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"Root Cause Analysis"
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Root cause analysis : a step-by-step guide to using the right tool at the right time
\"This book covers root cause analysis, with an emphasis on using quality tools to empirically investigate issues. It starts with the theoretical background and then provides step-by-step instructions for performing root cause analysis using various quality tools. The book explains how to use PDCA together with scientific methods and quality tools when investigating quality failures. The tools and concepts presented are appropriate for both the manufacturing industry and service industry\"-- Provided by publisher.
Book
Using Safety-II and resilient healthcare principles to learn from Never Events
Conduct a secondary analysis of root cause analysis (RCA) reports of Never Events to determine whether and how Safety-II/resilient healthcare principles could contribute to improving the quality of investigation reports and therefore preventing future Never Events.
Qualitative and quantitative retrospective analysis of RCA reports.
A large acute healthcare Trust in London.
None.
None.
Quality of RCA reports, robustness of actions proposed.
RCA reports had low-to-moderate effectiveness ratings and low resilience ratings. Reports identified many system vulnerabilities that were not addressed in the actions proposed. Using a Safety-II/resilient healthcare lens to examine work-as-done and misalignments between demand and capacity would strengthen analysis of Never Events.
Safety-II/Resilient Healthcare concepts can increase the quality of RCA reports and focus attention on prospectively strengthening systems. Recommendations for incorporating Safety-II concepts into RCA processes are provided.
Journal Article
Sensitivity Analysis and Power for Instrumental Variable Studies
In observational studies to estimate treatment effects, unmeasured confounding is often a concern. The instrumental variable (IV) method can control for unmeasured confounding when there is a valid IV. To be a valid IV, a variable needs to be independent of unmeasured confounders and only affect the outcome through affecting the treatment. When applying the IV method, there is often concern that a putative IV is invalid to some degree. We present an approach to sensitivity analysis for the IV method which examines the sensitivity of inferences to violations of IV validity. Specifically, we consider sensitivity when the magnitude of association between the putative IV and the unmeasured confounders and the direct effect of the IV on the outcome are limited in magnitude by a sensitivity parameter. Our approach is based on extending the Anderson-Rubin test and is valid regardless of the strength of the instrument. A power formula for this sensitivity analysis is presented. We illustrate its usage via examples about Mendelian randomization studies and its implications via a comparison of using rare versus common genetic variants as instruments.
Journal Article
An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference
In recent years, fault detection and diagnosis for industrial processes have been rapidly developed to minimize costs and maximize efficiency by taking advantages of cheap sensors and microprocessors, data analysis and artificial intelligence methods. However, due to the nonlinear and dynamic characteristics of industrial process data, the accuracy and efficiency of fault detection and diagnosis methods have always been an urgent problem in industry and academia. Therefore, this study proposes an adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window kernel principle component analysis (KPCA) and information geometric causal inference (IGCI). The proposed scheme has three main contributions. Firstly, a research scheme combining moving window KPCA with adaptive threshold is presented to handle the nonlinear and dynamic characteristics of complex industrial processes. Then, the multiobjective evolutionary algorithm is employed to select the optimal hyperparameters for fault detection, which not only avoids the blindness of hyperparameters selection, but also maximize model accuracy. Finally, the IGCI-based fault root-cause analysis method can help field operators to take corrective measures in time to resume the normal process. The proposed scheme is tested by the Tennessee Eastman platform. Its results show that this scheme has a good performance in reducing the faulty false alarms and missed detection rates and locating fault root-cause.
Journal Article
Root Cause Failure Analysis - A Guide to Improve Plant Reliability
Process equipment and piping systems are essential for plant availability and performance. Regularly exposed to hazardous service conditions and damage mechanisms, these critical plant assets can result in major failures if not effectively monitored and assessed-potentially causing serious injuries and significant business losses. When used proactively, Root Cause Failure Analysis (RCFA) helps reliability engineers inspect the process equipment and piping system before any abnormal conditions occur. RCFA is equally important after a failure happens: it determines the impact of a failure, helps control the resultant damage, and identifies the steps for preventing future problems. This book offers readers clear understanding of degradation mechanisms of process equipment and the concepts needed to perform industrial RCFA investigations. This comprehensive resource describes the methodology of RCFA and provides multiple techniques and industry practices for identifying, predicting, and evaluating equipment failures. Divided into two parts, the text first introduces Root Cause Analysis, explains the failure analysis process, and discusses the management of both human and latent error.
eBook
Failure modes analysis and assessment of aluminum-based metallic matrix composites printed with LPBF technology
Laser powder bed fusion (LPBF) of metal matrix composites (MMC) is a popular additive manufacturing technique due to its design flexibilities, reduced waste, improved properties, and shorter fabrication lead times. However, in order to enhance the related performance, a number of essential factors are to be carefully considered to make the product get up to standard. Significant advancements have been made in the application of this fabrication method, and the present paper focuses on the use of metal matrix composites (MMCs), with particular emphasis on silicon carbide (SiC)-reinforced aluminum matrix composites (AMCs). This paper commences with a review of the AMCs with the objective of summarizing the LPBF process issues and main failures using quality management tools, thereby simplifying the understanding of the process for easier failures Root-Cause Analysis (RCA). Then, quality management tools such as the Ishikawa standpoint, 5-whys analysis, and SWOT matrix were exploited to interpret the methodologies involved in the LPBF, come up with root causes of possible defects and anomalies during fabrication, and find the advantages, opportunities, and possible threats associated with the fabrication of AMC using LPBF, respectively; risk insight was formally assessed by adopting Fault Tree Analysis (FTA) and Failure Mode Effects and Criticality Analysis (FMECA); the proposed approach results in determining the most prominent failures in terms of failures mechanism developments (FTA) and Pareto/ABC classification of the FMECA criticality. Furthermore, a systemic scheme was proposed to better understand the process and risk exposition using an optimization loop for illustrating the areas of multi-criteria and multi-optimization niches as an integrated high-order system.
Journal Article
Big GCVAE: decision-making with adaptive transformer model for failure root cause analysis in semiconductor industry
Pre-trained large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), especially for the task of text summarization, generation, and question answering. The success of LMs can be attributed to the attention mechanism introduced in Transformer models, which have outperformed traditional recurrent neural network models (e.g., LSTM) in modeling sequential data. In this paper, we leverage pre-trained causal language models for the downstream task of failure analysis triplet generation (FATG), which involves generating a sequence of failure analysis decision steps for identifying failure root causes in the semiconductor industry. In particular, we conduct extensive comparative analysis of various transformer models for the FATG task and find that the BERT-GPT-2 Transformer (Big GCVAE), fine-tuned on a proposed Generalized-Controllable Variational AutoEncoder loss (GCVAE), exhibits superior performance in generating informative latent space by promoting disentanglement of latent factors. Specifically, we observe that fine-tuning the Transformer style BERT-GPT2 on the GCVAE loss yields optimal representation by reducing the trade-off between reconstruction loss and KL-divergence, promoting meaningful, diverse and coherent FATs similar to expert expectations.
Journal Article
Validity of root cause analysis in investigating adverse events in psychiatry
Root cause analysis (RCA), imported from high-reliability industries into health two decades ago, is the mandated methodology to investigate adverse events in most health systems. In this analysis, we argue that the validity of RCA in health and in psychiatry must be established, given the impact of these investigations on mental health policy and practice.
Journal Article
Are root cause analyses recommendations effective and sustainable? An observational study
Abstract
Objective
To assess the strength of root cause analysis (RCA) recommendations and their perceived levels of effectiveness and sustainability.
Design
All RCAs related to sentinel events (SEs) undertaken between the years 2010 and 2015 in the public health system in Victoria, Australia were analysed. The type and strength of each recommendation in the RCA reports were coded by an expert patient safety classifier using the US Department of Veteran Affairs type and strength criteria.
Participants and setting
Thirty-six public health services.
Main outcome measure(s)
The proportion of RCA recommendations which were classified as ‘strong’ (more likely to be effective and sustainable), ‘medium’ (possibly effective and sustainable) or ‘weak’ (less likely to be effective and sustainable).
Results
There were 227 RCAs in the period of study. In these RCAs, 1137 recommendations were made. Of these 8% were ‘strong’, 44% ‘medium’ and 48% were ‘weak’. In 31 RCAs, or nearly 15%, only weak recommendations were made. In 24 (11%) RCAs five or more weak recommendations were made. In 165 (72%) RCAs no strong recommendations were made. The most frequent recommendation types were reviewing or enhancing a policy/guideline/documentation, and training and education.
Conclusions
Only a small proportion of recommendations arising from RCAs in Victoria are ‘strong’. This suggests that insights from the majority of RCAs are not likely to inform practice or process improvements. Suggested improvements include more human factors expertise and independence in investigations, more extensive application of existing tools that assist teams to prioritize recommendations that are likely to be effective, and greater use of observational and simulation techniques to understand the underlying systems factors. Time spent in repeatedly investigating similar incidents may be better spent aggregating and thematically analysing existing sources of information about patient safety.
Journal Article