Carl Byington with PHM Design, LLC reviews:
Conceptual functional architecture:
Describes functions and functional interactions
Traces functions to capabilities or services desired in the COO
Conceptual physical architecture:
Allocates and describes the conceptual implementation of functions
Traces implementation to function
Activity Flows:
Identifies primary paths through the principal use-cases to meet the goals and interests of the stakeholders
Trades identify preferred path which, in turn, provides context for requirements derivation and operational thread development.
#phmdesign
https://phmdesign.com
CBM Cost Benefit Analysis by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews some of the elements of CBM Cost Benefit Analysis. The analysis consider implementation and non recurring engineering cost as well as deferred, eliminated scheduled maintenance, reduced unscheduled maintenance, and operational cost savings drivers. Specific examples from aircraft, ground vehicle, and industrial applications are provided.
#phmdesign
https://phmdesign.com
Carl Byington has over thirty years developing models and analyzing data from various equipment and critical assets. He is currently a consultant who leads PHM Design, LLC, based in the greater Atlanta area. One operational strategy Carl Byington employs involves condition-based maintenance (CBM). This protocol rests on the concept that equipment failure is a process, rather than one single event.
Condition-based maintenance (CBM) is a philosophy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. By contrast, time-based or use-based maintenance involves performing periodic maintenance after specified periods of time or hours of operation. CBM has the potential to decrease life-cycle maintenance costs (by reducing unnecessary maintenance actions and greater operational failures), increase operational readiness, and improve safety.
Implementation of condition-based maintenance involves predictive diagnostics (i.e., diagnosing the current state or health of a machine and predicting time to failure based on an assumed model of anticipated use). CBM and predictive diagnostics depend on multisensor data—such as vibration, temperature, pressure, and presence of oil debris—which must be effectively fused to determine machinery health.
Definition of RCM, principles and goals of RCM; Four major components of RCM: reactive maintenance, preventive maintenance, predictive testing and inspection and proactive maintenance; RCM strategies.
Condition monitoring (or CM) is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.) and identify a significant change which is indicative of a developing fault.
The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent failure and avoid its consequences.
One of the major challenges for Gas Turbine users is to ensure high level of engine availability and reliability, and efficient operation during their complete life-cycle. For this purpose, Various maintenance approaches have been introduced over the years for the gas turbine maintenance: Breakdown Maintenance or Run to Failure, Preventive Maintenance or Scheduled Maintenance and Condition-Based Maintenance (CBM). Here the focus is on CBM or predictive maintenance.
CBM Cost Benefit Analysis by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews some of the elements of CBM Cost Benefit Analysis. The analysis consider implementation and non recurring engineering cost as well as deferred, eliminated scheduled maintenance, reduced unscheduled maintenance, and operational cost savings drivers. Specific examples from aircraft, ground vehicle, and industrial applications are provided.
#phmdesign
https://phmdesign.com
Carl Byington has over thirty years developing models and analyzing data from various equipment and critical assets. He is currently a consultant who leads PHM Design, LLC, based in the greater Atlanta area. One operational strategy Carl Byington employs involves condition-based maintenance (CBM). This protocol rests on the concept that equipment failure is a process, rather than one single event.
Condition-based maintenance (CBM) is a philosophy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. By contrast, time-based or use-based maintenance involves performing periodic maintenance after specified periods of time or hours of operation. CBM has the potential to decrease life-cycle maintenance costs (by reducing unnecessary maintenance actions and greater operational failures), increase operational readiness, and improve safety.
Implementation of condition-based maintenance involves predictive diagnostics (i.e., diagnosing the current state or health of a machine and predicting time to failure based on an assumed model of anticipated use). CBM and predictive diagnostics depend on multisensor data—such as vibration, temperature, pressure, and presence of oil debris—which must be effectively fused to determine machinery health.
Definition of RCM, principles and goals of RCM; Four major components of RCM: reactive maintenance, preventive maintenance, predictive testing and inspection and proactive maintenance; RCM strategies.
Condition monitoring (or CM) is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.) and identify a significant change which is indicative of a developing fault.
The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent failure and avoid its consequences.
One of the major challenges for Gas Turbine users is to ensure high level of engine availability and reliability, and efficient operation during their complete life-cycle. For this purpose, Various maintenance approaches have been introduced over the years for the gas turbine maintenance: Breakdown Maintenance or Run to Failure, Preventive Maintenance or Scheduled Maintenance and Condition-Based Maintenance (CBM). Here the focus is on CBM or predictive maintenance.
Incorporating a predictive maintenance strategy has been proven to provide savings. Learn how to use your pressure and temperature gauges as part of your initiatives.
The Predictive Maintenance solution accelerator is an end-to-end solution for a business scenario that predicts the point at which a failure is likely to occur. Use this solution accelerator proactively to optimize maintenance and to create automatic alerts and actions for remote diagnostics, maintenance requests, and other workflows. The solution combines key Azure IoT services like IoT Hub and Stream analytic.
Condition-Based Maintenance Basics by Carl Byington - PHM Design, LLCCarl Byington
Condition-based maintenance (CBM or CBM+) is a strategy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. CBM is enabled by the evolution of key technologies, including improvements in - sensors, microprocessors, digital signal processing, simulation modeling, multisensor data fusion, reliability engineering, Internet of Things (IoT) connectivity, data warehousing, cloud computing, machine learning (ML), artificial intelligence (AI), and predictive analytics. CBM involves monitoring the health or performance of a component or system and performing maintenance based on that inferred health and in some cases, predicted remaining useful life (RUL). This predictive maintenance philosophy contrasts with earlier ideologies, such as corrective maintenance — in which action is taken after a component or system fails — and preventive maintenance — which is based on event or time milestones. Each involves a cost tradeoff.
Carl Byington with PHM Design, LLC reviews some of the elements of CBM.
#phmdesign
https://phmdesign.com
This presentation outlines the processes and benefits of applying enhanced maintenance planning techniques such as Reliability Centred Maintenance at your place of work. Please go to www.simenergy.co.uk for more information.
Why predictive maintenance should be a combined effortWouter Verbeek
Predictive maintenance is an extremely promising maintenance strategy, but implementation often turns out to be way more complicated than expected. A lot of attempts to implement predictive maintenance strand at the same departments as where they were initiated. The key towards successful implementation of predictive maintenance is to combine the knowledge of all departments in making decisions. In this presentation we start by explaining, based on the subject of sensor selection, why involving your entire organization is so important. Afterwards we give advice on how to implement predictive maintenance, give examples based on the Strukton Worksphere case and discuss how to get your entire organization on board.
CONDITION-BASED MAINTENANCE USING SENSOR ARRAYS AND TELEMATICSijmnct
Emergence of uniquely addressable embeddable devices has raised the bar on Telematics capabilities.
Though the technology itself is not new, its application has been quite limited until now. Sensor based
telematics technologies generate volumes of data that are orders of magnitude larger than what operators
have dealt with previously. Real-time big data computation capabilities have opened the flood gates for
creating new predictive analytics capabilities into an otherwise simple data log systems, enabling real-time
control and monitoring to take preventive action in case of any anomalies. Condition-based-maintenance,
usage-based-insurance, smart metering and demand-based load generation etc. are some of the predictive analytics use cases for Telematics. This paper presents the approach of condition-based maintenance using
real-time sensor monitoring, Telematics and predictive data analytics.
Ever heard planned maintenance (PM) stops failures? Or Condition Based Maintenance = Vibration analysis? Or should we apply RCM or CBM? This slide share helps explain why the first two of the these part of the story and why the third is like comparing apples and pears
In medicine - an MRI can quickly reveal a hidden ailment and actionable insight to get better. For IT and business leaders whose key concern with the mainframe is the platform costs and lean operations - the CA Mainframe Resource Intelligene reveals multiple sources of hidden mainframe costs and operational inefficiencies along with actionable recommendations.View this slideshare to understand how this new SaaS offering from CA brings together automation, speed, analytics and mainframe expertise of 40+ years. CA Mainframe Resource Intelligence reports answer your CIO’s toughest questions about mainframe optimization and potential for digital transformation.
For more information, please contact your account director or mainframe specialist at:
http://ow.ly/PALG50htHgF
Incorporating a predictive maintenance strategy has been proven to provide savings. Learn how to use your pressure and temperature gauges as part of your initiatives.
The Predictive Maintenance solution accelerator is an end-to-end solution for a business scenario that predicts the point at which a failure is likely to occur. Use this solution accelerator proactively to optimize maintenance and to create automatic alerts and actions for remote diagnostics, maintenance requests, and other workflows. The solution combines key Azure IoT services like IoT Hub and Stream analytic.
Condition-Based Maintenance Basics by Carl Byington - PHM Design, LLCCarl Byington
Condition-based maintenance (CBM or CBM+) is a strategy of performing maintenance on a machine or system only when there is objective evidence of need or impending failure. CBM is enabled by the evolution of key technologies, including improvements in - sensors, microprocessors, digital signal processing, simulation modeling, multisensor data fusion, reliability engineering, Internet of Things (IoT) connectivity, data warehousing, cloud computing, machine learning (ML), artificial intelligence (AI), and predictive analytics. CBM involves monitoring the health or performance of a component or system and performing maintenance based on that inferred health and in some cases, predicted remaining useful life (RUL). This predictive maintenance philosophy contrasts with earlier ideologies, such as corrective maintenance — in which action is taken after a component or system fails — and preventive maintenance — which is based on event or time milestones. Each involves a cost tradeoff.
Carl Byington with PHM Design, LLC reviews some of the elements of CBM.
#phmdesign
https://phmdesign.com
This presentation outlines the processes and benefits of applying enhanced maintenance planning techniques such as Reliability Centred Maintenance at your place of work. Please go to www.simenergy.co.uk for more information.
Why predictive maintenance should be a combined effortWouter Verbeek
Predictive maintenance is an extremely promising maintenance strategy, but implementation often turns out to be way more complicated than expected. A lot of attempts to implement predictive maintenance strand at the same departments as where they were initiated. The key towards successful implementation of predictive maintenance is to combine the knowledge of all departments in making decisions. In this presentation we start by explaining, based on the subject of sensor selection, why involving your entire organization is so important. Afterwards we give advice on how to implement predictive maintenance, give examples based on the Strukton Worksphere case and discuss how to get your entire organization on board.
CONDITION-BASED MAINTENANCE USING SENSOR ARRAYS AND TELEMATICSijmnct
Emergence of uniquely addressable embeddable devices has raised the bar on Telematics capabilities.
Though the technology itself is not new, its application has been quite limited until now. Sensor based
telematics technologies generate volumes of data that are orders of magnitude larger than what operators
have dealt with previously. Real-time big data computation capabilities have opened the flood gates for
creating new predictive analytics capabilities into an otherwise simple data log systems, enabling real-time
control and monitoring to take preventive action in case of any anomalies. Condition-based-maintenance,
usage-based-insurance, smart metering and demand-based load generation etc. are some of the predictive analytics use cases for Telematics. This paper presents the approach of condition-based maintenance using
real-time sensor monitoring, Telematics and predictive data analytics.
Ever heard planned maintenance (PM) stops failures? Or Condition Based Maintenance = Vibration analysis? Or should we apply RCM or CBM? This slide share helps explain why the first two of the these part of the story and why the third is like comparing apples and pears
In medicine - an MRI can quickly reveal a hidden ailment and actionable insight to get better. For IT and business leaders whose key concern with the mainframe is the platform costs and lean operations - the CA Mainframe Resource Intelligene reveals multiple sources of hidden mainframe costs and operational inefficiencies along with actionable recommendations.View this slideshare to understand how this new SaaS offering from CA brings together automation, speed, analytics and mainframe expertise of 40+ years. CA Mainframe Resource Intelligence reports answer your CIO’s toughest questions about mainframe optimization and potential for digital transformation.
For more information, please contact your account director or mainframe specialist at:
http://ow.ly/PALG50htHgF
Maintenance accounts for approx. 30% of the life-cycle costs of a high-speed train, making it the largest rolling stock operating cost factor besides energy. Advancements in the Big data technologies and predictive analytics with M2M telematics are enabling deep insights into the machine operations by providing full functionality status in real time - giving rise to optimal maintenance schedules,
improved machine availability and asset usage. Predictive maintenance, also known as Condition Based Maintenance (CBM) aims to reduce these unnecessary costs by basing the maintenance need on the actual condition of the machine rather than on preset schedules or assumptions.
This presentation includes:
- Why performance matters for digital businesses?
- Use Cases for performance / load testing
- Load Test Design Considerations
- Tools and Technologies
- Methodology and Approach
- Activities and Deliverables
- Load Testing Success Stories
This prez talks about the automation benefits, usage of QTP and it's different kind of frameworks.
Also talks about the skills set required for QTP implementations.
In December 2023 we started a series of three webinars about the theme "asset management enabling technologies". In these webinars, we discuss the different aspects of connectivity and the enabling technologies, which in our opinion is crucial for the successful operation of your assets.
In this third webinar, Johan Ferket and Pieter Wielemaker will focus on three important items in the Asset Performance Management (APM) journey: transformational planning, tool and software evaluation and performance tracking.
They will provide answer to the questions:
- What are the main stage gates in the APM Roadmap?
- Which tooling and software are best suited for your situation: how to evaluate and choose?
- How to close the loop: APM performance tracking?
Optimizing connected system performance md&m-anaheim-sandhi bhide 02-07-2017sandhibhide
Sandhiprakash Bhide presenting at the Smart Manufacturing Innovation Summit/Industry 4.0 event on "Optimizing Connected System Performance and Establishing Tangible Goals for Sensor Use"
An assumption of learning curve theory is which of the followingjohann11370
FOR MORE CLASSES VISIT
www.ops571help.com
1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
FOR MORE CLASSES VISIT
www.ops571help.com
1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
In medicine - an MRI can quickly reveal a hidden ailment and actionable insight to get better. For IT and business leaders whose key concern with the mainframe is the platform costs and lean operations - the CA Mainframe Resource Intelligene reveals multiple sources of hidden mainframe costs and operational inefficiencies along with actionable recommendations. This is the only offering in the market that combines economic consulting services with proprietary utilities and automation technologies. View this SlideShare to understand the solution – how services, best practices and mainframe expertise of 40+ years from CA comes together to solve the CIO and CFO’s biggest challenge.
Call your account director or mainframe specialist.: https://www.ca.com/us/contact/mainframe-economic-consultant.html
The Fine Art of Combining Capacity Management with Machine LearningPrecisely
Today, capacity management within the enterprise continues to evolve. In the past, we were focused on the hardware – but now we are focused on the services. With that in mind, the amount of data available has increased significantly and has become difficult for individuals to sort through.
It is apparent that to be successful in this discipline, we need the machines to do more of the heavy lifting. This includes automatically creating reports, calling out anomalies and producing forecasts. The intuition of the human computer is imperative to the success.
View this webinar on-demand where we discuss:
• The strengths and weaknesses of capacity management with and without machine learning
• What machine learning can provide throughout the process
• The benefits of using capacity management and machine learning within your organization
Similar to CBM Requirements by Carl Byington - PHM Design, LLC (20)
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
CBM Requirements by Carl Byington - PHM Design, LLC
1.
2. • A little bit about how PHM Requirements
are manifested
– aka - Who moved my cheese?¥
• The impacts on operational, maintenance,
and logistics measures of effectiveness
• Implementation tradeoff strategies
• The cost assessment for the PHM and
the benefits
• Some examples of cost analysis and
motivations that might help your current
initiatives
¥Who Moved My Cheese? An Amazing Way to Deal with Change in Your Work and in Your
Life, by Spencer Johnson, Putnam Publishing, 1998.
3. • Reliability
– Was: Component MTBF is king.
– Is (becoming): MTBF yes, but prevent system aborts (MTBSA) and
No Surprises! What’s my updated failure risk based on usage?
• Operational
– Was: Performance is king. Scheduled maintenance is acceptable and
doesn’t count against operational availability. Limited to no predictive
diagnostics in field.
– Is (becoming): Balance of performance, availability, and affordability.
Embedded PHM capability. “True” operational availability by reducing
as many maintenance actions as practicable. Faster deployment
readiness/optempo. Mission readiness assessment and decision
aiding with better predictive information.
• Maintenance
– Was: Preventative maintenance is acceptable and doesn’t count
against availability. Three level maintenance systems. “Silo” approach
to information management and diagnostic/repair process.
– Is (becoming): Condition Based (and JIT) Maintenance. Preference
for 2-Level maintenance. Merging of intermediate functions between
operational and depot levels.
• Logistics, Supplies, and Sparing
– Was: End user’s problem and significant spares market for suppliers
– Is (becoming): Performance-Based Logistics (PBL) and Full-service
contractors risk. Autonomic logistics support concept of operations.
4. Corrective Preventive CBM Proactive
§ Small Items
§ Non-critical
§ Unlikely to fail
§ Redundant
§ Predictable
Wear
§ Consumables
§ Known Failure
Patterns
§ ‘Random’
Failures
§ Minimal Wear
§ Critical Items
§ PM Induced
§ Inspection and
MMH drivers
§ Accurate Usage
Prognostics
§ Incipient Fault
Detection/Diagnos
is
§ Health & Adaptive
Prognostics
Evolving Maintenance Approach
Condition-Based
Corrective
Preventive
Percentage of Maintenance Actions
100%
Present Future
Eliminated
CBM+ Implementations
5. US Navy: OPNAVINST 4790.16, Condition-Based Maintenance
(CBM) Policy, 6 May 98
– “CBM Methodology shall be used to determine maintenance
decisions and reduce scheduled maintenance and manpower
requirements …”
– “Chief of Naval Operations (CNO) will fund naval programs,
processes, and enabling technologies proven applicable and
effective in supporting the maintenance, manning, and cost reduction
objectives of this instruction.”
US Army VCoS Memo: Accelerated Application of Embedded Diagnostics, 30 Apr
98
§ “… include embedded diagnostics on all new and retrofit equipment…”
§ “...We will not field systems or retrofit equipment without embedded diagnostics. If
a tradeoff must be made because of funding, Army policy will be to obtain fewer,
more capable systems.”
Hint: Google “CBM+ requirements” for a newer mandate list
6. • Apply embedded diagnostics to detect XX% of failures relating to
essential / mission critical functions
• Improve availability – readiness & downtime.
• Maximize combat power w/reduction in logistics & personnel
w/reduced demand for maintenance & supply
• Enable an anticipatory maintenance (JIT, pit-stop,etc.)
maintenance
• Provide User/Soldier/Sailor/Marine notification of pending failures
• Provide vehicle monitoring of conditions and mission
capability/readiness status
• Embedded diagnostics & prognostics for individual components,
LRU’s & LRM’s; meet levels of detection or isolation to meet
maintainability goals.
• Enable vehicle-to-vehicle prognostics and mission decision aiding
• Minimize the need for test and ground support equipment
• Embed instrumentation, provide at-vehicle test and diagnostics
7. LRC/Module On-board On-Ground
F u n c t i o n a l
Failures
96% Detection Capability
Non-Electronic 80% Isolation 85% Isolation, 3 LRCs or
less
75% Isolation, 1 LRC
Electronic 85% Isolation, 1 LRC
8. PHM System Requirements Summary
Coverage: Detect up to 40 Faults (on monitored systems)
Failure Detection/Diagnostics: Detect and Isolate > 75% of failures
(< 5% false identification)
Failure Prognostics: Prediction with > 90% Confidence (threshold) and
>96% Confidence (objective) at least one (threshold) and seven
(objective) 24-hour missions
11. § Example: Telelogic’s
Rhapsody
§ Models constructed
using Systems
Modeling Language
(SysML™)
§ Other PHM Design
and Metrics tools to
capture underlying
requirements
13. § Conceptual functional architecture
§ Describes functions and functional interactions
§ Traces functions to capabilities or services desired in the COO
§ Conceptual physical architecture
§ Allocates and describes the conceptual implementation of functions
§ Traces implementation to function
§ Activity Flows
§ Identifies primary paths through the principal use-cases to meet the
goals and interests of the stakeholders
§ Trades identify preferred path which, in turn, provides context for
requirements derivation and operational thread development.
14. 3rd Party Database Server
User
Windows Application
Browser (Web application)
Ground Station Server
Web Service
• Authenticate user
• Get recent files
• Perform analysis
• …
Database
Security:
128-bit encryption
Internet
• IVHM Server shall be web-accessible
• 3rd Party Databases accessed through network
15. • Rhapsody utilized to design system:
– Components – Coherent / decoupled
– Information flow – What travels between pieces
• Functionality and information flow is being defined
• High-level design enables partitioning / testing
Platform
«block
»
Attributes
Operations
Groundstation
Groundstation Groundstation
«block
»
Attributes
Operations
ExternalService
Platform
«flow »
PlatformUpdates
ExternalService
Platform
ThirdPartyService
«block
»
Attributes
Operations
ServiceUser
«flow »
ServiceData
ServiceUser
EnterpriseAccess
«block»
itsDataAccessLayer:DataAccessLayer
1
PresentationData
Logic
Report
Query
itsPresentationLayer:PresentationLayer
1
Display
Command
itsLogicLayer:LogicLayer
1
DisplayData
CommandData
PresentationData
«flow»
DataAccess
«flow»
itsDataServiceLayer:dataServiceLayer
1
Display
«flow»
Command
«flow»
DisplayData
«flow»
CommandData
«flow»
itsDataStorageLayer:DataStorageLayer
1
Report
«flow»
Query
«flow»
DataIn
itsSoftwareUpdateEngine
1
ConfigCheck
ExtResponse
«flow»
DepSysUpdate
PlatformUpdate
ExtCommand
«flow»
DeployData
«flow» PresentationData
Logic
Report
Query
Display
Command
DisplayData
CommandData
PresentationData
DataAccess
Display
Command
DisplayData
CommandData
Report
Query
DataIn
ConfigCheck
ExtResponse
DepSysUpdate
PlatformUpdate
ExtCommand
DeployData
Break into:
SoftwareUpdate Engine
MissionDataEngine
SecurityEngine
Each has these layers scoped to their
function.
Groundstation
«block»
itsWideAccess:EnterpriseAccess
1
ConfigCheck
ExtResponse
DepSysUpdate
PlatformUpdate
ExtCommand
DeployData
itsDeployedSys:DeployedSystem
1
ConfigCheck
«flow»
ExtResponse
«flow»
ExtCommand
«flow»
Update
«flow»
DeploySysUpdates
PlatformUpdate
«flow»
PlatformUpdateInstructions
Enterprise
«flow»
PlatformData
Platform
ExternalService
«flow»
«flow»
Platform
«flow»
PlatformUpdates
ConfigCheck
ExtResponse
DepSysUpdate
PlatformUpdate
ExtCommand
DeployData
ConfigCheck
ExtResponse
ExtCommand
Update
PlatformUpdate
Enterprise
Platform
ExternalService
Platform
Requires Authentication
When the DeployedSystem User connects to the
Enterprise System through the EnterpriseServices link,
the remaining interfaces between deployed and
enterprise automatically establish themselves.
Link to Existing
Ground Station
Software
Link to
Enterprise
Application Layers
16. • Engineering design tool used to:
– Prioritize spoken and unspoken customer
wows, wants, and needs
– Translate these needs into technical
characteristics and specifications
– Build and deliver a quality product or service
by focusing everybody toward customer
satisfaction
• Useful in the current setting to
understand “how” to implement and the
interdependencies
http://www.qfdi.org/
17. Strong
Medium
Weak
Technical Requirement
Prioritization
(Assumes Sortie-type Aircraft)
1. Onboard Data Storage
2. Real-Time Data Collection
3. Open System Design
4. Open Data Transport Protocols
5. Real-time Signal Processing
6. Real-time Signal Monitoring
7. Onboard Reasoning
8. At Wing Processing
9. Use Existing Processing Capacity
10. Use Existing Data Storage
11. At Wing Stimulation
Importance
Rank
Open
System
Design
Open
Data
Transport
Protocols
Real-time
Signal
Monitoring
Real-Time
Data
Collection
Real-time
Signal
Processing
Onboard
Data
Storage
Onboard
Reasoning
At
Wing
Processing
At
Wing
Stimulation
Use
Existing
Processing
Capacity
Use
Existing
Data
Storage
Minimal Onboard Hardware 7 5 3 5 5 5
Minimize Hardware Cost 8 2 2 2 5 5
Minimize Recertification 7 2 5 5
Facilitate Current Equip. 9 2 3 2 2 2 2
Data Continuity 10 5 5 5
Reusable Modules 9 5 5 1
Detect and Isolate Problems 10 5 5 5 3 3 3 2
Reduce NFF 7 3 5 3 5 2 3 2
Reduce CND 8 3 5 3 5 2 3 2
Reduce RTOK 7 3 5 3 5 2 3 2
Logistics Trigger 6 5 5 5
Enable Reconfiguration 6 5 5 5
Dynamic Recoverability 7 5 5 5 5
Graceful Degradation 8 5 3 5 2
194 203 156 249 181 295 164 130 117 128 128
0.11 0.11 0.09 0.14 0.10 0.16 0.09 0.07 0.06 0.07 0.07
Integration
Performance
Enhanced
Performanc
e
Customer
Requirements
Technical
Requirements
+ +
+
+
+
+
+
+
– –
–
–
18.
19. 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Onboard Capture + D&P Processing
Onboard Capture/At Wing Processing
Onboard Capture+Reconfigurable Tests
BIT only + At Wing Stimulation
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Onboard Capture + D&P Processing
Onboard Capture/At Wing Processing
Onboard Capture + Reconfigurable TPS
BIT only + At Wing Stimulation
Full Functionality Problem Type
Partial Functionality 1 Binary Fault
Limited Funtionality 2 Intermittent but Repeatable
Very Limited Functionality 3 Intermittent but Pseudo-Random
No Functionality 4 Graceful Degradation
Predictive
Prognostics
Onb. Impact
Assessment
Dynamic
Recovery
Diagnostic
Processing
Ambiguity
Reduction
Op. Decision
Support
Coupled
IETM
Problem
Isolation
Problem Type Problem Type
Logistics
Trigger
Maintenance
Planning
PHM Approach
Problem Type
Problem Type
Problem Type Problem Type
Problem Type
Basis and Relative Evaluation of Functionality for any System
PHM Approach
Problem Type Problem Type Problem Type Problem Type
Anomaly
Detection
20. • During System Design and Development (SDD) phase and
run parallel to the mainstream design effort
– 80% of Total Life Cycle Costs are “fixed” at the end of SDD
– Systems need to be plumbed with a PHM baseline system
• Initial development costs relatively low when compared with TOC
• Avoids costly retrofits after production and deployment
• Accuracy and Reliability of system will grow over time with minimal
investment
• May allow tradeoff of design margins if PHM design provides real time
monitoring
– Sensor augmentation and data publishing requirements should be
determined early so that prognostics growth can occur
– PHM must be synchronized with PBL planning so that PBL risks can
be fairly evaluated by senior executives
– This is a paradigm shift for many
• Service life extension and similar upgrade programs are
other good options – make sure the ground station
infrastructure and maintenance/logistics follow