ltp data analysis: statushewitson/presentations/... · ltpda status, swt meeting, estec, feb 2nd...

LTP Data Analysis: Status

M Hewitson for the LTP DA TeamSWT Meeting

ESTEC, Feb 2nd 2010

+----------------------------------------------------+| || || **** || ** || ------------- || //// / \\\\ || /// / \\\ || | / | || ** | +----+ / +----+ | ** || ***| | |//-------| | |*** || ** | +----+ /+----+ | ** || | / | || \\\ / /// || \\\\ // //// || ------------- || ** || **** || || Welcome to LTPDA Toolbox || || Version: 2.2 || Release: (R2009a) || Date: 23-09-09 || |+----------------------------------------------------+

LTPDA Status, SWT Meeting, ESTEC, Feb 2nd 2010

Contents

• Toolbox• new features• releases

• Current activities• in labs• algorithms• MDC3• STOC Exercises

• Project status• release and testing schedule

2


The Team

3

Adrien Grynagier

Martin Hewitson Miquel Nofrarias Ingo Diepholz Anneke Monskey Heather Audley

Michele Armano Marc Diaz Aguiló

Andrea Manica Andrea Mattioli

Mauro Hueller Luigi Ferraioli

Giuseppe Congedo Fabrizio De Marchi

Boutheina Rais


DA Infrastructure

4

MUST (raw data) Converter Repository

Web interface

LTPDA Toolbox


LTPDA Repository

5

RepositoryMySQL Server

Apache Web Server Web browser

LTPDA (MATLAB)


Repository Status

• Version 2.2 just released• fully functional web interface:

• user interface• administration interface

• built-in ‘robot’ for dumping objects to XML store• supports simple update procedures• guided installation through web-browser

• In (heavy) use (10’s of thousands of objects)• AEI

• MDCs, LTPDA Testing, OMS hardware tests• UTN

• MDCs, LTPDA Testing, FE tests• STOC

• LSS testing, LSS and STOC Exercises• Glasgow, Birmingham, Imperial

6


DA Infrastructure

7

MUST (raw data) Converter Repository

Web interface

LTPDA Toolbox


DA Infrastructure

8

LTPDA Toolbox

GUIs

Algorithms Utilities

Repository Client


Graphical User Interfaces

9


Algorithms

10

AO

• Standard data container for• y-data, xy-data, time-series, freq-

series, xyz-data• Algorithms for• spectral estimations• linear and non-linear fitting• filtering• whitening• standard math

MFIR/MIIR/Filterbank

• Implementation of digital filters• Compute response• Create from various models• pole/zero models• statespace models• ....

SSM

• Implementation of statespace models

• Many ‘built-in’ models for LTP• Methods for• assembling systems• setting parameters• discretisation• extracting TFs• time-domain simulations

pzmodel/parfrac/rational

• Implementation of transfer functions• pole/zero models• partial fractions• rational function

smodel• Parametric models of 1 dimension• Parameter substitution• Evaluation to AO• Various symbolic transformations• fourier, ifourier• laplace, ilaplace• diff, ztrans, ....

matrix/collection

• Containers for other LTPDA objects• matrix: e.g. 2D array of AOs• collection: cell-array of objects

• Various methods• matrix: filtering, det, math, conj, ...• collection: useful for repository

interaction

... and much more...


Repository Client

11

• Interact with LTPDA repository from within MATLAB• query repository to search for objects• submit one or more LTPDA object to repository• retrieve one or more LTPDA object from repository• update data for an object in the repository• move/copy objects between repositories (coming soon)


Current DA activities

• Toolbox development• structure mostly done • work still needed on algorithms

• maintenance, bug fixing, final delivery• Analysis preparation• mock-data challenges• STOC exercises• technical notes

• Lab hardware testing• OMS EM test campaign in Hannover• Torsion pendulum + FM testing of GRS in

12

LTPDA Status, SWT Meeting, ESTEC, Feb 2nd 2010 13

LTP DA Usage


OMS Test Campaign

• Compliment the formal analysis by Astrium• Heavy use of repository• Very realistic treatment of • telemetry packets• analysis routines• ‘real’ data

14


Data flow

15

RAW DATA FILE(128.3,128.4)

RAW DATA AO

RAW DATA AO

RAW DATA AO

RAW DATA AO

TEST CASEAO

SubmitToRepository CreateTestCase

Definitionfile

Method does:

1. Reading Raw data files (128.3,128.4) in HEX format

2. Split regardless of experiment, by: - If gap > 1s - or if duration > 3600s - or if SID changes 3. Create Raw data AOs and sets

unit,name and description according to definition file.

4. Raw data AOs defined by Metadata: - Exp. title = 'OMS EM Raw data' - reference_id = 't0' - quantity = package ID (e.g. 128.3)

Method checks: 1. Is sampling frequency ok? 2. Do we get the expected number of AOs?

Method does:

1. Create AOs according to test step-by-step 2. Adds definition file information to

AO (files needed for calibration) or to Metadata (experiment id,etc.).

3. Collapse X's to fs =10 if check is ok, if not keeps original X's.

4. TestCase AO defined by: - Exp. title = 'OMS-C-K1' - reference_id = 'S4.1-12' Method history:

Contains history steps up to Raw data AO. No Raw datafile needed to rebuild

% Global fields

TestCase.experiment_title = 'OMS-C-K1';

TestCase.keywords = 'TP-3045,DWS-K1,DWS-C1';

TestCase.additional_authors = '';

% Step 4.1 - 4.12

n = 1;

TestCase(n).experiment_description = 'S4.1-S4.12: wipe piezos [-8V,8V], adjusting piezo manually ';

TestCase(n).analysis_description = 'Splitting by SID';

TestCase(n).quantity = {'128.3', '128.4 (SID9001)'};

TestCase(n).reference_ids = 'S4.1-12';

TestCase(n).description = 'OMS_Param_1p7_1p1_1p0_1p1, OMS-C_COEFF-L1.';

TestCase(n).additional_comments = 'OMS_Param_1p7_1p1_1p0_1p1, OMS-C_COEFF-L1.';

TestCase(n).t1 = [date '08:54:36.883'];

TestCase(n).t2 = [date '13:02:00.883'];

Repository

Definitionfile

HEXconverter % dtype % name % unit % description

addDef(18, @omsHex2single, 'tsdata', 'HORIZ_DWS_F', 'rad', 'LST15683');

addDef(19, @omsHex2single, 'tsdata', 'VERT_DWS_F', 'rad', 'LST15684');

addDef(20, @omsHex2single, 'tsdata', 'HORIZ_DWS_R', 'rad', 'LST15700');

addDef(21, @omsHex2single, 'tsdata', 'VERT_DWS_R', 'rad', 'LST15701');

addDef(22, @omsHex2single, 'tsdata', 'HORIZ_DC_R', 'rad', 'LST15698');

addDef(23, @omsHex2single, 'tsdata', 'VERT_DC_R', 'rad', 'LST15699');

addDef(24, @omsHex2single, 'tsdata', 'CTAST_1', '', 'LST15663');

addDef(25, @omsHex2single, 'tsdata', 'CTAST_12', '', 'LST15680');

addDef(26, @omsHex2single, 'tsdata', 'CTAST_F', '', 'LST15697');

addDef(27, @omsHex2single, 'tsdata', 'CTAST_R', '', 'LST15714');

addDef(28, @omsHex2single, 'tsdata', 'HORIZ_DC_F', 'rad', 'LST15681');

addDef(29, @omsHex2single, 'tsdata', 'VERT_DC_F', 'rad', 'LST15682');

Data flow


Handling telemetry packets

• Repository contains two RawData AOs for EM campaign• Laser House-keeping [ Pack.ID: 3.25 (1Hz) ]• Telemetry [ Pack.ID: 128.3/128.4 (10Hz)]

• RawData AOs are created from telemetry data• set basic properties (name, units,...)• sampling frequency is not exactly 10Hz due to DMU time

stamping• Individual RawData AOs are created if:• gap > 10 samples• duration > 3600s• SID changes

• RawData AOs are identified in the repository by• Experiment Title (e.g. ‘OMS EM Raw Data’ )• Name (e.g. 'FAST_LOOP_CTRL_OUTPUT32')• Timespan

16


Example

17


New functions to handle telemetry

18

omsSplitFileBySegment Splits by segments. A segment is specified by: 1.) The package ID changes (only TLM 128.4) 2.) ‘Large’ gap 3.) The file exceeds the max number of seconds

omsSplitLA_HK_FileBySegment Idem. for Laser Assembly House-keeping

omsSubmitFilesToDatabase Submits AOs collections to repository

omsSubmitLA_HK FilesToDatabase Idem. for Laser Assembly House-keeping

omsIdentifyGaps Find gaps in the original telemetry package


Functions for analysis

19

oms_em_retrieve_raw data built-in model: gets AO from repository given timespan

omsGetTelemetry (beta)

Used to get relevant information for the analysis stored in telemetry/telecommand. Eg: signal amplitude, sig. frequency, signal t0,...

analysis_OMS_OPD Full characterisation of OPD control loop: OLG, CLG, delay, fit pole/zero andmoldel

analysis_OMS_Frequency Idem. for Frequency Loop

analysis_OMS_Cs Get phasemeter calibration factors

analysis_OMS_Ks Get interferometer geometric calibration factors


Extracting the goodness

• Utility scripts can be used • to enhance AO converter• in further test campaigns (LA FM, OMS FM)

• Analysis scripts can be directly converted to LTPDA methods and used in the mission

20


Example: Frequency Loop analysis

21

Frequency Control loop: all loop variables as DMU variables

G

HSlow

ψR

xs

ε

xo

HFast

OPD_LOOP_CONTRL_OUTPUT32

OPD_LOOP_CONTROL_ERROR32

OPD_LOOP_VAR_SETPOINT32

LONG_PHASE_R32


Frequency Loop analysis: the data

• LSM10031: 128.4 telemetry (user-defined data package)• SID set to 9030 to get on-line telemetry • Data: (x0,xS,ε,ᴪR) at 10Hz

• LSM10031: 128.4 telemetry (user-defined data package)• SID set to 9031 to get Interferometer Data Log (IDL)• Data: (x0,xS,ε,ᴪR) at 100Hz

• LSC10127: start IDL download tele-command • Data: Start time for IDL data

• LSC10088: set Testing Frequency Offset tele-command• Data: Start time, amplitude, frequency for the sinusoid

• LSC10129: stop Testing Frequency Offset tele-command• Data: Stop time for the sinusoid

22


Frequency Loop: get AOs from repository

23

date = ['2009-12-02',' '];t1 = [date '09:00:00.100'];t2 = [date '12:00:00.000']; % get 9030 objectsname = 'FAST_LOOP_CTRL_OUTPUT32';

pl = plist(... 'database' ,'oms_em_tests',... 'package ID' ,'128.4 (SID9030)',... 'names' ,name,... 'start time' ,t1,... 'stop time' ,t2);

Out = ao(plist('built-in', 'retrieve_oms_raw_data_by_timespan'),pl);

0 2000 4000 6000 8000 10000−15

−10

−5

0

5

10

15Time origin: 2009−12−02 09:02:48.000

Am

plitu

de [V

]

Time [s]

FAST_LOOP_CTRL_OUTPUT32


Frequency Loop: get relevant segments

Start and end time from tele-commands are crucial to get the signal data

24

0 1000 2000 3000 4000 5000 6000 7000−10

−8

−6

−4

−2

0

2

4

6

8

10Time origin: 2009−11−26 17:47:30.000

[r

ad]

Time [s]

0 1000 2000 3000 4000 5000 6000 7000−10

−8

−6

−4

−2

0

2

4

6

8

10Time origin: 2009−11−26 17:47:30.000

[r

ad]

Time [s]

Set

Set IDL

Original data Only non-red segments are selected


Frequency Loop: Open Loop Gain

25

Compute OLG: full characterisation needs both 128.3 and 128.4 data to be used

10−2

10−1

100

101

10−2

100

102

104

am

plit

ude []

Frequency [Hz]

10−2

10−1

100

101

−200

−100

0

100

200

imag

Frequency [Hz]

OLG

OLG (IDL)

f−1

OLG

OLG (IDL)

OLG =DFT( R)

DFT(")


Frequency Loop: loop delay

26

Delay is obtained as linear fit of IDL data

0 2 4 6 8 10 12 14 16 18 20−2

−1

0

1

2

3

4

5

am

plit

ude []

Frequency [Hz]

unwrap(angle(split(OLG (IDL))))

4.7305*os + −0.29422*xs


Frequency Loop: model

27

Vector fit applied to get pole/zero model

10−2

10−1

100

101

102

10−4

10−2

100

102

104

am

plit

ude []

Frequency [Hz]

10−2

10−1

100

101

102

−200

−100

0

100

200

Phase [deg]

Frequency [Hz]

OLG

OLG (IDL)

model

OLG

OLG (IDL)

model


Towards data analysis integration with ASD

• EM test campaign data analysis performed independently by AEI and ASD groups

• Equivalent results so far: • Quantitative test performed in PSD estimate from AEI and

ASD tools. No difference.• Control loop analysis lead to equal delay and loop

parameters (OLG, CLG, Controller,etc.)• LTPDA handling of the repository and analysis tools

proved to be efficient• OMS EM campaign worked as LTPDA set-up test• Ongoing work for common use of LTPDA during OMS FM

28


LTPDA at UTN

• Single test mass pendulum facility: • AO converter running• noise estimate almost complete• coherent analysis: next

• 4 test mass pendulum facility, testing Flight Model Replica GRS : • AO converter running• other analysis: mostly “variation on the theme”

• Other LTP activities (magnetics):• Complete chain in place

29


Torsion pendulum data analysis: preprocessing

30

AO#retrieve#Transducer#gain#scaling#+#rota4on#

Raw#data# Calibrated#data# Preprocessing#data#

AC#

AO#retrieve#EMS#

Time#range#

Calibra4on#matrix#

Sensor/channel#names#

LockDin#gain#

Angle#

Zero#offset#

Transducer#gain#scaling#

Drop#duplicates#+#

Interpolate#missing#

+#Fix#sampling#freq.#

Sampling#freq.#

Drop#duplicates#+#

Interpolate#missing#

+#Fix#sampling#freq.#

Filtering#+##

downsampling#

Filtering#+##

downsampling#

Sampling#freq.#

Filter#


Torsion pendulum data analysis: Conversion to torque

31

Torque'conversion'

Torque'conversion'

AC'

EMS'

Quality'factor'

Deriva9on'method'

Moment'of'iner9a'

Period'

Torque'conversion'


Calibration

32

Built&a&bilinear&fit&(mul/linear)&method&

Handling&uncertain/es&

φEMSηEMS

#

$%%

&

'((=

∂φ∂Vx1

∂φ∂Vx2

∂η∂Vx1

∂η∂Vx2

#

$

%%%%%

&

'

(((((

Vx1Vx2

#

$%%

&

'((−

φoffηoff

#

$%%

&

'((=

φACηAC

#

$%%

&

'((

Applied&to&EMS&data&Applied&to&AC&

data&

Calibra/on&

AC&

Sensi/vity&

AC&rota/on&

EMS&voltages&

Sensi/vity&Calibra/on&Rota/on&Offset&


Disturbance subtraction

• Known disturbances can be subtracted• Low frequency, linear coupling assumed:

• PSD and CPSD• BH92 window, 50% overlap• De-trending: linear• Binning/averaging• Need to make a “binning” before averaging over windows• Instrument noise: built-in models

33

N t( ) = N t( ) − knxn t( )

n∑


Spectral estimation: example

34


STOC Exercises


STOC Exercises

• Aim to validate mission time-line using all components• Tight integration and training of data analysis team in a

mission-realistic environment• more realistic data• aim to use fixed releases of tools• to train the ability to ‘work-around’ problems

• give structure to • toolbox/algorithm development• we need tool ‘x’ before the next STOC exercise!

• the validation of TNs through MDCs• model development for an ‘unknown’ system

36

STOC Exercise

First-cut analysis

In-depth analysis


Filling the time...

37

STOC Exercise N

Data Preparation •define PORs •run simulations •upload data to repo

In-STOC activity •retrieve data •analyse data •Q: was the run successful?

In-depth investigations •follow-up analysis •quantitative results •report

STOC Exercise N+1

Data Preparation •define PORs •run simulations •upload data to repo

In-STOC activity •retrieve data •analyse data •Q: was the run successful?

STOC Exercise N-1

In-depth investigations •follow-up analysis •quantitative results •report

2-3 days

4-5 weeks


STOC Components

38

Data Generation

Data Ingestion

Data Conversion

Data Storage

Data Retrieval

Data Analysis

Data Import/Export

Documentation

Mission LPF MUST + DT tool

AO Converter (final version)

AO Repository

(final version)

MATLAB LTPDA client LTPDA (V3)

Repository, mysql cluster + dedicated data moving

tools

STOC Wiki

EX 1 LSSdrag files into folder, fake EOP

flag file

AO Converter (0.1 beta version)

AO Repository

V2.1

MATLAB Client CVS

versionLTPDA CVS

version not tested not tested


flag file

AO Converter V1.0

AO Repository

V2.1.1MATLAB

Client V2.1.1LTPDA V2.1.1 not tested STOC Wiki


flag file

AO Converter V1.0

AO Repository

V2.2

MATLAB Client CVS

VersionLTPDA CVS

version not tested STOC Wiki

... ... ... ... ... ... ... ... ...


STOC Ex 3 - highlights

• Aim to ‘simulate’ experiments of UTN-TN-3045• system identification along the x-axis

• Sub-aims:• ‘understand’ the configuration of the STOC simulator• ‘understand’ the noise models

39

1/2/10 6:50 AMLogbook 3 - LPF STOC Wiki

Page 1 of 7http://www.rssd.esa.int/wikiSI/index.php?title=Logbook_3&instance=LISAPATHFINDER_STOC

Logbook 3

From LPF STOC Wiki

Contents1 Introduction2 Noise breakdown3 Investigate injected signals4 Investigate signal-only run5 Make transfer function measurements6 Parameter estimation

IntroductionDue to some problems with the ESA repository (database was too large) downloadingfiles was too slow from the stoc_simulator_exports database. Luckily Michele has alocal copy of all the objects. We created a new database on the STOC server called raw3where we will upload the raw data objects again. The analyses below will then start byreading data from that database. Any results produced will be written to the databasecalled exp3.

Once the data is in raw3 we will begin as if that is end-of-pass.

Noise breakdownA number of runs were made with different noise sources switched off. Here we want tolook at the spectra of all of these compared to the full noise run, and see where eachnoise is contributing.

Noise run 1 (experiment tag: noise_24h_exp_20100111_165805)

First we will download objects from the noise run:

o1 == 174o12 == 175

First we'll take a look at the time-series of these runs


Project time-line

40

Nov 2009 Dec 2010STOC Exercises

MDC3

V2.2V3.0


End

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