The need for process monitoring in industry is ubiquitous. By monitoring process output, problems may be rapidly detected and corrected. However, in many industrial and medical applications observations are censored either due to inherent limitations or cost/time considerations. For example, when testing breaking strengths or failure times often a limited stress test is performed and only a sma...

The aircraft engine is a complex and repairable system, and the diversity of its failure modes increases the difficulty of operation reliability analysis. It is necessary to establish a dynamic relationship among monitoring information, failure mode and system reliability for achieving scientific reliability analysis for aircraft engines. This paper has used fuzzy support vector machine (FVSM) ...

This paper presents a supervised competitive learning network approach, called a fuzzy-soft learning vector quantization, for control chart pattern recognition. Unnatural patterns in control charts mean that there are some unnatural causes for variations in statistical process control (SPC). Hence, control chart pattern recognition becomes more important in SPC. In order to detect e€ ectively t...

Some of the most widely-investigated control charting techniques for autocorrelated data are based on time series residuals. If the mean shift in the autocorrelated process is a sudden step shift, the resulting mean shift in the residuals is time varying and has been referred to as the fault signature. Traditional residual based charts, such as a Shewhart, CUSUM, or EWMA on the residuals, do no...

As manufacturing quality has become a decisive factor in global market competition, statistical quality techniques such as Statistical Process Control (SPC) are widely used in industry. With advances in information, sensing, and data collection technology, large volumes of data are routinely available in processes employing Automatic Process Control (APC) and Engineering Process Control (EPC). ...

Manufacturing for a multitude of continuous processing applications in the era automation and ‘Industry 4.0? is focused on rapid throughput while producing products acceptable quality that meet customer specifications. Monitoring stability or statistical control key process parameters using data acquired from online sensors fundamental to successful manufacturing applications. This study addres...

INTRBackground and Objectives: Good medical nutrition therapy (MNT) is crucial to inpatients' health and treatment, and is part of routine hospital cares. Surgery ward is a highly danger-prone section in any hospital. The present study was conducted for a proactive risk analysis of nutrition and food distribution in Mashhad Qaem Hospital’ Women’s Surgery Ward in 2013 through health care failure...

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences su...

In intervention studies in which randomization to groups is not possible, researchers typically use quasi-experimental designs. Time series designs are strong quasi-experimental designs but are seldom used, perhaps because of technical and analytic hurdles. Statistical process control (SPC) is an alternative analytic approach to testing hypotheses about intervention effects using data collected...

Most radiation therapy machines are based on microwave linear accelerators. Manufacturing reliable accelerators and hence, reliable radiation therapy machines requires accurate, efficient, and well-controlled processes throughout the steps of electron accelerator manufacturing. Statistical Process Control (SPC) techniques are utilized at Siemens – Oncology Care Systems (OCS) to meet these requi...