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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    2006 by Smiths Aerospace: Proprietary Data

    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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    2006 by Smiths Aerospace: Proprietary Data

    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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    2006 by Smiths Aerospace: Proprietary Data

    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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    2006 by Smiths Aerospace: Proprietary Data

    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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    2006 by Smiths Aerospace: Proprietary Data

    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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    2006 by Smiths Aerospace: Proprietary Data

    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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    2006 by Smiths Aerospace: Proprietary Data

    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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    2006 by Smiths Aerospace: Proprietary Data

    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