuk industria alorreko kasu errealak izanik.El Mantenimiento Predictivo es la metodología de mantenimiento que mejor rendimiento aporta a las organizaciones industriales en cuestiones de tiempo, eficiencia del equipamiento, y rendimiento económico. Gracias a los recientes avances en tecnología, la captura de datos de proceso de máquinas y sensores ya no es un reto, y puede utilizarse para realizar complejos análisis que ayuden con el cumplimiento de los requerimientos de mantenimiento. Por otro lado, el conocimiento de expertos de dominio puede ser combinado con la información extraída de las máquinas para otorgar una mejor comprensión de los fenómenos ocurridos. Esta tesis propone una metodología que cumple con diferentes requerimientos establecidos para el Mantenimiento Predictivo. Estos son (i) la Detección de Anomalías (AD), el Análisis de la Causa-Raíz (RCA) y (iii) la estimación de la Vida Útil Remanente. Pueden encontrarse múltiples técnicas y algoritmos de aprendizaje automático en la literatura para llevar a cabo el cálculo de estos requerimientos. En esta tesis, el algoritmo Attribute Oriented Induction (AOI) ha sido seleccionado y adaptado a las necesidades que establece el Mantenimiento Predictivo. AOI tiene la capacidad de estimar el RCA, pero puede usarse, también, para el cálculo de la AD. Con el propósito de aplicar Mantenimiento Predictivo, se ha propuesto una variante del algoritmo, denominada Repetitive Weighted Attribute Oriented Induction (ReWAOI ). ReWAOI tiene la capacidad de combinar información extraída de la máquina y conocimiento de expertos de área para describir su comportamiento, y así, poder cumplir con los requerimientos del Mantenimiento Predictivo. Mediante el uso de ReWAOI, se puede obtener una función de cuantificación unidimensional, a partir de datos multidimensionales. Esta función está correlacionada con la evolución de la máquina en el tiempo, y por lo tanto, la estimación de AD y RUL puede ser realizada. Además, ReWAOI facilita la descripción de las causas-raíz de los fallos producidos. Las contribuciones propuestas en esta tesis han sido validadas en distintos escenarios, tanto en casos de uso industriales emulados como reales

Mixed control charts using EWMA Statistics

In this paper, two mixed control charts are designed for process monitoring when the quality characteristic of interest follows a normal distribution. The mixed control chart starts with monitoring the number of non-conforming items but switches to monitoring using exponentially weighted moving average (EWMA) statistic or hybrid EWMA statistic when the decision is indeterminate with the attribute data. The average run lengths are calculated to evaluate the performance of the proposed control charts according to the mean shift. The performance of both control charts is compared with each other and with the existing control chart. Simulation study is given to demonstrate the efficiency of the proposed control charts.1150Ysciescopu

Neutrosophic multivariate EWMA control chart

The MEWMA chart is one of the traditional multivariate charts which are widely employed in inspecting the quality of manufacturing and services. This chart is created through monitoring the small shifts of mean vectors of variable quality characteristics. Often in practice, the measurement of a quality characteristic produces uncertain, incomplete values, so that ambiguous numbers are obtained. In this condition, a neutrosophic-based control chart can overcome the problem resulting from the ambiguous data. The paper’s objective is to construct a new multivariate monitoring scheme based on a neutrosophic chart, namely the neutrosophic Multivariate EWMA (NMEWMA). Furthermore, the performance of the new multivariate monitoring scheme is evaluated in detecting process shifts employing the Average Run Length (ARL) and Standard Deviation Run Length (SDRL). This control chart is an innovation in the quality monitoring of uncertain data. The research result obtained indicates that the NMEWMA chart performs better than the MEWMA in finding the small mean shifts as well as in the real case application

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.