1. Department of Mechanical Engineering and Mechanics
Intelligent Maintenance Systems: Vehicle Health and Usage Systems MFPT 2014 Conference Norfolk, Virginia
Dr. John Lacontora
Melvin Mathew
Department of Mechanical Engineering and Mechanics
Drexel University
21 May 2014
Thank you Mr. XXX and Good Morning to all of you. As Mr. XX mentioned, my name is Melvin Mathew and I’m a graduate student with the Department of Mechanical Engineering and Mechanics at Drexel University. Over the span of a year I’ve been working alongside Dr. Lacontora and with the Intelligent Maintenance Systems Lab at Drexel in studying Health and Usage Monitoring systems on various platforms. We’ve reached leaps and bounds from where we started, and have reached through to model development which from here on would lead into experimentation. In this paper then I try to propose a framework of an Intelligent Maintenance System that can be developed for implementation on different vehicle systems.

2. Introduction
Over the last two decades, manufacturers and owners of complex systems have been looking into ways to significantly increase system reliability and reduce cost of ownership.
Intelligent Maintenance Systems have since emerged as the necessary health monitoring tool enabling owners to determine the useful life of life limited parts by analyzing diagnostic trends over time.
The modern rotorcraft industry has since taken significant leaps in the direction of developing Health and Usage Monitoring Systems (HUMS), enabling automated and semi automated diagnostics and prognostics information for technical support.
Over the last two decades industries have increasingly been hearing terms such as efficient performance, increased reliability, increased safety factors, higher cost savings, six sigma operation capabilities. The purpose here that is being raised by owners operators and corporates alike is to reduce the cost of ownership of assets. In the case of high value assets such as aircrafts, ships, bridges, any form of reduction in ownership costs will be significant in the long run. The aim then of an Intelligent Maintenance System is then to provide for these needs. The essence here is to develop a health monitoring tool enabling owners to determine the useful life of life limited parts, through scientific and technological evidence, thereby reducing maintenance costs, unexpected breakdowns and the high costs incurred as collateral.
And since, the modern rotorcraft industry has taken a special interest in health monitoring systems and have enabled the growth of this research field.

3. Motivation Primary Secondary Reduction in cost of ownership
The end user industries that incur high ownership costs via asset management costs such as maintenance personnel, parts replacement, etc. would be the targeted first user clients.
Primary
Reduction in cost of ownership
Operation
Lifecycle
Secondary
Improved Operational Availability
Known component state
Shows promise for use in unmanned system domains
This led us to the declare the motivation and the goals of our research and the Lab as a whole.
On a strategic scale the number of end user industries that will be impacted by the availability of a robust system capable of providing these deliverables would be astonishing.

4. Intelligent Maintenance Systems (1/2)
An optimal IMS system would be capable of analyzing data trends from input values across multiple sensor regimes, inclusive of possible data fusion for comprehensive decisions.
The aim is then to be able to create a system that can alert maintenance personnel exactly when the parts need to be changed
Watchdog Agent Toolkit
Watchdog Agent Toolkit is capable of providing degradation assessments based on readings from multiple sensors measuring critical values for the system under consideration [2].
Its prognostic capability lies in analyzing data trends and subsequent statistical modelling allowing it to predict future behavior and forecast health.
Its diagnostic tools is also capable of analyzing signature patterns, enabling it to recognize similar events witnessed in the past or alert personnel in the case of undeterminable trends.
The essence then of an IMS is to observe and analyze the health of the vehicle, be it on the scale of how its used, or the scale of how individual components are conditioned via Non-Destructive Evaluation methods. This would imply that there would be numerous sensors placed strategically across the body of the vehicle to pick up changes or alerations in state. Live data will need to be picked up and assimilated from multiple sensor points, which needs to be processed for diagnostics and prognostics and then further make decisions and subsequently alert the necessary staff.
The cost savings through this system would be all about timing. The system should be able to predict exactly when a part of the vehicle needs to be replaced or go through thorough inspection. Changing a part earlier than required would reduce operational availability to no good end, cost money for labor and spare parts and changing a part after it has failed could be disastrous and possibly fatal.
For this comprehensive computing a robust software platform must be established capable of handling high data loads, processing capabilities and trend analysis algorithms. And that is how we came across the Watchdog Agent Toolkit, developed by the University of Cincinnati and hosted on the LabVIEW platform, this software is capable of providing degradation assessments based on reading from multiple sensors. It is capable of recognizing diagnostic trends that have been observed before and can move to make decisions accordingly or bad comes to worse, alert the necessary service station regarding a flawed reading from the sensors or an unobserved trend for validation.
Further to this cause the Watchdog Agent Toolkit is capable of providing prognostic capabilities, through statistical modelling to allow the system to predict remaining useful life and expected behavior.

5. Intelligent Maintenance Systems (2/2)
The Watchdog AgentTM has elements of intelligent behavior that enable it to answer the following questions:
When is the observed process, or equipment going to fail, or degrade to the point where its performance becomes unacceptable?
What is the current condition of the equipment in use?
Why is the performance of the observed process, or equipment degrading?
Thus this software provides the computing and processing capability of answering the questions that the goals of this lab wants to focus on.
The software would then be working with the framework you see here. The toolkit can be programmed to wear various hats across various systems. Subsequently the end aim being to provide a comprehensive view on the health of the system, future behavior and health predictions and a lean maintenance schedule.
Sensor values received into the toolkit can be compared against models developed for threshold values (which on a feedback loop, or via an authorized engineer is altered over time and usage) to provide diagnostic and prognostic knowledge. For all these reasons we believe that the Watchdog Agent would be the best kit for our work.

6. Health and Usage Monitoring Systems
The HUMS system is the crux of the overall IMS framework consisting of CBM and UBM tools.
Rotorcraft HUMS typically performs vibration monitoring and exceedance monitoring, to detect mechanical failures [4], in addition to usage and regime recognition
The Boeing Company recently conducted a study in support of the US Army CBM program that combined usage monitoring and the condition of components to grant credits to life-limited rotorcraft components [3].
Aircraft SHM programs enable
(a) efficient fault detection, isolation, and recovery,
(b) prediction of impending failures or functional degradation,
(c) increased reliability and availability,
(d) enhanced situational awareness for crew and support personnel,
(e) condition-based and just-in-time maintenance practices and
(f) efficient ground processing and increased asset visibility and availability.
Now the IMS framework is the whole web including owners, asset manager, maintenance personnel, OEM technicians, and Fleet data managers, some automated, semi-automated or completely manual. But for this spread apart framework the HUMS is the brain of entire system. As I had mentioned, the HUMS works with Non-destructive evaluation methods. This can be achieved through vibration monitoring say for shaft misalignments, oil debris analysis for gear chip and wear, or even acoustic emission technology to detect incipient crack growth in the frame.
Now the health of a system can be monitored in two ways. One is to check how the vehicle is being used. Is the aircraft being flown on maneuvers that would regularly create high stress values on its structures or components thereby reducing useful life. Or is the aircraft flying very well within dictated standards, meaning the parts are not undergoing the stresses it was designed to withstand. This would essentially be known as Usage Based Monitoring or UBM.
Another approach could be to take a more deep dive look into how the individual components are during operation. Analyze the “condition” of each subcomponent and with an intrinsic knowledge paint the bigger picture of the overall structure.
In comparison both then reflect as a big to small and small to big failure prediction tactic.
Recently the Boeing company along with the US Army combined usage monitoring and component conditional knowledge to grant credits to life limited rotorcraft parts. And since then it has become apparent in the CBM program that usage monitoring is a tenant of the program and a subset of a bigger System Health Management platform.
The dictated terms of an aircraft SHM program is then to enable ….

7. Usage Based Maintenance
UBM is a method of recording and analyzing comprehensive knowledge of the actual aircraft usage.
The usage patterns, based on parameters such as bank angles, G-forces, landing gear forces, etc. can be recorded and analyzed for fatigue levels of the structural components.
Usage monitoring uses Regime recognition algorithms to accomplish this by recording data when the aircraft enters preset maneuvers that would have higher than threshold stress values on parts.
Clustering algorithms can be employed to analyze the output of onboard RR Algorithms (RRA). The RRA’s output would be a listing of regimes flown along with the time spent in those regimes. All event based maneuver occurrences for the flight are also identified [3].
Based on the usage spectrum of the aircraft. System will start recording data, once the flight enters a preset maneuver threshold.
From this, usage spectrum regime recognition algorithms will be used to analyze the maneuver ID exactly.
Damage faction calculators can then be used to analyze the levels of stress that is involved and the fatigue levels suffered by the framework.

8. Condition Based Maintenance (1/2)
CBM is based on the theory that accurate wear and failure of a component is predicted through analysis [1].
CBM methods are enabled to actively/passively detect the present operating condition of individual components/equipment thereby providing live information regarding intrinsic health to maintenance personnel.
This can predict the failure of individual components before subsequent wear and tear causes catastrophic failure or collateral damage.
FMECA
Physical Models
Sensors
Processing
Detection Algorithms
Data Analysis
Prognostics
Component State
This is the small to big approach of failure prediction. The aim is to analyze the given state and condition of individual components to draw the bigger picture on the health of the aircraft. The advantage of this spectrum is that it gives the user intrinsic knowledge on the health of each individual monitored component, enabling maintenance to change the parts as per condition indicators.
The process involves multiple stages and a vast array of sensors placed across the body of the aircraft. Due to economic reasons and for factors of power considerations, the number of sensors has to be limited to deliver an optimal reading into the health of the aircraft without costing too much. The first step in this is then to carry out a failure mode and criticality analysis. This analysis basically identifies potential failure possibilities and its overall impact on the mission critical parameters, functionality and safety of the aircraft. Once critical failure modes are identified, sensors are placed around the body accordingly to observe potential failures.
From here then the subsequent sequence of data processing follows, it could include raw data analysis or fused data analysis. Data integrity checks, diagnostic analysis, trend setting and then prognostic analysis.

9. Condition Based Maintenance (2/2)
The US Army ADS-79C-HDBK [1] has set up four goals regarding CBM implementation:
reduce current maintenance burden required for assurance of airworthiness,
increase availability of the aircraft,
improve flight safety, and
reduce overall maintenance costs.
This advisory circular also describes elements that enable the possibility to issue CBM credits by measuring condition and actual usage of a component, in order to change inspection intervals and removal criteria. CBM depends on data collection from the various sensors that is processed, analyzed and correlated to set material conditions that requires maintenance.
Since the work of Boeing in collaboration with the US Army, an advisory circular ADS-79C has been released which list four goals with regard to implementation of CBM on aircrafts . They are to :………
This circular further goes on to suggest methods to use CBM to grant credits to the aircraft, via validated, and calculated remaining useful life assessments based on the data from the condition based analysis. This is the how we can provide for a lean maintenance schedule, via enabling changes in inspection intervals to a more optimal, need by need basis.
Although the field is still nascent, the growing amount of fleet data regarding failure patterns and threshold levels will enrich the models that we can use to compare live aircraft data against threshold values to analyze the condition and reliability of each component and the aircraft as a whole.

10. CBM/UBM Credits
CBM/UBM Credit: The approval of any change to the maintenance for a specific end item or component, such as an extension or reduction in inspection intervals or calculated retirement time (CRT) established for the baseline system prior to incorporation of CBM/UBM as the approved maintenance approach
Health and Utilization Management Systems (HUMS) credit (e.g., UBM or CBM) can be defined as the approval of a HUMS application that adds to, replaces, or intervenes in previously approved maintenance practices or flight operations for non-HUMS equipped systems.
Now since the aircraft industry is very stringent on its safety regulations, as it very much needs to be, methods and regulations have to be devised to grant changes in maintenance intervals to parts of the aircraft or component. This is then what would be termed as granting UBM/CBM credits. This process would have to go through regulated procedures, anti corruption softwares, encryption packages and must maintain a certain confidence level to be approved for granting credit.
Similarly the HUMS credit application would be the approval of a HUMS application that adds to, replaces, or intervenes in previously approved maintenance practices or flight operations for non-HUMS equipped systems.

11. CBM IMS Notional Framework
CBM/UBM
Sensor Data
Fleet Intelligence Maintenance System
Maintainer
Operator
Tech Rep
Vehicle
Characteristics
FMECA
Data
Virtual Logbook Data
Vehicle Health Monitoring Sys and PSS
Prognostics
Fleet Maintenance Management
LVC
Training
LVC Performance Support
This is then the cumulative end of our research and the framework with which we want to propose our IMS.
This framework is intended to demonstrate the flow of data and logic across the web of design to achieve the objectives set by our lab. …..
The effectiveness of the framework is based on the initial and later versions of the Failure Mode Effects and Criticality Analyses of the system and its components

12. Conclusion
In the long run, implementation of IMS systems on high value aviation assets should prove to be an essential component of ownership.
The improved safety factors and cost savings provide a valid return of investment.
The US Army’s investment in HUMS showed a 12-22% decrease in parts cost per flight hour for HUMS-equipped helicopters between 2007 and 2009 [4].
However extensive validity criteria, regulatory approval and back up system must be made available to establish IMS’s as a mandatory component of a vehicle.
Questions that need research foci would be:
Are regime based clustering algorithms appropriate for predicting remaining useful life of life limited components?
Can IMS also serve as a performance support system for logistics support personnel and maintenance technicians?
Can a CBM IMS serve to enable autonomous systems to maintain safe operation parameters?

13. References
D. B. Cripps, “Aeronautical Design Standard Handbook for Condition Based Maintenance Systems for US Army Aircraft Systems” ADS-79C-HDBK, January 2012.
J. Ni, J. Lee and D. Djurdjanovic, “Watchdog – Information Technology for Proactive Product Maintenance and Its Implications to Ecological Product Re-Use”.
P. Shanthakumaran, T. Larchuk, R. Christ, D. Mittleider and E. Hitchcock, “Usage Based Fatigue Damage Calculation for AH-64 Apache Dynamic Components”, May 2010.
US Joint Helicopter Safety Implementation Team, HFDM Working Group, “Health and Usage Monitoring Systems Toolkit,” International Helicopter Safety Team, February 2013.

14. Thank You ?