SUMMARY AND CONCLUSIONSThis paper studies the vibration signals obtained from a reactor coolant pump (RCP) of a power plant. A discrete wavelet transform (DWT) is used to decompose the vibration signals generated within a time window into approximation (cA2) and detailed coefficients (cD2 and cD1). Additionally, twelve features consisting of some statistics are generated from each of the DWT coefficients or sub-bands, resulting in a total of 36 features per vibration signal. These features are used as inputs for the principal component analysis (PCA). From the PCA results, only 4 principal components (PCs) explain most variability (about 96%) of the features. The variability was found in features X2, X13, X14, X25, and X26 which correspond to the 75th percentile of cA2, 25th percentile of cD2, 75th percentile of cD2, 25th percentile of cD1, and 75th percentile of cD1 respectively. Therefore, we can monitor these small number of 4 PCs variables instead of the original 36 PCs. These 4` PCs can provide the behavior of normal vibration signals and how to detect abnormal signals in the future.
SUMMARY & CONCLUSIONSThis paper describes the results of a survey that was conducted to determine the knowledge and skills that reliability engineering practitioners utilize in their everyday work. The survey consisted of twenty-two (22) questions and was conducted in person at two (2) events. The first event was the 13th Annual Training Summit event held by the Society of Reliability Engineers (SRE) – Huntsville Chapter, in Huntsville, Alabama on November 29-30, 2021. The second event was the 68th Annual RAMS in Tucson, Arizona on January 24-27, 2022. In all, there were fifty (50) respondents to the survey with two being determined ineligible for lack of working in a reliability engineering role. Results showed that the vast majority of reliability engineers who participated in the survey were interested in having better data science skills for themselves and were also interested in having a teammate with data science skills. Approximately 56% of practitioners that were surveyed showed preference for having a reliability engineer with data science skills on their team. By contrast, 26% preferred a data scientist with reliability engineering knowledge and 14% had no preference. Survey results also showed that the data science skills used most frequently by reliability engineers are statistical summaries and analysis, data modeling, predictive modeling, data visualization, data cleansing/preparation, and data mining. Finally, survey results showed not only an interest hiring reliability engineers with data science skills, but also a significant openness to hiring data scientists with reliability engineering skills.
SUMMARY & CONCLUSIONSThis paper provides a Reliability & Maintainability Strategic Decision-Making Framework Model for repairable systems in Rail Transportation Fleets. The Model is supported by, a total of 84 case studies, over a period of fifteen years, suggests that Safety is a function of Reliability. Those case studies on a large series of nonstructural repairable systems and components, impacting customer service in subway transportation fleets demonstrate that a greater proportion of the items examined exposed non-ageing related failure patterns over their in-service life-cycles. These results of In-service incursions, modeled by the use of probability distributions widely used in lifetime series analysis, revealed wear out patterns in agreement with studies in aircraft and ship fleets, as documented in Nowlan & Heap Reliability Report for UAL 1968 [1]. Remarkably, the distinctiveness of patterns which allow for the characterization of Age-Related from Non-Ageing related failure distributions provide a strategic decision making framework for component maintainability and asset configuration performance optimization within transportation operation system safety and cost. The project revealed relevant and significant truths and miss concepts in asset maintenance management reliability, such as: If an asset repair is not properly described, diagnosed and prescribed and executed, it could cause more negative impact in customer service, frustrating the expectation of a reliable performance. Also, if an asset that doesn’t expose typical age-related wear out failure distribution pattern is placed under a scheduled time-based maintenance repair or overhauls, it will not or marginally benefit from the program work; inevitably can re-introduce "infant-mortality" or premature failures. Results suggest that maintenance doesn’t improve asset reliability, it is meant to restore an item functions as close as new possible, however it is observed that there is always decay and problems of obsolescence. We argue that that results presented in this paper are a novel practical quantitative confirmation of the Reliability Centered Maintenance (RCM) [1] failure patterns in rail mass transit systems with Subway Failure Patterns in agreement with RCM and Failures in Aircrafts and Ships. The confirmation establishes a foundation for an innovative Reliability & Maintainability Strategic Decision-Making Framework Model for repairable Systems in Rail Transportation Fleets, which may further offer insights on the limitation on the application of the RCM Decision Diagram when there is no sufficient data to support an analysis. As Safety, Reliability & Maintainability are interconnected and cannot be separated; the Model can support safety analysis. With the exponential increase in technology complexity on the application of "Fault-Tolerant" & "Fail-Safe" designs, Safety is still a function of Reliability. Therefore, due to the fact we cannot completely dissociate the much close relationship between Reliability & Safety the Model proves to be profoundly essential to support the Safety Culture.
Summary and ConclusionsThis paper presents a methodology to show compliance to safety objectives using a new data curation system called RACK. First, we use a bowtie diagram as a lightweight safety analysis to demonstrate the connection between safety assessment and software development process. Once an assurance level is properly assigned, we use a Python script to highlight which DO-178C objectives we are obligated to show compliance to. Next, we present the use of RACK—Rapid Assurance Curation Kit—to find evidence in each of the software development processes. Finally, we present a novel way to concisely visualize traceability and status of compliance to DO-178C objectives via a sunburst. Our finding is that provenance language is embedded in DO-178C and their referenced activities. This makes RACK suitable for demonstrating compliance since its underlying data model is built on provenance classes and relationships. In summary, this paper presents a methodology that automatically generates a visualization to show compliance to DO-178C objectives using RACK to query for evidence that an activity was completed.
SUMMARYWhile the definition of a Digital Twin (DT) is provided within recent literature, each DT should be designed to meet the specific requirements of the user. As RAMS is focused on the identification, understanding and mitigation of technical risk in a system, a RAMS engineer requires a DT that can autonomously establish the potential dependencies and impacts of functional and physical failures on a system, and auto-generate various analyses to identify the appropriate mitigation approach.The concept of a Digital Risk Twin (DRT) described in this paper should uses an integrated and inter-related set of information about the system, autonomously reflecting changes across analyses that utilize causal simulation to understand potential risks and map their dependencies. This definition has been examined thoroughly from the original literature for Digital Twins, through an examination of core system safety/risk assessment practices demonstrated in RAMS and finally arrives at the main point of the definition and key aspects of a Digital Risk Twin (DRT).As the DRT digitizes the RAMS process across each stage of the Product Lifecycle, it is important that it offers common DT features such as integration, visualization, and simulation. The DRT will also implicitly digitize the engineering domain knowledge utilized in the design process (‘Digital Domain Knowledge’), providing a persistent context for analysis and design decisions, and so requires a framework of automated data management, maintaining traceability between activities and decisions made in relation to identified risks.
