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8/3/2019 53rd Hsrmc Hums Aomal 4164a
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Smiths Aerospace
www.smiths-aerospace.com 2006 by Smiths Aerospace: Proprietary Data
CAA HUMS Research: Demonstration of Advanced HUMS Anomaly
Detection System
HSRMC Meeting, 16 November 2006
Presentation by: Brian Larder
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CAA research on Advanced HUMS Data Analysis
Identified HUMS needs
Improved fault detection performance e.g.Cracked AS332L bevel pinion missed byHUMS
Reduced false alarm rate
Reduced management workload
Operator expertise/workload
Threshold management
Proliferation of different systems
Previous successful application ofunsupervised learning techniques
Analysis of gearbox seeded fault testdata by MJAD
CAA awarded HUMS research programto Smiths Aerospace, in partnershipwith Bristow Helicopters
AS332L MGB Bevel Pinion
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CAA HUMS research programme
1. Development of Anomaly Detection technology and system
2. Off-line demonstration on a historical database of BristowSuper Puma IHUMS data, plus Scotia data for cracked BevelPinion
3. Six month live trial of Anomaly Detection System on SuperPuma fleet by Bristow Helicopters at Aberdeen (completes22 November)
4. Follow-on development based on trial experience to furtherenhance system capabilities
Bristow will continue operating the system during this interim period
5. Six month trial extension period
6. Additional technology development and demonstration inparallel with trial extension period
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Introduction to Anomaly Detection
Used for all kinds of applications
The underlying theme is that there is no large library of tagged fault data with whichto train a model
Conceptually simple Build a model of normal behaviour
For a new sample, assess its fit against this model
If the fit is not within a models threshold then flag it as anomalous
Nearly all approaches assume a set of normal data is available toconstruct a model of normal behaviour.
Anomaly detection is usually difficult but HUMS data present significant
additional challenges.
Gearboxes tend to occupy their own space of normality (e.g. vibration levels varybetween gearboxes)
The condition of the training data is unknown. (Due to the lack of feedback fromgearbox overhauls, we must expect any training set to contain some anomalousdata.)
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Development of Advanced Anomaly Detection process
to address challenges of HUMS data1. Data pre-processing
Remove outliers and extract trends
2. Data fusion and modelling
Construct unsupervised probabilistic cluster models forgroups of HUMS CIs (separate models for each shaft andeach form of pre-processing)
Define a detailed model of the data density distribution
3. Model adaptation for anomaly detection
Adapt model to suppress regions that are likely to beassociated with outlying data
This eliminates the potential fault masking effect on anyanomalies contained in the training data set, resulting in arobust anomaly detection process
4. Perform anomaly trending
For each acquisition and each model, output a predictedfitness score, which is a fusion of all indicators used toconstruct a model
Detect anomalies using both the absolute values and trendsof the gearbox fitness scores
tgq
,
Mixture model
Query predictions executedthrough ProDAPS
Score over space Adapted modeltgq
,
Mixture model
Query predictions executedthrough ProDAPS
Score over space Adapted model
Mixture modelMixture model
Query predictions executedthrough ProDAPS
Score over space Adapted model
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
rest of fleet
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
rest of fleet rest of fleet
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
Gearbox g, component c, over
time t
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Development of web-based anomaly detection system
The anomaly detection system operates as a secure web server, located atSmiths in Southampton
HUMS data automatically transferred overnight from Bristows Web Portal
Data automatically imported into the HUMS data warehouse and analysed
Bristow have a remote secure login to the system to view results at any time
www
HUMS Data onWeb Portal HUMS DataWarehouse
BHL HUMS Type
Engineers PC
BHLAberdeen
SmithsSouthampton
Other BHLEngineers PCs
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Example Live Trial findings
Previously demonstrated successful detection of Scotia cracked MGB Bevel Pinion
No IHUMS indication
G-BWZX MGB 2nd stage epicyclic ring gear - abnormal ESA-SD/ESA-PP and SIG-SD/SIG-PPtrends (multiple sensors)
Gearbox rejected 2 days later for metal contamination (20/3/06). No IHUMS indications
G-TIGT TGB input abnormal ESA-SD/ESA-PP and SIG-SD/SIG-PP trends
Gearbox rejected for metal contamination (31/12/05). No IHUMS indications
G-BWXZ oil cooler fan - abnormal SON trend
Fan believed to have been rubbing on casing. Corroborated IHUMS alerts
LH and RH AGBs detected abnormal trends on multiple aircraft Some AGBs are on close monitor. Trends not so apparent in IHUMS
G-TIGG and G-TIGJ IHUMS DAPU - detected DAPU problem as anomalies moved with theDAPU when this was swapped between aircraft
No IHUMS indications
G-TIGC MGB epicyclic stage high and variable trends from multiple components analysedfrom sensor 7
Possible wiring harness problem? No IHUMS indications
MGB sensors abnormal SO1 and SON trends
Detected multiple sensor problems. No IHUMS indications
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On-going research, building on initial trial experience
Further development part 1
Model tuning and re-modelling to optimise the anomaly alerting.
Review of data pre-processing methods to attempt to improve trend detectioncapabilities.
Implementation of influence traces to automatically identify which IHUMSindicators are driving an anomaly indication.
Implementation of a probabilistic alerting policy, which will also normalise the
fitness score outputs across shafts.
Six month trial extension period
Further development part 2
Further improve system effectiveness and usability through the application of
automated reasoning to the outputs from the anomaly detection process. Anomaly model can be embedded in a probabilistic reasoning network.
Data mining of anomalous trend features to test established diagnosticknowledge and further develop this knowledge.
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Summary
An advanced HUMS anomaly detection system has beensuccessfully developed and implemented
A live trial by Bristow Helicopters in Aberdeen has shown
that: The system is user friendly, with a clear and simple-to-use interface
The system is detecting faults that are not being seen by theIHUMS
The system is also providing much better visibility of IHUMS
instrumentation problems (if these are not addressed, monitoringcoverage is lost)
This system is providing fresh insight into HUMS datacharacteristics
It is sometimes difficult to interpret the significance of IHUMSCondition Indicator trends that are shown to be anomalous
The system is increasing HUMS effectiveness and usability
On-going developments will further enhance systemcapabilities