Performance Of Statistical Process Control Research Articles

Data-Quality Assessment for Digital Twins Targeting Multi-Component Degradation in Industrial Internet of Things (IIoT)-Enabled Smart Infrastructure Systems

In the development of analytics for PHM applications, a lot of emphasis has been placed on data transformation for optimal model development without enough consideration for the repeatability of the measurement systems producing the data. This paper explores the relationship between data quality, defined as the measurement system analysis (MSA) process, and the performance of fault detection and isolation (FDI) algorithms within smart infrastructure systems. This research employs a comprehensive methodology, starting with an MSA process for data-quality evaluation and leading to the development and evaluation of fault detection and isolation (FDI) algorithms. During the MSA phase, the repeatability of a water distribution system’s measurement system is examined to characterise variations within the system. A data-quality process is defined to gauge data quality. Synthetic data are introduced with varying data-quality levels to investigate their impact on FDI algorithm development. Key findings reveal the complex relationship between data quality and FDI algorithm performance. Synthetic data, even with lower quality, can improve the performance of statistical process control (SPC) models, whereas data-driven approaches benefit from high-quality datasets. The study underscores the importance of customising FDI algorithms based on data quality. A framework for instantiating the MSA process for IIoT applications is also suggested. By bridging data-quality assessment with data-driven FDI, this research contributes to the design of digital twins for IIoT-enabled smart infrastructure systems. Further research on the practical implementation of the MSA process for edge analytics for PHM applications will be considered as part of our future research.

Use of Statistical Process Control Methods for Early Detection of Healthcare Facility-Associated Nontuberculous Mycobacteria Outbreaks: A Single-Center Pilot Study.

Nontuberculous mycobacteria (NTM) are emerging pathogens increasingly implicated in healthcare facility-associated (HCFA) infections and outbreaks. We analyzed the performance of statistical process control (SPC) methods in detecting HCFA NTM outbreaks. We retrospectively analyzed 3 NTM outbreaks that occurred from 2013 to 2016 at a tertiary care hospital. The outbreaks consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition, cardiac surgery-associated extrapulmonary MABC infection, and a bronchoscopy-associated pseudo-outbreak of Mycobacterium avium complex (MAC). We analyzed monthly case rates of unique patients who had positive respiratory cultures for MABC, non-respiratory cultures for MABC, and bronchoalveolar lavage cultures for MAC, respectively. For each outbreak, we used these rates to construct a pilot moving average (MA) SPC chart with a rolling baseline window. We also explored the performance of numerous alternative control charts, including exponentially weighted MA, Shewhart, and cumulative sum charts. The pilot MA chart detected each outbreak within 2 months of outbreak onset, preceding actual outbreak detection by an average of 6 months. Over a combined 117 months of pre-outbreak and post-outbreak surveillance, no false-positive SPC signals occurred (specificity, 100%). Prospective use of this chart for NTM surveillance could have prevented an estimated 108 cases of NTM. Six high-performing alternative charts detected all outbreaks during the month of onset, with specificities ranging from 85.7% to 94.9%. SPC methods have potential to substantially improve HCFA NTM surveillance, promoting early outbreak detection and prevention of NTM infections. Additional study is needed to determine the best application of SPC for prospective HCFA NTM surveillance in other settings.

Implementation of fuzzy rule-based algorithms in p control chart to improve the performance of statistical process control

In the statistical process control when the process is very sensitive and control limit shifts are the prime concerns, there fuzzy control charts can be a better solution. In decision making, extra “rather in control” and “rather out of control” decisions facilitate to find out the slight changes in the control chart. The automation of fuzzy control chart in the Excel VBA makes the data input and decisions making process faster. The vagueness of the data is removed as the charts deal with the triangular or trapezoidal area rather than some points in the control limits. Alongside the fuzzy control charts, Marcucci approach has been followed to find out the goodness-of-fit of the samples and to find out the effectiveness of fuzzy control charts.

Determining measurement error requirements to satisfy statistical process control performance requirements

Measurement error (ME) is a source of variation that may considerably affect the performance of control charts applied within a statistical process control scheme. While the consequences of ME on the actual performance of various control charts has been studied in recent publications, the more important inverse problem of how to specify ME requirements to achieve desirable control chart performance characteristics has not been addressed in the literature. In this paper, we develop guidelines regarding the formulation of specification limits to distribution-related ME characteristics. Both the Shewhart X control chart and the S 2 control chart are addressed. The main results of this paper are delivered in the form of expressions, from which permissible values for measurement-error bias and the standard deviation, required to achieve specified average run length characteristics, may be easily identified. Some related issues are addressed.

A fuzzy expert system for fault detection in statistical process control of industrial processes

Little work has previously been reported on the use of fuzzy logic within statistical process control when this is used for fault detection as part of quality control systems in industrial manufacturing processes. Therefore, the paper investigates the potential use of fuzzy logic to enhance the performance of statistical process control (SPC). The cumulative sum of the deviation in the monitored parameter is combined with the deviation in an attempt to discriminate between false alarms and real faults and, consequently, to improve the quality of the solution. Combinations of control rules are utilized and trained to cope with different inputs such that rejection of false alarms is achieved and quick detection of real faults is obtained. The design and implementation of this fuzzy expert system (FES) are presented, and a comparative rule based study is performed.