Designing for health - A methodology for integrated diagnostics/prognostics

Beshears, Raymond; Butler, L.

doi:10.1109/mim.2006.1664038

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Beshears and Butler provide a detailed discussion of the entire Raytheon HMS Methodology in their paper titled "Designing For Health: A Methodology For Integrated Diagnostics / Prognostics". [9] An effective HMS Methodology must start early in the product life cycle with thorough system trade studies to derive HMS requirements. Once the HMS requirements have been comprehended, the Analysis / Design Influence stage addresses the identification of optimal candidates for diagnostic / prognostic technology insertion.…”

Section: Phm Methodology Overviewmentioning

confidence: 99%

Diagnostics To Prognostics - A Product Availability Technology Evolution

Janasak

Beshears

2007

2007 Proceedings - Annual Reliability and Maintainability Sympsoium

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From the customer's perspective, one of the most critical expectations of R&M in the New Frontier focuses on product availability and cost of ownership. Whether you consider a personal automobile or a pilot's avionics, it must operate reliably, on demand, at an affordable cost. Consumer products such as automobiles, aircraft engines, medical diagnostic equipment, and computer server farms already employ remote diagnostics to monitor product health and optimize product availability. In this context, the next frontier is to evolve robust diagnostics into maturing prognostics and embed these technologies in our products in the form of Health Management System (HMS) capabilities. Using health based information for real time mission planning and autonomic maintenance is a key enabler for achieving ultra-reliability and availability in tomorrow's system of systems environment. The problem we face is how to best achieve these HMS capable products in a cost effective manner.Today's robust diagnostic technologies are proving to be key enablers for condition based maintenance, improved fault detection / fault isolation, and lower operational cost. Some success has been realized with the insertion of early prognostic technology, primarily in the area of mechanical PHM applications. By using a systematic methodology to develop, implement, and mature electronic prognostics into the next generation of HMS capable products, we are providing our customers (and most importantly the end user in the field) with new insight into the health and system readiness of our products. Being able to make health based decisions for mission planning, real time operational modes, and just-intime maintenance will become the key to meeting product availability at an affordable cost.

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Section: Phm Methodology Overviewmentioning

confidence: 99%

Diagnostics To Prognostics - A Product Availability Technology Evolution

Janasak

Beshears

2007

2007 Proceedings - Annual Reliability and Maintainability Sympsoium

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“…Raytheon developed a closed-loop health management systems (HMS) methodology to develop and field HMS capable products (Janasak and Beshears 2007). Raytheon’s HMS methodology contains five phases that span the product life cycle and follow the DoD 5000.2 Instruction: concept refinement, technology development, system development and demonstration, production and deployment, and operation and support (Beshears and Butler 2005). Key tasks for successful implementation of the HMS process were identified as trade studies to set up the HMS architecture, clear HMS requirements, product characterization through analysis, strategically-placed sensors, and data collection/maturation (Beshears and Butler 2005).…”

Section: Phm Frameworkmentioning

confidence: 99%

A review of diagnostic and prognostic capabilities and best practices for manufacturing

2016

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Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

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“…With reference to relevance of operating context to motor failure, a condition-monitoring data scheme can be developed to collect, record and analyse predictive maintenance data of motor under various operating contexts, where both an off-line monitoring and/or on-line monitoring data can be used (a sample sheet may be seen in Table III). For on-line recording, many process industries today use systems like CMMS or ERP systems (Zajicek et al, 2002;Carnero, 2003;Beshears and Butler, 2005;Hart, 2005;Kothamasu et al, 2006;Kumar and Chaturvedi, 2006) to collect and manage data in both real time as well as offline. The offline monitoring data can be hooked to DCS/network through portable data collectors.…”

Section: Maintenance Of Industrial Equipmentmentioning

confidence: 99%

Maintenance of industrial equipment

Kumar

Chaturvedi²,

Deshpande³

2009

International Journal of Quality &Amp; Reliability Management

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Purpose -The purpose of this paper is to ascertain overall system health and maintenance needs with degree of certainty using condition-monitoring data with hierarchical fuzzy inference system. Design/methodology/approach -In process plants, equipment condition is ascertained using condition-monitoring data for each condition indicator. For large systems with multiple condition indicators, estimating the overall system health becomes cumbersome. The decision of selecting the equipment for an overhaul is mostly determined by generic guidelines, and seldom backed up by condition-monitoring data. The proposed approach uses a hierarchical system health assessment using fuzzy inference on condition-monitoring data collected over a period. Each subsystem health is ascertained with degree of certainty using degree of match operation performed on fuzzy sets of condition-monitoring data and expert opinion. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge. Findings -The proposed approach has been applied to a large electric motor (. 500kW), which is treated as four subsystems i.e. power transmission system, electromagnetic system, ventilation system and support system. Fuzzy set of condition-monitoring data of each condition indicator on each subsystem is used to ascertain the degree of match with the expert opinion fuzzy set, thus inferring the need for periodical overhaul. Subjective expert opinion and quantitative condition-monitoring data have been evaluated using hierarchical fuzzy inference system with a rule base. It is found that the certainty of each subsystem's health is not the same at the end of 600 days of monitoring and can be classified as "very good", "good", "marginal" and "sick". Degree of certainty has helped in taking a managerial decision to avoid "over-maintenance" and to ensure reliability. Large volumes of condition-monitoring data not only helped in assessing motor overhaul health, but also guide the maintenance engineer to suitably review maintenance/monitoring strategy on similar systems to achieve desired reliability goals. Practical implications -Condition-monitoring data collected for long periods can be utilized to understand the degree of certainty of degradation pattern in the longer time frame with reference to domain knowledge to improve effectiveness of predictive maintenance towards reliability. Originality/value -The paper gives an opportunity to evaluate quantitative condition-monitoring data and subjective/qualitative domain expertise using fuzzy sets. The predictive maintenance cycle "Monitor-analyse-plan-repair-restore-operate" is scientifically regulated with a degree of certainty. Approach is generic and can be applied to a variety of process equipment to ensure reliability through effective predictive maintenance.

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Designing for health - A methodology for integrated diagnostics/prognostics

Cited by 4 publications

References 2 publications

Diagnostics To Prognostics - A Product Availability Technology Evolution

Diagnostics To Prognostics - A Product Availability Technology Evolution

A review of diagnostic and prognostic capabilities and best practices for manufacturing

Maintenance of industrial equipment

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