In some multivariate statistical control applications, the data of the process cannot be precise and defined linguistically in practice. Using multivariate control charts in such situations with non-precise data leads to misleading results. In this paper, a new neural network-based monitoring scheme is presented by considering fuzzy multivariate multinomial data. The proposed approach is also a...

One of the existing problems of multi-attribute process monitoring is the occurrence of high number of false alarms (Type I error). Another problem is an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, we address both of these problems and consider monitoring correlated multi-attributes proce...

abstract. in the case of process capability index several methods are identified. in this paper ,a new process capability index using fuzzy number and fuzzy probability concept, in order to remove the weakness of other famous method is suggested. after introduction of fuzzy index in univariate case, fuzzy multivariate process capability index is investigated. finally, this new method is compare...

Discretization is an important preprocessing technique in data mining tasks. Univariate Discretization is the most commonly used method. It discretizes only one single attribute of a dataset at a time, without considering the interaction information with other attributes. Since it is multi-attribute rather than one single attribute determines the targeted class attribute, the result of Univaria...

the most well-known uni-arribute control chart used to monitor the number of nonconformities per unit is the shewhart type c-chart. in this paper, a new method is proposed in an attempt to reduce the false alarm rate in the c-chart. to do this, the decision on beliefs (dob) concept is first uti [1] corresponding author e-mail: [email protected]   lized to design an iterat...

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they either cannot deliver satisfactory detection performance or cannot c...

Process safety and environment pollution demands are continuously increasing in the process industry. Apart from that, requirements regarding final product quality and production efficiency are higher and higher [1]. This can be achieved by applying advanced process monitoring and control techniques. Process control is heavily dependent on the quality of the data, so it is crucial to measure as...