OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Overview of WP4: Development of Fuzzy and Computational Intelligence based models for maintenance management

7TH FRAMEWORKPROGRAMME FOR RESEARCHFP7-SST-2012-RTD-1

OPTIRAIL WORKSHOP October 23, 2014

BRUSSELS

OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS

Overview of WP4:Development of Fuzzy and Computational

Intelligence based models for maintenance management


Goal:Develop intelligent models to represent railway infrastructures, maintenance, management and

traffic processes


TASK 4.1: Development of fuzzy models for railway infrastructures and components

Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT), SINTEF (NO).Output: D4.1 Report on fuzzy systems built for railway infrastructure component modeling

Data preprocessingModel selection

Model identificationModel fine tuningModel validation

Components of railway

InfrastructureGeometric

auscultationsMaintenance

data

Fuzzy and CI

Models

WP1 and WP2 Data Mining Techniques


TASK 4.2: Development of fuzzy models for maintenance, management and traffic processes

Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT).Output: D4.2 Report on fuzzy systems built for maintenance processes modeling

Data preprocessingDynamic models

EnsemblesCRISP-DM

Maintenance processes

Maintenance operations

(work orders)Traffic

FRBSSVMANN

WP1 and WP2 Data Mining Techniques


TASK 4.3: Knowledge Extraction from Experts

Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT), SINTEF (NO).Output: D4.3 Report on knowledge extraction from experts and combination with data-driven models

K. RepresentationK. AcquisitionK. Validation

Knowledge Extraction

Knowledge base


TASK 4.4: Cooperation/Fusion of expert knowledge and data-driven models

Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT).Output: D4.4 (Combined with Task 4.5)

Expert Knowledge base

Data-Driven Models

Knowledge aggregation and

fusionCombined

Knowledge base


TASK 4.5: Development of multicriteria decision-making

Partners involved: UGR (ES), CARTIF (ES), OSTFALIA (DE), EVOLEO (PT).Output: D4.4 Report on multi-criteria decision making and multi-objective optimization

Input Data

Criteria

Models and Knowledge

Multiple objective optimization

Mainte-nance

Decisions


Deliverable 4.1: Report on fuzzy systems built for railway infrastructure component modeling


• Use machine learning methods learn a functional

relationship between features and targets

Possible inputs in OPTIRAIL

• Historical geometrical condition data

• Infrastructure data (asset characteristics), such as sleeper type and curvature

• Available work order data

Possible outputs in OPTIRAIL

• Predictions of geometrical condition data (min, max, mean, sd)

• Thresholded predictions prediction of need for interventions

• Prediction of work orders ( D4.2)

Feature1

Feature2

Feature3

... Target Feature1

Target Feature2

Instance1

Instance2

Instance3

...

Input Output

Introduction


Overview of processing steps


Alignment of geometrical inspection data


Methods for data alignment

• Some methods take objects such as bridges and crossings into account

• Our approach is based on the correlation of excerpts/snippets of the curvature of

the measurements

Examples


• The offset is by no means constant, but varies quite significantly due to the way

the position of the train is determined (number of wheel rotations)

• Hence: The offset between the measurements is determined every, say, 1km,

i.e. at discrete points, and linear interpolation is used in between

Some remarks


Dynamic SegmentationThe idea is to use the available asset characteristicsto determine homogeneous track sections.


Predictive modeling of deterioration


Two deterioration models for TQIs can be used:

• Linear deterioration model

• Exponential deterioration model (derived from the observation that the deterioration of track quality is proportional to the current quality)

• is the track quality at time t = 0 (immediately after a work order)

Predictive modeling of deterioration


• In the example of the Swedish Q-value, if no

maintenance is performed, the quality gets worse,

and the Q-value decreases

• Predicting future geometrical inspection data is

„simply“ fitting the (exponential) model

• When work along the track is performed,

the quality increases, with a jump, and the

parameters of the (exponential) model may

change

• The time series is cut into pieces by the work orders.

One such piece is called a deterioration branch


An example from the data

Q-value

sigH = TQI used in OPTIRAIL(maximum of the sd of the long. levelling

of left and right rail)


Machine learning models


Use nonlinear regression methods (FRBS, neural networks, SVR, random forest, etc.) to establish a relationship between asset characteristics and the parameters Q0 and b of the deterioration model for the TQI

Sleeper type

Rail type

Traffic data

Curvature

... Q0 b

Asset1

Asset2

Asset3

...

Input Output


Experiments for Sweden (track 118 of the Iron Ore Line)

Q0 b (exp) #rules/units

ANFIS 1.598 0.000499 104/104

DENFIS 2.229 0.000493 7/8

GFS.MEMETIC 0.594 0.000514 35/35

Random Forest 0.578 0.000349 500/500

SVR 0.628 0.000386 -

MLP 0.661 0.000422 3/3

• Random forest performs well

• Resulting models are not straightforwardly interpretable

• sigH is usually between 0 (perfect) and 3 (maintenance threshold)

• With an error around 0.6, we see that the approach is feasible, but has a high

error, due to uncertainty in the data, data quality, etc.

• Non-constant attributes are used

as input

• One model for Q0, one for b

• RMSE to assess quality of models


Conclusions D4.1

• A methodology for deterioration modelling was developed

• Goes beyond current state of the art

• Data has high amounts of uncertainty models are currently not very accurate

Possible solutions:

• Acquire more data (monthly (geometric) inspections)

• Get more information about the track (ballast type, drainage , subsoil type, etc.)


Deliverable 4.2: Report on fuzzy systems built for maintenance processes modeling


Predict future auscultation results, and predict from auscultations the work to do

• Historical geometrical

auscultation data

• Infrastructure data

predictive model

Future geo-metrical aus-cultation data

Future workorders

predictive model, or existing

(expert knowledge) model

D4.1 D4.2


Two approaches for D4.2

Expert knowledge driven:

• We predict the auscultation, the expert tells us what has to be done.

• Simple version: Use the thresholds from D1.1

This only tells us that something has to be done, but not what

• Probably more expert knowledge needed to distinguish operations

Data driven:

• Use historical work orders to learn the condition in which the asset was directly

before the work order

• Problem: How to include policies, such as thresholds?


Expert knowledge driven modeling of work orders:

• Idea: After having predicted an auscultation, apply the process currently

implemented by the railway administrator to get work orders from the

auscultations

• Detailed expert knowledge is needed for this approach (see also D4.3)

• We collected information from Spain (and some information from Sweden). E.g.:

Parameters Maintenance action

Longitudinal level Automatic Tamping

Alignment Automatic Tamping

Cant Automatic Tamping/ Ballast renewal

Gauge Fastening renewal


Data driven modeling of work orders:

Longitudinal left D1

… Alignment D1

… Twist9m

Work to do

Asset1 Nothing

Asset2 Tamping

Asset3 Sleeper replacement

... …

Input

Output


Data driven modeling of work orders:

Problems:

• Is the information in the data?

• We need historical work orders and geometrical

data that fit together, also, we need sufficient amounts of work orders for every

type of work to be done. Example: With 5 historic sleeper renewals difficult to

build a model for this type of operation


Results Sweden:

• Good results especially for Random Forest:

• 100% recall (this means that all tamping work orders are correctly classified)

• 91% precision (which means that when the classifier determines that a tamping should be

performed, this is correct in 91% of the cases)

Error Recall Precision

Random Forest 0.59% 100.00% 91.18%

SVM 10.30% 99.56% 37.25%

FRBCS.W 6.09% 0.44% 100.00%

GFS.GCCL 6.12% 0.00% N/A


Modeling of the effect of a maintenance operation,Lifecycle modelling


We need a TQI which adequately assesses deterioration behavior through time, not at a single

time point. With an exponential deterioration model, we have:

At time t=0:

So, Q‘ as the product of Q0 and b can be used as a quality measure (the tangent to the TQI)

We use the following formula to model the effect of a tamping operation (i, i+1):

I.e., a tamping operation will worsen the quality by a constant c.


• We consider the following asset:

• We lower the nominal track speed to 90km/h. The model of D4.1 predicts values of 1.467

and 0.0001485 for Q0 and b, respectively

• We set the threshold of triggering a work order to 3

Lifecycle modelling:

Radius Class

Speed Det. Branch Num.

Sleeper Age

Rail Age

1 110 km/h 0 18 years 18 years 1.731 0.0002113


Lifecycle modelling:

Black curve: lifecycle model with

110km/h nominal track speed,

remaining life: 33.48 years

Red curve: lifecycle model with

90km/h nominal track speed,

remaining total life: 44.23 years


Conclusions D4.2:

• A methodology for modelling maintenance decisions from current auscultations was

developed

• This can be done data-driven and/or with expert knowledge

• We showed that the data-driven approach works well for the case of Sweden and tamping,

and a random forest can classify tamping vs. no-tamping reliably

• We investigated the effect of tamping on deterioration behaviour, and applied/developed a

model, using a constant change in Q0‘=Q0*b for exponential deterioration.

• We also did some modelling of traffic data from Norway, and from work orders of Spain (not

shown in this presentation)


Deliverable 4.3: Report on knowledge extraction process from experts and

combination with data-driven fuzzy models


A questionnaire was developed

Aims:

• Gather expert knowledge regarding infrastructure deterioration and maintenance

operations

• Which variables/asset characteristics have an influence on infrastructure

deterioration? How big is this influence?

• How does the geometrical measurement determine a maintenance decision?

Which other data is necessary for a maintenance decision?


Results:

• We collected 26 answers:

• Problems: The answers differ considerably between the countries, and for all countries

except Spain, not enough answers are available to make a within-country analysis

• The only distinction that is done is Spain vs the rest (we didn‘t take into account that this

may underrepresent the answer from ADIF)

Country Number of answers

Spain (SP) 17 (VIAS 16, ADIF 1)

Poland (PO) 3

Norway (NO) 2

England (EN) 1

Germany (GE) 2

Austria (AU) 1


Results (summarized):

• quality and condition of the ballast, the track load and the type of traffic, i.e.

freight traffic and passenger traffic strongly influence track deterioration

• subsoil and drainage is also considered important for track deterioration, but in

many cases, information along the whole track is not available

• deterioration of the track in curves is generally higher than compared to straight

tracks

• The answer for the shortest tamping interval still considered feasible varies from

20 days to 12 months



• Decision for performing tamping is based on auscultation data

• Experts think that there exists an optimal interval between consecutive tamping

operations in the sense that the useful track life is largest.

• Rail substitution can be based on the geometrical inspection data only in a limited

way. Instead, other factors such as the rail condition (obtained from visual

inspection) should be taken into account.



• There is no consensus whether or not rail grinding should be based on the geometrical

inspection data. Additionally, visual and ultrasonic inspections should be considered.

• The replacement of sleepers can partially be based on the geometrical inspection data,

especially on the gauge. Moreover, also the condition of the sleepers and of the fastenings,

which can be determined by visual inspections, as well as the age of the components,

should be taken into account.

• Finally, for ballast cleaning, there is no consensus among the experts which geometrical

variables (besides the longitudinal level of the left and right rail) should be considered for

ballast cleaning. Instead, information on the condition of the ballast and its contamination

(determined by visual inspections) should be considered.


Deliverable 4.4: Report on multi-criteria decision making and multi-objective optimization



Main objectives

• track maintenance cost

• the availability/capacity

• the safety/quality of the track

Other important factors

• Constraint or objective?

• Planning horizon (3,5,30 years)

• Granularity of planning in time (daily, monthly, trimestral)

• Granularity of planning in space (track sections, track length)


Objective: Minimize maintenance cost

• Maintenance operation cost (MOC) in framework from Lulea:

i sums over the track sections and j over the time intervals in the planning horizon. The variable r is a discount rate.

: material cost for the maintenance operation in €/km

: average time to perform the maintenance operation (MO) on the ith track section in hours/kilometre

: total length of maintenance section in kilometres

: average labour cost in €/hour

: equipment cost for the maintenance operation in €/hour

: cumulative load / time (in MGT or years)

: interval for the maintenance operation of the ith track section in MGT (or years)


Objective: Minimize maintenance cost

• The costs can be described with parameters that can later be easily changed by

each railway operator.

• Example costs used by Lulea:

• One tamping machine is available for the considered track (otherwise external

factors have to be taken into account)

• Constraints regarding time of day: Maintenance window of approx. 5h, which

translates into maximal tamping distance per day

• Constraints regarding time of year

Constraints regarding maintenance costs


Objective: Availability and capacity

The objective has the following aspects:

• capacity of the track

• punctuality of trains

• penalties if capacity and punctuality goals are not met

Capacity loss is the sum of delays and non-availability of the track due to maintenance.

Train delays occur due to parts of the track that cannot be used with their nominal track speed

as their quality is not sufficient.

Changes in speed should be minimal (as they waste resources and cause more maintenance

necessity)

Capacity may often be a constraint and not an objective


Objective: Safety and quality of the track

• maximize safety and ride comfort

• minimize costs caused by damage to trains and track components due to bad

track condition

• minimize penalties that may result from these

• Cost is proportional to how much measured values lie above the thresholds

Both safety and ride comfort are difficult to measure

Safety is not optimized but guaranteed, so this is modeled as a constraint and not

an objective


• Cost (tamping and renewal cost) is defined by:

• Cost is subject to minimization:


Calculation of train delays:

• Maximal admissible speed can be obtained from (predicted) track quality using,

e.g., EN-13848-5:

• Delay is to be minimized:


In this approach, the constraints handle important parts:

• Ensuring that the speed the trains go with is admissible given the tamping and

renewal actions:

• Machine limits (of overall tamping and renewal that can be performed):


• Degradation model from D4.1:

• Tamping effect model from D4.2: Derivative of sigH is constant over tamping

operations:


• Renewal effect model: Both and are set to values that can be

considered “as good as new“

• Renewal is always programmed in a whole section k

• Tamping and renewal are exclusive. Renewal has higher priority


Initialization of the solutions

• Problem has very high dimensionality and complexity

• An intelligent initialization can be performed and is necessary

Program operations in first trimester where necessary

Calculate the remaining tamping capability up to the established threshold. Generate a random

number r between 0 and this remainder.

Sort the track segments that have no tamping scheduled according to decreasing quality. Include

tamping along this list up to the computed limit.

From this list, choose for each segment randomly if tamping will be performed or not.

Do the same for renewal

Apply deterioration and effect models, and begin from start iteratively for all trimesters


Parameters for the Sweden case study:

• Sections ij are statically segmented, 5km each

• Segments k are dynamic segmentation from D4.1

•

• Values for “as good as new“ (see also D4.2):


Results

• 500,000 evaluations of the fitness function, 104 solutions in the initial population


Results (2)

• AMOSA yields better results than NSGA2: the problem is complex and high dimensional, and

the initialization already uses a lot of problem-specific knowledge. AMOSA favors local

search instead of exploration

• Best solutions with AMOSA:Cost (€) Delay

(hours)Tampings Renewals

5689494 137.98 665 8

5690557 126.63 667 8

6286985 126.63 623 9

6289973 126.14 625 9

6293171 119.84 630 9

6293924 118.34 630 9

… … … …


Conclusions D4.4:

• Possibilities for optimization have been analysed in depth, and a framework was developed

• We implemented a state-of-the-art model: Multi-objective optimization minimizing cost and

train delays, using the degradation model from D4.1 and the tamping effect model from

D4.2

• Complexity and high dimensionality is a big problem

• Intelligent initialization helps to cope with this problem

• However, with current resources, simplifications have to be made (regarding granularity of

planning in time and space)

• Scheduling is another problem not touched here. However, this is necessary to define

realistic benefits regarding transport and fixed costs


More conclusions:

--> Track is so bad that tamping doesn't lift it anymore above threshold

--> Renewal has to be programmed

--> Important how cost of tamping and renewal relate to each other. Currently, 20 tampings

cost the same as one renewal

--> With a planning horizon of 3 years, the situation that a renewal saves cost will not occur

--> With longer horizons, complexity even bigger, and deterioration model unreliable


Conclusions


• We have developed methodologies for modeling of infrastructure and

maintenance operations data, and we have shown how a predictive maintenance

plan could be generated

• We have shown how data and expert knowledge can be used to achieve the

OPTIRAIL goals, i.e., predict maintenance operations.

• We have adapted and implemented two multiple-objective algorithms for

maintenance decision making

Documents

OPTIRAIL WORKSHOP · OCTOBER 23, 2014 · BRUSSELS Overview of WP4: Development of Fuzzy and Computational Intelligence based models for maintenance management