Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.

1. 진료년도 기준(한의분류 제외, 약국 제외) 2. 건강보험 급여실적(의료급여 제외)이며, 비급여는 제외 - 2022년 6월 지급분까지 반영 3. 아래 질병통계 자료는 요양기관에서 환자진료중 진단명이 확정되지 않은 상태에서의 호소, 증세 등에 따라 일차진단명을 부여하고 청구한 내역중 주진단명 기준으로 발췌한 것이므로 최종확정된 질병과는 다를수 있음 4. 수가코드: M0111, M0115, M0116, M0121, M0125, M0126, N0011, N0012, N0053, N0054, NA055, NA056, N0057, NA057, N0058, NA058, N0181, N0182, N0183, N0184, N0061, N0062, N0063, N0064, N0071, N0072, N0073, U2211, U2212, U2213, U2214 5. 수가코드별 자료는 해당 수가에 대한 비용의 합이며 진료비와는 다른 개념임. 6. 2022년 자료는 미청구, 미지급분등으로 인해 제공 불가

This dataset contains the performance monitoring data for the Medicines, Technologies and Pharmaceutical Services (MTaPS) program from project year 5 quarter 3, which covered the period from April to June 2023. The purpose of this data is to measure the performance of the MTaPS program against program targets in order to determine the results of activity implementation and plan future activities.

Verification and validation (V&V) has been identified as a critical phase in fielding systems with Integrated Systems Health Management (ISHM) solutions to ensure that the results produced are robust, reliable, and can confidently inform about vehicle and system health status and to support operational and maintenance decisions. Prognostics is a key constituent within ISHM. It faces unique challenges for V&V since it informs about the future behavior of a component or subsystem. In this paper, we present a detailed review of identified barriers and solutions to prognostics V&V, and a novel methodological way for the organization and application of this knowledge. We discuss these issues within the context of a prognostics application for the ground support equipment of space vehicle propellant loading, and identify the significant barriers and adopted solution for this application.

This paper introduces a generic distributed prognostic health management (PHM) architecture with specific application to the electrical power systems domain. Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element (CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. Implementation results from a network of distributed embedded devices monitoring a prototypical aircraft electrical power system are presented, where the CEs are Sun Microsystems Small Programmable Object Technology (SPOT) devices.

This data asset contains the performance monitoring data for the MTaPS program from project year 5 quarter 4, which covered the period from July through September 2023. The purpose of this data is to measure the performance of the MTaPS program against program targets in order to determine the results of activity implementation and plan future activities. MTaPS aims to help low- and middle-income countries strengthen their pharmaceutical systems to ensure sustainable access to and appropriate use of safe, effective, quality-assured, and affordable essential medicines and pharmaceutical services.