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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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

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Mixture model

Query predictions executedthrough ProDAPS

Score over space Adapted modeltgq

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Mixture model


Score over space Adapted model

Mixture modelMixture model


Score over space Adapted model

Gearbox g, component c, over

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rest of fleet


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rest of fleet rest of fleet


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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

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53rd Hsrmc Hums Aomal 4164a