A Synergetic Intelligent Fault Prognosis Framework to support Product Life Cycle Considering Environmentally Conscious Production

ABSTRACT: The recent problems of increased oil prices, global warming, and environmental pollution highlighted the urgent need for cost effective, reliable and environmentally conscious production process. Hence to achieve clean and healthy production, the chemical process industry strives to continually improve their preparedness and awareness through adaptive inference logic by effectively extracting and signaturing cascade clues from past experiences and predicting the possible scenarios of risk and its sources. These sources are usually related to equipment life cycle, starting from suppliers’ evaluation and ending by its salvage or disposal. Methods are thus needed to effectively utilize data collected and knowledge available in order to make the right decision at the right moment. Despite the considerable technological advancement, these decisions still depend heavily on human expertise, which is, although very valuable, are subject to errors, and may be lost due to death, retirement or resignation. Therefore, an integrated equipment health management system that takes into consideration the equipment life cycle, which leads to environmentally conscious production, is proposed. In order to manage and develop environmentally conscious plant operation, it is essential to provide a synergetic intelligent fault diagnosis and prognosis framework embedded in systematic interoperable platform with respect to product life cycle, process safety and environmental measures. The proposed system employs a systematic expert knowledge structure considering operation execution, process safety and control, warranty policies, and environmental issues during equipment life cycle to assist the user in evaluating uncertainties and the process of decision making

Estimating Unit Cost for Dental Services: Evidence from Community Health Centers in Iran

Objectives: Cost information can help policy makers to set user fees, public and private tariffs and budgets and also to conduct an economic evaluation to provide health care with acceptable quality and affordable price. This study aimed to do a cost analysis for the Iranian Comprehensive Health Centers (CHCs), to estimate the unit cost of different dental services. Methods: This was a cross-sectional study. Capital and recurrent cost information of three urban CHCs in Mashhad was collected. Cost identification was based on the provider’s perspective. The step-down costing method was adopted from Konte and Waker and applied in five steps: defining cost centers, identification of operational activities, assigning inputs to cost centers, allocate all costs to final centers, and compute unit costs. Results: In dental services, the most important cost driver was human resources that comprised 69% of the total cost. The unit cost of a relative K for dental care was 12,189 Rials (1 USD: 31,407 Rials as in 2016). Accordingly, the unit cost for different dental services varied from 182,834 Rials for dental radiography to 1,589,570 Rials for class II composite restoration. The mean cost of a dental visit for examination and diagnosis estimated 247,436 Rials. Conclusion: Comparison between the estimated unit cost and the current dental tariffs reveled considerable differences. Integration of dental services to primary health care in the Iranian CHCs would result in the economies of scope.&nbsp

A Synergetic Intelligent Fault Prognosis Framework to support Product Life Cycle Considering Environmentally Conscious Production

ABSTRACT: The recent problems of increased oil prices, global warming, and environmental pollution highlighted the urgent need for cost effective, reliable and environmentally conscious production process. Hence to achieve clean and healthy production, the chemical process industry strives to continually improve their preparedness and awareness through adaptive inference logic by effectively extracting and signaturing cascade clues from past experiences and predicting the possible scenarios of risk and its sources. These sources are usually related to equipment life cycle, starting from suppliers’ evaluation and ending by its salvage or disposal. Methods are thus needed to effectively utilize data collected and knowledge available in order to make the right decision at the right moment. Despite the considerable technological advancement, these decisions still depend heavily on human expertise, which is, although very valuable, are subject to errors, and may be lost due to death, retirement or resignation. Therefore, an integrated equipment health management system that takes into consideration the equipment life cycle, which leads to environmentally conscious production, is proposed. In order to manage and develop environmentally conscious plant operation, it is essential to provide a synergetic intelligent fault diagnosis and prognosis framework embedded in systematic interoperable platform with respect to product life cycle, process safety and environmental measures. The proposed system employs a systematic expert knowledge structure considering operation execution, process safety and control, warranty policies, and environmental issues during equipment life cycle to assist the user in evaluating uncertainties and the process of decision making