Supporting the Selection of Prognostic-based Decision Support

Methods in Manufacturing

Alexandros Bousdekis

, Babis Magoutas

, Dimitris Apostolou

and Gregoris Mentzas

School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

Department of Informatics, University of Piraeus, Piraeus, Greece

Keywords: Condition based Maintenance, Decision Tree Learning, Method Filtering, Decision Support.

Abstract: In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition

Based Maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions

based on the current and predicted health state of equipment. Although decision support for CBM is not an

extensively explored area, there exist methods which have been developed in order to deal with specific

challenges such as the need to cope with real-time information, to prognose the health state of equipment

and to continually update decision recommendations. We propose an approach for supporting analysts

selecting the most suitable combination(s) of methods for prognostic-based maintenance decision support

according to the requirements of a given maintenance application. Our approach is based on the ID3

decision tree learning algorithm and is applied in a maintenance scenario in the oil and gas industry.

1 INTRODUCTION

In manufacturing enterprises, high reliability, low

environmental impact and safety of operations are

important issues for every industry (Peng, Dong, and

Zuo, 2010). Maintenance is a significant contributor

to the total company’s cost, so optimal maintenance

policy in terms of cost, equipment downtime and

quality should be identified (Garg, and Deshmukh,

2006). Condition Based Maintenance (CBM) is a

type of maintenance strategy, which relies on

diagnostic and prognostic models and uses them to

support decisions about the appropriate maintenance

actions based on the current health state of a system

through condition monitoring (Jardine, Lin, and

Banjevic, 2006). Condition monitoring in

manufacturing enterprises is increasingly realised

with equipment-installed sensors, which have the

capability of measuring with high frequencies a

multitude of parameters. This capability leads to

storage of a huge amount of data. Generating and

storing Big Data has become possible due to recent

developments in both hardware and data

management software (Zikopoulos, and Eaton,

2011).

Big Data-driven CBM poses challenges to

Decision Support Systems. These challenges are not

easily addressed within the complex manufacturing

environment, especially when dealing with

maintenance where several factors should be

considered simultaneously such as costs of

maintenance actions as a function of time, safety

issues and equipment degradation.

Although decision support for CBM is not an

extensively explored area, there exist several works

focusing on combinations of methods that can be

utilised for CBM decision support. Such methods

deal with real-time data which are gathered in high

frequency, develop prognostic models for the

estimation of Remaining Useful Life (RUL) or

Remaining Life Distribution (RLD) and provide

recommendations for maintenance. In the current

paper, we propose a practical approach for

supporting analysts to select the most suitable

combination(s) of methods for prognostic-based

maintenance on the basis of Big Data according to

the requirements of the application which they are

involved with.

Aiming to support prognostic-based maintenance

in various application domains and for a wide range

of functional and non-functional application

requirements, we follow a practical multistage

decision making approach. The basic idea of our

hierarchical approach is to break up the problem of

selecting the most suitable combination(s) of

487

Bousdekis A., Magoutas B., Apostolou D. and Mentzas G..

Supporting the Selection of Prognostic-based Decision Support Methods in Manufacturing.

DOI: 10.5220/0005372104870494

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 487-494

ISBN: 978-989-758-096-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

methods for prognosis and prognostic-based

maintenance, into a union of several simpler

decisions about the suitability of the method

combination depending on the functional and non-

functional application requirements by developing a

Decision Tree (DT).

To formulate the information found in the papers

examined in our literature review in the form and

structure needed for feeding the DT learning

algorithm and building the DT, the following steps

were performed. First, generic categories of methods

for prognosis and prognostic-based maintenance

were defined in our effort to avoid specific method

extensions or variations used in the various papers

and keep the resulting DT as generic as possible.

Second, unique combinations of the previously

defined generic methods, which are actually the leaf

nodes of the DT, were identified in the papers

reviewed. Third, rules for classifying the method

combinations using criteria that depend on

functional and non-functional application

requirements were defined; such rules and criteria

are used in the decision nodes of the DT. Fourth, for

all method combinations identified, the fulfilment of

the criteria used in the DT’s decision nodes was

assessed. Finally, the appropriate DT learning

algorithm was selected and fed with the training

dataset.

The rest of the paper is organized as follows:

Section 2 presents the literature review; Section 3

outlines the method filtering approach, while Section

4 illustrates its application in a maintenance scenario

in the oil and gas industry. Section 6 discusses the

method filtering approach and the results and

concludes the paper.

2 LITERATURE REVIEW

2.1 Literature Search and Pre-filtering

of Results

Several research works have examined and

developed maintenance decision support methods,

based on historical and real-time data as well as

expert knowledge, in order to address different

maintenance challenges. Most of the existing

research works address maintenance issues for

components subjected to condition monitoring in the

context of Condition Based Maintenance (CBM).

Maintenance decision support is related to

reliability, safety and environmental issues as well

as associated with equipment downtime costs in

cases of breakdowns or malfunctions of machines

(Peng, Dong, and Zuo, 2010). First, prognostic

methods are applied and then decision methods are

developed in order to provide prognostic-based

recommendations. Table 1 summarises the

prognostic-based decision support methods

reviewed, as well as their inputs and outputs. The

methods have been separated in two groups: one

group supports the Prognostic (P) and the second

one the Decision (D) step.

The papers examined were identified by

searching Google scholar with the keywords ‘CBM,

‘recommendations’, ‘decision support’, ‘decision

making’,’ manufacturing’, ‘maintenance’ in various

combinations among them. We focused on papers

dealing with the decision step of CBM. However,

we realized that most of them proposed a

combination of methods so that they develop a

prognostic model based on real-time data and then,

based on this, they provide recommendations for

maintenance. The focus was on most recent papers,

after 2008, with exceptions in cases where an older

paper satisfied the keywords and proposed a novel

and useful method which has not been extended

until now.

2.2 Categorising Methods

The methods found in the literature for prognostic-

based decision support can be divided into the

following generic categories:

 Bayesian Network (BN), which also

include Dynamic Bayesian Network

 Neural Network (NN)

 Statistical Analysis (SA), which include

statistical techniques such as Statistical

Quality Control (SQC), Support Vector

Machine (SVM) and moving average.

 Degradation Modelling (DM), which

includes all the mathematical techniques

dealing with representing the degradation

process.

 Reinforcement Learning (RL), such as

State-Action-Reward-State-Action

(SARSA) algorithm.

 Markov Chain (MC)

 Mathematical Programming (MP)-

Optimisation, which includes operational

research methods such as linear, non-linear

and stochastic dynamic programming

 Markov Decision Process (MDP), which

also includes Semi-Markov Decision

Process (SMDP) and Partially Observable

Markov Decision Process (POMDP).

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 Rules (R), which include rule-based

systems such as IF-THEN rules and Event-

Condition-Action (ECA) rules.

2.3 Identifying Combinations of

Methods

As shown in Τable 2, there exist in the reviewed

literature ten unique combinations of methods used

for providing prognostic-based recommendations.

3 METHOD FILTERING

APPROACH

3.1 Identifying Method Filtering

Criteria

Following our analysis of existing methods and

combinations of methods, we propose criteria for

selecting the appropriate ones based on the

functional and/or non-functional requirements of

specific applications. Selection should be based on

desired output that the business analyst expects to

get after the implementation of the method

combination. Depending on the available input,

different combinations of methods can be applicable.

Another criterion is whether Domain Knowledge

(DK) can be expressed in terms of utility functions.

Finally, the existence of degradation knowledge

affects the selection of the appropriate prognostic

methods and thus the selection of the suitable

combination of methods.

3.2 Evaluating Methods

Based on the information given in Table 1, the

identified combinations of methods are evaluated

according to the four specified criteria. Evaluation of

method combinations on the criteria of available

input and desired output was done based on the

information summarized in Table 1. For evaluating

method combinations on the other two criteria, we

examined in more detail the information provided in

the respective papers. Desired output can be either

the optimal time of applying a predefined action

(e.g., optimal time of replacement of some part of

equipment) or the optimal action and the optimal

time of applying it (e.g., lubrication of metal parts

accompanied with the optimal time). Available input

can include historical data about cause (e.g.,

vibration, temperature, etc.) and effect (e.g., failure,

malfunction, etc.) or prognostic information.

Knowledge of the degradation process is a

prerequisite for some prognostic methods, while this

is not the case for others. Table 3 shows the

evaluation of the methods’ combinations using the

four specified criteria.

3.3 Decision Tree Learning

The data presented in Table 3, were fed into a DT

learning algorithm, i.e. a DT classifier, in order to

produce the DT for supporting analysts perform

prognostic-based maintenance in various application

domains and for a wide range of functional and non-

functional application requirements. DT classifiers

have the ability to handle data which are measured

in different scales, they do not require any

assumptions about the frequency of data in each

class, while they are able to handle non-linear

relationships between features and classes.

Furthermore, the analyst can comprehend and

interpret a decision tree as it is not a ‘black box’

(Pal, M., and Mather, 2003). The ID3 (Iterative

Dichotomiser 3) algorithm, that was used in our

case, classifies all training data provided that there

are enough attributes to do so.

There are several extensions of the ID3

algorithm, such as J48, C4.5 and C5.0, which,

among others, are able to handle continuous

attributes, training data with missing attribute values

and attributes with differing costs. However, these

capabilities are not useful in our case because there

are not any related issues to address. Moreover,

these additional capabilities provide improvements

in terms of speed and memory usage, which are

nevertheless not needed in our case because it

consists of a small number of combinations of

methods and the DT learning is done once. Finally,

the aforementioned extensions of ID3 create smaller

DTs because the probability of over-classifying the

data is much smaller compared to ID3. However, in

our case, we want to separate our method dataset as

much as possible. The four criteria used for the

separation of the combinations of methods and their

alternative values were defined in an abstract level.

This means that if, for example, two combinations of

methods are classified in the same class, they are not

necessarily the same and cannot be used

interchangeably because e.g., their input may require

additional, more specific data or knowledge than

those specified in Table 3.

The ID3 decision trees algorithm is based on

information theory and tries to minimize the number

of comparisons among the data of the training set.

The core idea behind the algorithm Is asking

SupportingtheSelectionofPrognostic-basedDecisionSupportMethodsinManufacturing

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Table 1: Reviewed Research Works on Prognostic-based Decision Support.

Reference

Methods Input Output

(Kaiser, and

Gebraeel,

2009)

P BN; DM Real-time and historical data;

Threshold; Replacements

Estimation of RLD

D R RLD; Costs of maintenance; Process

knowledge

Compute/update maintenance

schedule

(Besnard,

and

Bertling,

2010)

P DM; MC (Continuous

time);

Degradation condition monitoring;

Degradation process

RUL; Failure rate

D MC (Continuous time);

MP; R

RUL; Failure rate; Maintenance and

production knowledge and costs

Optimal maintenance strategy

(Besnard, et

al., 2011)

P - - -

D MP (Stochastic); R Wind forecasting ; Failure rate; List of

actions; Production and maintenance

knowledge and costs

Minimised cost of production

losses and transportation

(Castro, et

al., 2012)

P DM Real-time and historical data; Threshold Mean Residual Life; Times of

replacement

D MP (cost minimisation) Mean Residual Life; Times and costs of

maintenance

Minimised maintenance cost;

Optimum policy

(Wu, et al.,

2007)

P NN; MP (Non-linear

programming); SA

(Moving average)

Real-time and historical data; Threshold Residual Life Percentile

Prediction

D MP (Non-linear

programming)

Predicted Residual Life Percentile;

Times of operation; Costs related to

maintenance

Minimised cost; Optimal

replacement time

(Ivy, and

Nembhard,

2005)

P SA (SQC) Real-time and historical data; States;

Threshold

Transition Matrix; Estimation

of observation distribution

parameters

D MDP (POMDP) Transition Matrix; Estimation of the

observation distribution parameters;

Maintenance costs

Minimised expected cost ;

Optimal maintenance and

monitoring actions

(Aissani,

Beldjilali,

and

Trentesaux,

2009)

P RL (SARSA algorithm) Real-time and historical data;

Degradation and maintenance

knowledge

Solution of SARSA

algorithm; Probabilities of

events

D MDP Solution of SARSA algorithm;

Probabilities of events

Predictive and corrective

maintenance tasks

(Elwany,

and

Gebraeel,

2008)

P DM Real-time and historical data; Failure

threshold

RLD

D MP (replacement

model)

RLD; Costs of maintenance; Lead times

of spare parts

Optimal replacement and

inventory ordering times

(Bouvard,

et al., 2011)

P DM; SA Real-time monitoring; Degradation

process; Threshold

Failure probability; Estimated

degradation path; Time-to-

failure

D MP (maintenance

optimization)

Failure probability; Time-to-failure;

Maintenance costs

Optimal maintenance cost and

planning

(Huynh,

Barros, and,

Berenguer,

2012)

P DM Condition monitoring; Degradation

process

Reliability; Probability

density function

DMP (dynamic

replacement model); R

Reliability; Probability density function;

Cost function

Replacement Time

Estimation; Optimised cost

(Muller,

Suhner, and

Iung, 2007)

P BN (DBN) Real-time and historical data; Process Degradation process;

Prognosis

D MC (Discrete time); R Degradation process; Prognosis; List of

actions; Costs

Optimal maintenance policy

(Engel,

Etzion, and

Feldman,

2012)

P BN Real-time monitoring; Historical data of

transitions

Probability distribution of an

event; Time-to-failure

DMDP

Probability distribution of an event;

Time-to-failure; States; Actions; Cost

function

Optimal action; Optimal time

of action

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Table 2: Methods and Techniques for Decision Making.

Reference BN NN SA DM MC RL MP MDP R

(Kaiser, and Gebraeel, 2009) v v v

(Besnard, and Bertling, 2010) v v v v

(Besnard, et al., 2011) v v

(Castro, et al., 2012) v v

(Wu, et al., 2007) v v v

(Ivy, and Nembhard, 2005) v v

(Aissani, Beldjilali, and Trentesaux, 2009) v v

(Elwany, and Gebraeel, 2008) v v

(Bouvard, et al., 2011) v v

(Huynh, Barros, and, Berenguer, 2012) v v v

(Muller, Suhner, and Iung, 2007) v v v

(Engel, Etzion, and Feldman, 2012) v v

Table 3. Methods’ combinations evaluation.

Combinations

of methods

Desired Output

Available input

DK expressed in

utility function

Knowledge of the

degradation process

Time of

action

Action

and time

Historical

data

Prognosis Yes No Yes No

NN-SA-MP v v v v

BN-DM-R v v v v

BN-MC-R v v v v

RL-MDP v v v v

DM-MC-MP-R v v v v

SA-MDP v v v v

DM-MP-R v v v v

DM-MP v v v v

MP-R v v v v

BN-MDP v v v v





questions the answers of which provide the most

information. The splitting criteria are prioritized

according to the information gain; splitting criteria

with more information gain are used first. The

decision tree is constructed by employing a top-

down, greedy search through the given sets to test

each attribute at every tree node. Information is

measured by the entropy which represents the

amount of uncertainty of a data set D (Chen, Zhang,

and Tong, 2014). Based on the entropy, the

information gain can be measured. Information gain

is the difference in entropy from before to after the

data set D is split on an attribute A or equally, how

much uncertainty in the data set was reduced after

splitting it on an attribute A (Gaddam, Phoha, and

Balagani, 2007).

The DT was built by feeding the combinations of

methods identified in the literature as training data to

the decision tree learning algorithm and shows the

sequence of steps needed to be followed by an

analyst in order to decide which combination(s) of

methods are the most appropriate ones for a specific

problem. The pseudo-code of the application of the

ID3 algorithm for the classification of the

combinations of methods according to the four

criteria is shown below (adapted from (Jin, De-lin,

and Fen-xiang 2009)):

ID3 (Set of combinations of methods D,

Set of criteria-attributes S, Criteria-

Attributes_values V)

Return Decision Tree.

Begin

Load set of combinations of methods D

first, create decision tree root node

'rootNode', add learning set D into

root node as its subset.

For rootNode, we compute

H(rootNode.subset) first

If H(rootNode.subset)==0, then

rootNode.subset consists of

records all with the same

value for the categorical

criterion-attribute,

return a leaf node with

decision criterion-

SupportingtheSelectionofPrognostic-basedDecisionSupportMethodsinManufacturing

491

attribute: criterion-

attribute value;

If H(rootNode.subset)!=0, then

compute IG for each

criterion-attribute left

(have not been used in

splitting), find attribute S

with Maximum(IG(D, S)).

Create child nodes of this

rootNode and add to rootNode

in the decision tree.

For each child of the

rootNode,

apply ID3(D, S, V)

recursively until reach

node that has H=0 or

reach leaf node.

End ID3

Our problem was formulated in the ID3 notation

using the RapidMiner machine learning software,

while the DT obtained after running ID3 is shown in

Figure 1.

4 APPROACH ILLUSTRATION

IN A MAINTENANCE

SCENARIO

In this section, we outline how our method filtering

approach can help in selecting the most appropriate

method combination for supporting decision making

in a maintenance scenario in the oil and gas industry.

In the scenario under consideration, sensors collect

data with a very high frequency, and these data

accompanied with historical data and domain

knowledge are used for detecting the current health

state of the equipment examined, estimating RUL

and calculating the probability distribution of an

undesired event, e.g., breakdown of the gearbox of

an oil drilling company’s equipment. Historical data

show the patterns of the monitored parameters,

which are used as indicators of degradation till

failure. Domain knowledge can include a list of

maintenance actions, failure threshold as well as

utility functions considering criteria such as cost,

time and safety. Then, the optimal action and the

optimal time of applying it are recommended.

The DT flow in the aforementioned scenario is

shown in Figure 2. First, as far as the available input

is concerned, in the aforementioned scenario there

are historical data about causes (sensed parameter)

and effects (failure) but not prognostic information

(e.g. RUL, probability distributions of the

occurrence of failure, etc.), while data are

continuously updated with the ones coming from

sensors.

The prognostic information is not known in

advance, but it will derive from the processing and

analysis of data by using the appropriate method.

The output of this method will feed into another

method for providing recommendations. Then, the

desired output is the optimal action and the optimal

time for this action, because the objective is to

identify the best maintenance action out of a list of

actions as well as the best time to implement it in

Figure 1: Method filtering process for choosing the appropriate combination of methods.

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Figure 2: Method filtering process in a maintenance scenario.

terms of cost.

DK can be expressed in utility functions as there

is extensive industrial experience on the domain

which can be expressed in a systematic way. Issues

about cost function, safety, current maintenance

policy, etc. can be embedded to a utility function

which can be used in optimization techniques in

order to provide reliable recommendations. Finally,

there is knowledge of the degradation process.

Hence, there are four options: BN-MC-R, DM-MC-

MP-R, SA-MDP and BN-MDP.

For example, for the BN-MDP combination, BN

are used for data-driven estimation of probability

distribution of an undesired event (e.g. gearbox

breakdown) and MDP for generating

recommendations about optimal maintenance

actions and optimal time for applying these actions.

Moreover, this particular combination can

effectively support decision-making in

manufacturing enterprise and especially for CBM

(Engel, Etzion, and Feldman, 2012). This case

shows that MDP and MC are the most suitable

methods for extracting this output provided that

some domain knowledge exists and probability

distribution of an undesired event has been extracted

from machine learning or statistical methods.

5 CONCLUSIONS

CBM relies on prognostic models and uses them to

support decisions about the appropriate maintenance

actions based on the current health state of a system

through condition monitoring (e.g. using sensors).

To do this, combinations of both machine learning

and decision methods are required in a way that they

are able to handle real-time data and provide

recommendations for maintenance decisions based

on predictions about future health state of the

equipment.

We examined literature that deals with methods

supporting decision making in the context of CBM.

The method filtering approach that we propose

supports the business analyst to select the most

appropriate combination(s) of methods based on the

requirements of the specific maintenance scenario.

Our method may recommend more than one

alternative method or method combinations, which

are applicable in specific maintenance scenarios and

under specific conditions. In such cases however,

methods or combinations of methods that are

classified in the same class are not the same and,

while our filtering method cannot discern between

them, a human expert should be able to do so by

taking into account additional data or knowledge in

order to select the most appropriate. Although we

identified several combinations of methods that are

used for prognostic-based maintenance

recommendations, most are not able to adequately

support proactive decision making by

recommending the optimal action and the optimal

time of applying it based on predictions.

Furthermore, there are limitations regarding the

SupportingtheSelectionofPrognostic-basedDecisionSupportMethodsinManufacturing

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continuous improvement of the recommendations

using these combinations of methods.

Our future research will focus on the

examination and incorporation of additional machine

learning and decision methods specifically targeting

proactive decision support. Moreover, we will

extend our method filtering approach with a

feedback loop, which will support collection of data

about the effectiveness of the recommended

decisions and will utilize the collected data as a basis

for improving the recommendation generation

process. Finally, we will test and evaluate our

approach in real maintenance scenarios in the oil &

gas and automotive industries.

ACKNOWLEDGEMENTS

This work is partly funded by the European

Commission project FP7 STREP ProaSense “The

Proactive Sensing Enterprise” (612329).

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