Sensors may range from relatively simple single-function units to multipurpose testing equipment with embedded analytic capability. Sensors are often positioned on or near the equipment being monitored.
Equipment can be monitored using sophisticated instrumentation, such as vibration analysis and infrared thermography. When instrumentation is used, parameters can be imposed to trigger maintenance response.
Condition monitoring converts an output from the sensor to a digital parameter representing a quantifiable physical condition and related information such as the time calibration, data quality, data collector utilized, or sensor configuration.
Condition monitoring provides the link between the sensor device and the health assessment analysis capability.
Health assessment is the capability to use the inputs from condition monitoring of system behavior machine condition and to provide to the operator and support management an assessment of the equipment’s operational condition i.e., assessment based upon current measurements and related data.
Health assessments based on condition monitoring are accomplished on the platform or operating equipment in real-time.
An “on-system” health assessment includes sensor signal analysis, produces meaningful condition descriptors, and derives useable data from the raw sensor measurements i.e., model-based reasoning combined with on-system real-time analysis of correlated sensor outputs.
Health assessment facilitates the creation and maintenance of normal baseline “profiles” and identifies abnormalities when new data are acquired, and determines in which assessment category, if any, the data belong e.g., “alert” or “alarm”.
Digital Health assessment diagnoses of component faults rates the current health of the equipment or process, considering such inputs as sensor output information, technical specifications, configuration data, operating history, and historical condition data.
Equipment health assessment may also be conducted in proximity to the system—”at-system” assessments using a portable maintenance aid PMA that interfaces to the equipment indirectly through an equipment access panel or directly to line replaceable units.
The PMA is then used to update “off-system” databases for real-time or future health assessment. Systems information from inspections and non-destructive evaluations NDE are also important sources of equipment health assessments.
The long-term health assessment goal is to provide managers with predictions about the remaining useful life of the machine before maintenance is required. There are two fundamental aspects to employing CBM+ health assessment capabilities.
The first relates to on-system processing and predictive maintenance to the extent a platform is enabled with those capabilities. Generally, on-system assessment data processing is automated, and analysis is performed through the use of embedded processors.
The second aspect of health assessment is the off-system processing of collected sensor data from data storage and management.
Off-system analysis uses communications networks, databases, and health analysis software applications that make up the enterpriselevel capability for CBM+ data collection and analysis.
Communication of condition-related data, other technical information such as configuration data, technical descriptive data, maintenance procedures, and management information is critical to an effective CBM+ implementation.
The sharing of maintenance information and other data among all elements of a CBM+ environment should be possible, regardless of the data storage location. An open architecture, commercial, or DoD-recognized data standard should be used to facilitate the sharing of data outside a single system and to provide for future updates and upgrades.
On-system data should be accessible to other on-system components using hardware data buses or collocated data repositories. Similarly, at- and off-system applications may require connectivity to required data sources using database access or interchange of transactions
Digital logbooks, message management tools, and database management software should be implemented to ensure needed communications capabilities. As the CBM+ environment becomes more complex and extensive, the expanded use of multiple communications mechanisms will occur.
The CBM+ implementer should plan for the maximum application of data communications standards to facilitate the various data exchange requirements.
Data management is central to implementation, consisting of acquiring data e.g., through sensors or other acquisition techniques, manipulating data into meaningful form e.g., converting analog to digital data, storing data electronically in digital form, transmitting data through electronic means, accessing data as a basis for analysis, and providing data information to decision makers.
In support of CBM+, data are held in two ways: on-system in small amounts to support embedded health assessment and reporting, or off-system in a larger electronic storage media sometimes referred to as a data warehouse. A data warehouse is a computer database that collects, integrates, and stores an organization’s computer data with the aim of maintaining and providing accurate and timely management information and supporting data analysis.
The data may be distributed; that is, located at multiple organizational and locations. One issue relating to the CBM+ database concerns data access and sharing. For example, if the CBM+ database comprises the single physical repository for condition, performance, trending history, and other data categories, then each database user including DoD and contract activities will require access to pertinent portions of the database.
Any effective CBM+ database should have well-established procedures for granting access to qualified users, and should apply available data format standards and definitions to ensure viable information exchange and a consistent data product for each using function.
Collection and aggregation of CBM+ data is a common concept and a good model for the composite or “virtual” database structure. CBM+ implementers may tailor this structure based on organizational or process requirements and the availability of an effective communications capability.
Digital Analytics tools are one of the most essential parts of a CBM+ strategy. For this Guidebook, analytics is defined as the off-system aspect of condition-based health assessment. Depending on the architectural approach used for CBM+ implementation, the analytic capability will need to acquire data from all sources within the architecture using different techniques, such as data mining.
The primary function of the analytic element is to determine the current health state of equipment and project this assessment into the future, taking into account estimates of future usage profiles. Root-cause analysis and tailored analytic algorithms may support this function.
Effective use of prognostic assessment or “prognostics” can be the ultimate goal of predictive maintenance. A prognostic module must be flexible enough to accept many different sources of information to adequately and accurately predict the remaining useful life.
By predicting the remaining useful life, the prognostic capability assists the operators and managers in actively managing their maintenance resources and determining appropriate maintenance actions.
Decision Support is critical to completing CBM+ capability includes the ability to make maintenance and related support decisions based upon the available condition data.
This involves using decision-support tools to assess equipment operating reliability and availability, identify needed changes in planned maintenance requirements and equipment modifications, and track equipment operating performance for individual components, equipment or groupings of equipment.
The objective of Decision Support Tools is to predict problems or failures in time to take remedial action. Decision support includes analytic and decision-support tools to help managers at all levels identify adverse trends and assist in maintenance planning. It may also include the use of data by other sustainment providers in such areas as supply, transportation, or engineering to ensure required support is available where and when it is needed by the operating forces.
The decision-support capability acquires data from the diagnostic and prognostics analytic elements. The primary function of decision support is to recommend maintenance or engineering actions and alternatives and to understand the implications of each recommended action.
Recommendations include establishing maintenance action schedules, modifying the operational configuration of equipment to accomplish mission objectives, or modifying mission profiles to allow mission completion. Decision logic needs to take into account such factors as operational history including usage and maintenance, current and future mission profiles, high-level unit objectives, and resource constraints.
An accurate forecast of an asset’s future use needs to match the other systems planning horizon to be effective. Output from the decision-support capability should be in the form of automated notices,
computer-to-computer transactions, alerts and alarms, or other advisory generations, including health and prognostic assessments.
Human Interfaces layer may access data from any of the other layers within the architecture, such as the decision-support component. Typically, status or recommendations for health assessments, prognostic assessments, or decision recommendations and alerts would be produced and displayed to human users by the decision tools, with the ability to drill down when anomalies are reported or additional information is required.
In many cases, the human interface capability will have multiple layers of access to data across the CBM+ environment, depending on the information needs of the user. This capability may also be implemented as an integrated multiple-user interface that accounts for the information needs of users other than maintainers. The goal of the human interface is to provide operators with actionable information regarding maintenance or operations that suggest or support management or technical decisions.
CBM architecture is the fundamental organization of a system or process embodied in its components, their relationships to each other and to the environment, and the principles guiding its design and evolution.
The framework is intended to ensure design descriptions and interfaces can be compared and related throughout the product or process life cycle across organizational and functional, and joint command boundaries.
At the operational or tactical level, equipment heath assessments help operational commanders gauge the operating capabilities of weapons and equipment under their control. It also assists in maintenance decision making regarding appropriate repair actions and future equipment availability.
1. What is the impact on Maintenance Down Time (MDT)?
To adequately assess the impact of MDT, ensure the MDT of the existing system is defined, as well as the primary MDT drivers (e.g., Mean Time To Repair (MTTR), logistics downtime, etc.). The primary drivers should be key MOEs and analyzed in the sensitivity analysis.
2. How will this CBM+ system/sub‐system affect operator usability?
Identify known impacts to the CBM+ capability, and its parent system, to ensure that the appropriate qualitative as well as quantitative MOEs are included. These should include defined impacts to the system’s operation and maintenance, policy, changes to tactics, techniques and procedures and personnel.
3. How will platform health monitoring affect system performance?
In cases where the primary purpose of the CBM+ BCA is to determine the effect of a health monitoring system, ensure that the system and mission performance MOEs, metrics and investment costs are adequately defined. To the extent possible utilized available historical data/information (e.g., Operational Availability, Mean Down Time, Material Availability, etc.). Select the factors that can be defined, measured, and evaluated within your CBM+ BCA framework to ensure conclusions and recommendations are based on a fair comparison and sensitivity analysis.
4. Does the system provide any increased prognostic/diagnostic capability?
Identify MOEs and metrics (related to failure prediction, RUL estimates, spare part management, warehouse management, etc.) that can be assessed in terms of timeliness, accuracy and relevance of prognostic and diagnostic analytical tools. Also, consider any risks associated with source data, data transfer, and systems processing data, as well as with the analytical tools themselves.
5. What affect does the CBM+ capability have available combat power, system readiness and availability?
The CBM+ BCA should clearly define combat power, operational availability, and material availability for the weapon system which the CBM+ capability is being proposed and also address any unknowns or areas that could not be addressed in quantitative terms. Assumptions should include a defined set of mission reliability MOEs and explain how the unknown areas will be treated in the analysis. The CBM+ BCA conclusions and recommendations should address the quantitative and qualitative costs and benefits as well as risks associated with expected unknown areas which have not been quantified.
6. What maintenance and acquisition processes will be affected and how will they be impacted in terms of data collection, transmission, and manpower costs associated with analysis and decision making?
Identify and define functions, tasks, and related activities for maintenance, acquisition, and logistics processes (e.g., Total Life Cycle Systems Management, PBL, Focused Logistics, Joint Capabilities Integration, and
Development System, Functional Needs Analysis, Functional Areas Analysis, etc.) that is affected by or provides information about the proposed CBM+ capability.
7. How will your overall supply chain be impacted by the CBM+ Initiative?
Identify the supply system process(es) that may be affected and specific MOEs/metrics that can be used for the analysis. Utilize existing logistics and historical data/information (e.g., failure data from VAMOSC) is utilized to define baseline MOEs and related costs.
8. Will CBM+ change the way you buy and provision parts?
Identify the purchasing processes that may be affected and specific MOEs/metrics that can be used for the analysis. Utilize existing RCM and historical data/information (e.g., failure data from VAMOSC) to define baseline MOEs and related costs.
9. Can any service, DoD, Defense Logistics Agency (DLA), other infrastructure be reduced as a result of the proposed CBM+ initiative?
Identify processes, functions and staffing related to the existing and proposed CBM+ capability and utilize any RCM, maintenance or logistics analyses that maybe available as a source to define an existing system baseline. Define specific MOEs (e.g., maintenance tasks, warehousing functions, inventory management, etc.) that can be reasonably analyzed.
10. How will the various COAs or alternatives be executed i.e., acquisition strategy and what is the associated cost/risk of each course of action?
Ensure that the ability to execute and implement alternatives have been addressed in the risk assessment and sensitivity analysis. Identify and define implementation related factors in the assumptions.