Journal of Quality Engineering and Management

Abstract This paper aims to develop a comprehensive model for the synergy of quality, sustainability, and agility in drug production systems. This model seeks to use data collected by automated inspection systems to improve product quality, plan preventive maintenance, and optimize production planning. Reviewing the literature on quality management, sustainability, and agility in production, an integrated model of quality, maintenance, and production (IQMP) was designed and developed using Bayesian approaches. The results show that the model can effectively improve product quality, and increase production stability and system agility against environmental changes and fluctuations. Using online inspection data in this model significantly increases its accuracy and efficiency in decisions related to quality, maintenance, and production planning. In addition to helping to improve the efficiency of production systems, this model can be used as a strategic tool for production and maintenance managers. By implementing this model in real conditions, companies can take advantage of the data collected by automatic inspection systems and make more detailed plans for maintenance and quality control.

Abstract In this research, a Multi-Objective model for reliability-centered preventive maintenance planning for a production system with parallel series components is developed. In this model, maintenance costs and cost of failures; including the cost of lost production, due to system shutdown are considered. In this way, this model plans preventive maintenance operations with the aim of increasing the system reliability level, with the lowest total cost. A binary nonlinear program is developed and a numerical example is solved for it and the results are discussed. To solve the proposed model, the Augmented Epsilon Constraint Method (AUGMECON) by GAMS software is used and the results are discussed. The results show the effect of preventive service planning on system failure rate and reliability. The proposed approach can be used to plan maintenance of industrial systems by considering the reliability related costs
 
 
 

Abstract In image monitoring, the information contained in an image is evaluated using control charts. A spatial control chart is a type of control chart in which the horizontal axis represents the position within the image. These charts are used to detect abnormal points in an image. Thermovision is a branch of machine vision that deals with the analysis of infrared images. Although infrared cameras have long been used in preventive maintenance to identify faulty equipment, overloads, and loose connections, the images captured by these cameras are usually analyzed only through empirical methods, and the few quantitative studies conducted in this area have not utilized control charts. In identifying defects in power distribution panels, several challenges must be considered, including the variety of equipment used in electrical panels, the lack of sufficient data to train pattern recognition models, autocorrelation, and the complex behavior of heat transfer by radiation, convection, and conduction. The insufficient data for training pattern recognition models such as neural networks makes spatial control charts relatively more advantageous than these methods. In this study, a combination of spatial control charts and robust regression is employed to detect defects in power distribution panels, and the detection capabilities of various control charts for identifying these defects are compared.