intelligent management analysis...• complex cross correlation across unstructured text data...
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Intelligent Management Analysis System
Patrick HarrisJason Fairey
Bernard Laskowski
Analatom Incorporated3210 Scott Boulevard
Santa Clara, California 95054
Analatom Proprietary 2017
OutlineProblem StatementTechnical ApproachIMAS
IMAS Building BlocksIMAS Software and Hardware StructureIMAS DataIMAS IndexIMAS Analysis ‐ SoftwareIMAS Analysis – HardwareIMAS ReportingIMAS Remaining Useful Life
Overall BenefitsTechnology DeploymentProject Team ParticipantsConclusion
Problem Statement• The Intelligent Maintenance Analysis System (IMAS) enables correct assessment of
aircraft specific maintenance event context. Different environment, mission and operational contexts drive different cost metrics. The IMAS solution improves cost estimates as it overcomes the present limitations associated with making simplifying assumptions with regard to failure and reliability functions in common practice.
• Combined with CBM+ improves ability to avoid cost of non‐required maintenance.• IMAS learns human expertise and reapplies their knowledge by utilizing advanced
machine learning technologies to memorize all available data and later recall patterns that indicate a degraded platform.
• Cost savings are realized via timely resolution to problem reports. Many companies provide CBM+ analytics capabilities; however many fielded systems have limited scalability and this increases overall costs. IMAS is designed to be scalable.
Technical Approach• Complex Cross Correlation across unstructured text data including Customer Service
Requests, Service Bulletins, Engineering Analyses, Faults, Maintenance Actions (MX) and numeric data including system wide Data Flight Data Recorder (DFDR) and Engine Health Management (EHM) monitored parameters.
• Engine vibration and turbine gas temperature sensors.
IMAS Building Blocks: Data (A), Fast Index (B), Analysis (C), Report (D)
IMAS Software and Hardware Structure
IMAS Data• MX Remis text data
• SHM, DFDR and EHM numerical data
IMAS Index• Associative Memory
– Content addressable– The ability to correct faults if false information is given– The ability to complete information if some parts are missing– The ability to interpolate information, in other words if a pattern is not currently stored the most
similar stored pattern is determined.
• Associative Memory Numerical Data – Fast Index
IMAS Index (Continued)• Associative Memory Text Data – Fast Index
# Narrative
1 Ramp actuator
2 Ramp panel corrosion
3 Hydraulic actuator leak
XML Table (2D)
# Panel Ramp Actuator Corrosion Hydraulic Leak
1 1
2 1
3 1
Word 1
# Panel Ramp Actuator Corrosion Hydraulic Leak
1 1
2 1
3 1
Word 2
# Panel Ramp Actuator Corrosion Hydraulic Leak
1
2 1
3 1
Word 3
DataImport
IMAS Analysis ‐ Software• Current methods are obtuse
A neural network can often tell you “if” something is wrong but not “why.”Other systems ignore complex variable interactions.
• IMAS Mutual Information (MI)Determination of Commonalities
The two shapes share two partsKolmogorov Complexity
Maximal CompressionK(Stream 1) = Smallest data stream to provide Stream 1
Mutual InformationMI(Stream 1, Stream 2) = K(Stream 1) if given all of K(Stream 2) for free.Pairwise associations require (N^2+1)/2 MI calculations
IMAS Analysis – Software (Continued)• MI Heatmap
IMAS Analysis ‐ Hardware• MI is time consuming
Perform parallel computingField Programmable Gate Array (FPGA)Complex Programmable Logic Device (CPLD)Graphics Processing Units (GPUs)
GPU cores have over 1000 cores for desktop chipsEach core can read from the same memoryWriting requires either synchronization or independent memory spaceIndustry supported, consumer productOpen source software support
• CPU Cluster versus CPU Cluster with FG MPI and GPUsSets of GPU cores can each run for a pair of variablesMany pairs overlap data requirements
Memory transfer can be reducedResulting information is a vector of MI values
Not much reverse memory transfer requiredFG MPI and GPU accelerate IMAS MI execution by a factor of 40 compared to solely cluster of CPUs.
IMAS Reporting Maintenance Data• Graphical Visualization Maintenance
IMAS Reporting Engine Health Management Data• Visualization engine temperature and vibration are time ordered
It is difficult to integrate across time and see interesting correlations that may exist.
For humans and data mining methods, time domain oriented obscure intermittent condition co-occurrences of parameter trajectories.
IMAS Reporting Engine Health Management Data (Continued)
Engine temperature and vibration related dynamics illustratedAnalatom Patent Pending Transform
The transform finds related metrics and dynamics that arecontextually dependent.
Analatom Proprietary 2017
X-Axis in months
IMAS Reporting Engine Health Management Data (Continued)
• Visualization DFDR, EHM and MX Data
Anomalous 747 Flight found by applying differential similarity across routes.
IMAS Different Aircraft – DFDR MGT Engine Data
Normal Engine DFDR Measured Gas Temperature (MGT) parameters are all correlated.
IMAS Different Aircraft – Similar Parameters Profile
Aircraft X MGT4 (red) separating from other MGT parameters.
Normal Engine MGT parameters are all correlated.
Aircraft Y MGT4 (red) separating from other MGT parameters.
Measured Gas Temperature (MGT) SHM MGT averaged over 4 enginesDFDR MGT1, MGT2, MGT3, MGT4
SHM Data DFDR Data
IMAS Different Aircraft – Degradation ProfilesIndicate Months Before End Of Life
Remaining Useful Life (RUL) determined from onset MGT increase to MGT maximum
Overall Benefits • Cost Benefit Analysis applied to just the C‐130 conducted show an estimated cost savings
of $153 Million over the period FY 2015 through FY 2025 with an ROI of 17.99.
• Provides effective cost policy / scheduling options rationalized by appropriate actions that prevent in‐service failure and/or unscheduled maintenance that can be validated across the fleet.
• Reduce the amount of labor time and cost associated with analyses processes for RCM; serve as a reliable data source for cost analysis and fusion of maintenance activities with aircraft sensor data; indirectly supports condition‐based maintenance plus (CBM+) by improving RCM data analysis processes.
• Efficiently find complex patterns by overlaying and integrating maintenance event data stream with health usage monitoring system sensor data streams. Discover, correlate, and recall complex patterns between sensors, faults, fault isolation manuals, and maintenance tasks.
Technology Deployment• MECSIP and Airworthiness C‐130 Hercules Division (AFLCMC/WLNEB): provides point of contact to serve
as project coordinator throughout the technology maturation and transition effort; monitor efforts and facilitate technology insertion with all stakeholders; provide engineering and technical assistance for integration of the technology into system platform; provide liaison with accreditation/approval authorities and systems; and participate in funding and technology transition processes as stated in this document.
• Technology Insertion Branch (AFSC/ENR OL‐Robins): provides technology transition assistance to all stakeholders throughout the maturation and transition insertion effort; coordinates funding and contractual activities through successful transition.
• Analatom (Contractor): primary contractor responsible for technology maturation and transition effort. Responsibilities include software development, testing and integration; compliance with cost, schedule, and performance requirements for each contract vehicle; management of all subcontractor efforts; and maintaining open communication with all stakeholders.
• Mercer Engineering Research Center (MERC) (Integrator): end‐user with ability to provide PM and RCM analysis services demonstrated through the accomplishment of the C‐130 Mechanical Equipment and Subsystem Integrity Program (MECSIP) and CBM+ program tasks. Provides the primary interface between Air Force and Analatom aiding in technology transitioning by providing maintenance data for technology development and serve as beta‐test site.
• National Center Manufacturing Sciences (NCMS): provides program management.
Project Team ParticipantsName Organization Contact Information Title
Timothy Floyd AFLCMC/WLNEB (478) 327‐9028 [email protected]
Air Force MECSIP and AirworthinessC‐130 Hercules Division
Feraidoon Zahiri WR‐ALC/ENSNTechnology Insertion Branch
(478) 327‐[email protected] Air Force SBIR Program Manager
Bernard Laskowski Analatom, Incorporated 408‐980‐[email protected] SBIR Firm President
Dave French Mercer Engineering Research Center (MERC) (478) 953‐6800dfrench@merc‐mercer.org MERC Director of Advanced Programs
Bill Chenevert National Center Manufacturing Sciences (NCMS) (734) 995‐[email protected] Senior Program Manager, Sustainability
Conclusion• Contextually linked text and sensor dynamics are indexed.• Queries against index finds similar profiles across aircraft and fleets.• Engineering investigation times are reduced saving time and labor.• Scheduling based on data driven RUL curves provides larger windows to
fix before failure, avoiding associated costs.