On the future of Chinese cement industry

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Decision Support Systems 38 (2005) 539–555

Selection of diagnostic techniques and instrumentation in a

predictive maintenance program. A case study

M.C. Carnero*

University of Castilla-La Mancha, Technical School of Industrial Engineering, Avda. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain

Received 7 October 2002; received in revised form 22 September 2003; accepted 22 September 2003

Available online 4 November 2003

Abstract

Predictive maintenance programs (PMPs) can provide significant advantages in relation to quality, safety, availability and

cost reduction in industrial plants. Nevertheless, during implementation, different decision making processes are involved, such

as the selection of the most suitable diagnostic techniques. A wrong decision can lead to the failure of the setting up of the

predictive maintenance program and its elimination, with the consequent economic losses, as the setting up of these programs is

a strategic decision. In this article, a model is proposed that carries out the decision making in relation to the selection of the

diagnostic techniques and instrumentation in the predictive maintenance programs. The model uses a combination of tools

belonging to operational research such as: analytic hierarchy process (AHP) and factor analysis (FA). The model has been tested

in screw compressors when lubricant and vibration analyses are integrated.

D 2003 Elsevier B.V. All rights reserved.

Keywords: Predictive maintenance; Decision making; Analytic hierarchy process; Factor analysis

1. Introduction els. This article aims to contribute towards resolving

The continuous production process requires a high

degree of availability and the elimination of unex-

pected breakdown that could cause a prolonged stop-

page in production [9]. Predictive maintenance can

contribute to improving plant availability, safety,

quality, reduction of maintenance costs, etc. This

has led to an increase in the number of predictive

maintenance programs (PMPs) applied, but, during

the setting up of a PMP, there is a number of decisions

involved that lack decision support systems or mod-

0167-9236/$ - see front matter D 2003 Elsevier B.V. All rights reserved.

doi:10.1016/j.dss.2003.09.003

* Tel.: +34-926-295-300; fax: +34-926-295-361.

E-mail address: [email protected] (M.C. Carnero).

this problem.

Although there is a limited number of decision

support systems related to predictive maintenance, the

following models should be taken into consideration.

In Ref. [15], a proportional-hazards model with Wei-

bull baseline hazard function and time-dependent

stochastic covariates representing monitored condi-

tions is suggested and a software is developed to

assist engineers to optimize decisions. In Ref. [30],

Markov models are described for establishing opti-

mum inspection intervals for phased deterioration of

monitored complex components in a system with

severe down time costs. In Ref. [16], statistical

analysis of vibration data is undertaken using a

software package to establish the key vibration signals

M.C. Carnero / Decision Support Systems 38 (2005) 539–555540

that are necessary for risk estimation. Ref. [19]

presents a real-time neural network-based condition

monitoring system for rotating mechanical equipment.

In Ref. [29], condition predictors of significant items

of the system are monitoring taking into account the

availability and cost-effectiveness of the monitoring

techniques.

In this article, a model is presented for the

selection of diagnostic techniques and instrumenta-

tion in a predictive maintenance program. To con-

struct the model, factor analysis and analytic

hierarchy process are combined. The model is applied

to screw compressors which are monitored by means

of PMPs based on lubricant and vibration analyses

and when the aforementioned techniques are applied

simultaneously.

The layout of the paper is as follows. Section 2 is

an introduction to predictive maintenance techniques,

lubricant and vibration analyses and the integration of

both techniques are presented. Section 3 describes the

characteristics of the mathematical tools used in the

construction of the decision support model proposed:

factor analysis and analytic hierarchy process. Section

4 presents the model for the selection of diagnostic

techniques and instrumentation in predictive mainte-

nance. Section 5 describes the application of the

model to a screw compressor. Section 6 presents the

results obtained from applying the model to a PMP

integrating lubricant and vibration analyses. Section 7

presents the conclusions.

2. Predictive maintenance techniques: lubricant

and vibration analyses

Predictive maintenance is a maintenance policy in

which selected physical parameters associated with an

operating machine are sensed, measured and recorded

intermittently or continuously for the purpose of

reducing, analyzing, comparing and displaying the

data and information so obtained for support decisions

related to the operation and maintenance of the

machine [5]. There are numerous predictive techni-

ques, as can be checked in Ref. [12]: lubricant

analysis, vibration analysis, thermography, penetrat-

ing liquids, radiography, ultrasound, control of corro-

sion, etc.; each technique is applied to a type of

specific industrial equipment.

The advantages of the introduction of predictive

maintenance programs (PMPs) are:

� Exclusive control of the machines that show the

beginning of a malfunction.� An increase in the availability of the industrial

plants [40].� The capacity to carry out quality checks of

both internal and subcontracted maintenance

interventions.� An increase in the security of the factory [9].� It facilitates certification and ensures the verifica-

tion of the requisites of the standard ISO 9000.� Provides the best programming of maintenance

actions.� Enables the effective programming of supplies and

staff.� Production quality is optimized by operating

machinery without interruption due to failures [21].� Support in the design phase of equipment,

particularly by means of the application of modal

analysis [3].� Reduction of direct maintenance costs by checking

only the equipment that is developing a fault [38].� By keeping to delivery dates, and by satisfying the

customers’ demand for quality, the image of the

company is improved.� Costs are brought down in relation to spare parts

and labour [18].� By maintaining the industrial equipment opera-

tional whilst applying the predictive tools, the

measuring process does not directly affect the

availability of the equipment.� Decrease in the costs related to insurance policies

as security within the factory increases.� Historical information on each piece of equipment

is completed, which helps to determine reliability

parameters and to optimize maintenance planning

[2]. This information on the machines and equip-

ment is available to the management for decision

making.� Reduction of energy consumption.

Industrial plants generally possess PMPs based on

vibration analysis [4], whereas medium-sized compa-

nies are starting to incorporate them in their Mainte-

nance Departments. Their suitability for application to

rotary and reciprocating machines [36], which can be

M.C. Carnero / Decision Support Systems 38 (2005) 539–555 541

considered to be the most widely used in general, as

well as their high capacity of diagnosis, make them

the most versatile predictive technique. In order to

carry out the setting up of a PMP based on vibration

analysis, it is vital to understand their technical

peculiarities, regarding instrumentation, procedures

and uses that make the production of diagnoses

possible. The success or failure of the setting up

process will depend on the program planner’s knowl-

edge of these subjects.

According to Ref. [37], the presence of a fault in

industrial equipment, whilst still in its incipient phase,

will be accompanied by a detectable increase or mod-

ification of vibratory signals. There is a wide range of

diagnostic techniques that can be applied in vibration

analysis to identify anomalies in machinery. It is

necessary to investigate which is the most appropriate

technique or techniques for diagnosis in a specific

machine; the operating conditions that exist, the degree

of criticality of the machine, the means, as well as the

personnel available for the control of the analysis, are

determining factors of the analysis to be performed.

There are two types of PMPs based on vibration

analysis: with portable instrumentation and on-line

acquisition system. With the portable system, data are

acquired at periodic intervals of time and are later

downloaded onto a computer [8]. The discontinuous

character hinders the obtaining of information regard-

ing the starting up and stopping of machinery and the

instants in which the process parameters change. The

costs of this type of PMP are lower than an on-line

system because the installation of instrumentation is

not needed and the number of sensors can be

reduced.

In on-line systems, the sensors are fixed in the

measurement position, information being obtained on-

line of the level of vibrations, which includes infor-

mation of transitory states such as starting up and

stopping [5]. The costs of implementation are very

much higher than in the case of portable systems, and

consequently, it is applied to critical machinery or that

found in dangerous environments.

The distinction between portable systems and on-

line systems will be illustrated in the model proposed

later; different results being obtained in each case.

Lubricant analysis consists of analysis of the state

of different physical and chemical parameters of oil in

order to verify the condition of the lubricant and the

machinery which requires investigation of the state of

wear of the equipment, level of oil contamination, and

oil condition and includes a recommendation outlin-

ing any corrective or preventive maintenance actions

that are necessary [39]. In the PMPs based on lubri-

cant analysis dispersion in the information available

about each test has been detected in managerial and

laboratory practice and there are no specifications on

the collections of tests that provide the most effective

information [26]. It has also been appreciated that

PMPs are supported by tests that provide redundant

information. There is also a shortage of information

about the latest generation technology that enables

this type of analysis and imprecisions in the industrial

plants that try to implement a program also exist [8].

All the diagnostic techniques used in a PMP

based on lubricant analysis can have top limits,

bottom limits or both. The evolution of each param-

eter can be represented depending on the hours of

operability of the lubricant. The value of the curve in

the analysis of trend not only shows the evolution of

the condition of the lubricant and the machine, but

also the speed at which the abovementioned trans-

formation takes place. The intersection of the line

that establishes the trend with the value of the limit

that is first reached, whether bottom or top, tells us

the time that must pass before a state of danger is

reached; this characteristic represents the remaining

life time of the equipment [13].

The diagnostic techniques based on vibration and

lubricant analysis that are applied at present appear in

Table 1. There are diagnostic techniques that provide

quantitative data (to which factor analysis will be

applied) and others that give qualitative information.

The integration of lubricant and vibration analyses

can provide significant profits, which so far have not

been sufficiently analyzed. For this reason, we will

now go on to detail the most relevant characteristics

that a predictive maintenance program that integrates

the analysis of vibrations and lubricants must possess.

A PMP integrating vibration and lubricant analysis

involves the acquisition of information of both tech-

niques, in order to correlate all the predictive infor-

mation to obtain an early diagnosis of the root causes

of the failures and the prediction of their consequen-

ces on the machinery.

Besides the benefits previously mentioned ob-

tained through the application of a PMP, the integra-

Table 1

Diagnostic techniques in lubricant and vibration analyses [8]

Vibration analysis Lubricant analysis

Spectral analysis Viscosity

Index of viscosity

Analysis of harmonic Water content

and orders Total acid number

Trend of the global

value of vibration

Total basic number

Cepstrum Insoluble in pentane

and benzene

Analysis of Freezing point

temporary signal Igniting point

Spike energy Combustion point

Demulsibility

Bode plot Tendency to foaming

Tendency to formation of

coal and ash content

Polar plot

Corrosion to copper sheet

Waterfalls Resistance to oxidation

Colour

Orbital analysis Stain of oil

Point of aniline

Statistical analysis Interfacial tension

Dielectric stiffness

Hilbert transform Spectroscopy of atomic emission

Envelope Infrared spectroscopy through

Fourier transform (FTIR)

Modal analysis Ferrografy

Particles count


tion of predictive techniques provides the following

additional advantages:

� An increase in the number of pieces of equipment

covered by a PMP. Vibration analysis is generally

applied in industrial plants, due to the prevalence of

rotary and reciprocating machinery, whereas lubri-

cant analysis has been adopted by machine tool,

maritime and terrestrial fleets and electrical sub-

stations submitted to significant operational loads,

etc. [35].� An increase in the set of anomalies that can be

controlled. Certain types of damage can only be

investigated by means of one of the predictive

techniques [17].� Guarantee of the diagnoses provided as the

information from both techniques is contrasted.

All the techniques present deficiencies [5,6,20], and

it is therefore advisable to confirm the diagnosis.

� Each of the predictive techniques detects deterio-

ration in different phases of its evolution [25].

Lubricant analysis is capable of detecting the

anomaly in the early phases of its development,

whereas vibration analysis will only be able to

evaluate it when the breakdown has already

occurred.� Detection of the root causes of the failures [24,35].

Consequently, there is an intensification and im-

provement of the level of information regarding

incidents that can be transmitted to the personnel in

charge of corrective activities.

Nevertheless, the simultaneous application of both

predictive techniques does not necessarily mean their

integration. The computer applications that have been

reviewed have designed the databases of each of the

predictive technologies on an independent basis and

without establishing any link of union between them

[23]. Furthermore, the instrumentation used in each of

the predictive techniques usually comes from different

manufacturers, which also impedes the integration of

the information [22].

The capacity of integration of particle counting and

ferrography techniques with spectral analysis is dem-

onstrated in the processes of wear and pollution that

lead to a mechanical breakdown [10]. The condition

of the equipment can be determined by means of

spectral analysis, and ferrographical analysis will

determine with accuracy the element or component

that is developing the anomaly; particle counting can

also determine the gradient of change of the condition

and determine if the initial cause of the failure is due

to an external factor. The periodic use of spectra in

waterfall is suggested as a diagnostic technology, as it

is a technique that identifies the condition of the

equipment and the failure that has developed, aspects

that cannot be investigated by means of other diag-

nostic techniques; moreover, as it is carried out by

means of comparison, it restricts the time of identifi-

cation of the failure.

The application of the diagnostic techniques on an

independent basis is recommended until as much

knowledge as possible has been obtained using each

technique individually, in order to later proceed to

their integration. Once this level has been reached, it

would be convenient to have software available that

incorporates utilities for this integration.


3. Introduction to the tools factor analysis and

analytic hierarchy process

3.1. Factor analysis

Factor analysis (FA) is a statistical procedure used

to determine the basic essential variables underlying a

large number of interrelated variables; a method of

processing data comprising too many variables to

allow direct analysis [27].

In FA, the researcher is usually interested in dis-

covering which variables in a data set form a coherent

subgroup that are relatively independent of one anoth-

er. The specific goal of analysis may be to outline

patterns of intercorrelatives among variables, to reduce

a large number of variables to a smaller number of

clusters while retaining maximum spread among ex-

perimental units, (to provide an operational definition

(a regression equation) for an unobserved, hypotheti-

cal construct by using observed variables, or to test a

theory about the nature of underlying variables.

Steps in a FA includes:

� Selecting and measuring a group of variables.� Preparing the correlation matrix.� Determining the number of components or factors

to be considered.� Extracting a set of components or factors from the

correlation matrix.� Rotating the components or factors to increase

interpretability.� Interpreting the results.

The correlation matrix R can be diagonalized

accomplishing and premultiplying it by the matrix V

and its transpose VV[1].

L ¼ VVRV ð1Þ

The matrix of eigenvectors V premultiplied by its

transpose produces the identity matrix:

VVV ¼ I ð2Þso, reorganising Eq. (1):

R ¼ VLVV ð3Þthe correlation matrix can be decomposed,

R ¼ ðVffiffiffiffiL

pÞð

ffiffiffiffiL

pVVÞ ð4Þ

If VffiffiffiffiL

pis called A (factor loading matrix), then

R ¼ AAV ð5Þthis equation is called the fundamental equation for

FA.

The matrix A contains correlations between factors

and variables. Usually a factor is more interpretable

when a few variables load highly on it and the rest do

not. The factorial matrix indicates the relation be-

tween the factors and the variables. Nevertheless, it is

often difficult to interpret the factors from the factorial

matrix. To facilitate the interpretation the matrix is

rotated.

The rotation consists of turning the axes of coor-

dinates, which represent the factors, until they are as

close as possible to the variables in which they are

saturated. The saturation of factors transforms the

initial factorial matrix into another matrix called a

factorial rotated matrix, of easier interpretation. The

factorial rotated matrix is a linear combination of the

first one and explains the same quantity of initial var-

iance [11]. Rotating is normally used after extracting to

maximise high correlations and minimise low ones.

Several methods of rotation exist. The most advis-

able is orthogonal rotation, and of these the most used

type is the varimax. The varimax technique accom-

plishes this aim by means of a transformation matrix

� [1].

� ¼cosc �sinc

sinc cosc

0@

1A

being w the rotation angle. Then,

Aunrorated� ¼ Arotated ð6ÞR ¼ AunrotatedAunrotatedV ð7ÞRres ¼ R � R ð8Þthe elements of this matrix must be small.

Regression coefficients for producing factor scores

from variable scores are a product of the inverse of the

correlation matrix and the factor loading matrix

B ¼ R�1A ð9ÞFactor scores are a product of standardised scores

on variables and regression coefficients.

F ¼ ZB ð10Þ


Standardised scores on variables may be predicted

as a product of scores on factors weighted by factor

loading.

Z ¼ FAV ð11Þ

Correlations among factors may be obtained by

producing a matrix of cross products of standardised

factor scores and dividing the results by the number of

cases minus one.

/ ¼ 1

N� 1

� �FFV ð12Þ

The structure matrix C is a product of the pattern

matrix of correlating among factors.

C ¼ A/ ð13Þ

In the phase of interpretation, the following steps

are considered to be fundamental:

� The study of the composition of the significant

factorial saturations of each factor.� The naming of the factors. The name must coincide

with the structure of the saturations.

3.2. Analytic hierarchy process

Decision analysis is used when a decision maker

wishes to evaluate the performance of a number of

alternative solutions for a given problem. These alter-

natives can be evaluated in terms of a number of

decision criteria. Often an alternative may be superior

in terms of one or some of the decision criteria, but

inferior in terms of some other criteria. The objective

Fig. 1. AHP h

of using an analytic hierarchy process (AHP) is to

identify the preferred alternative and also determine a

ranking of the alternatives when all the decision

criteria are considered simultaneously [33]. The use

of AHP instead of another multicriteria technique is

due to the following reasons:

� Quantitative and qualitative criteria can be in-

cluded in the decision making.� A large quantity of criteria can be considered.� A flexible hierarchy can be constructed according

to the problem.

With AHP, a complete classification of alternatives

can be obtained. Therefore, a hierarchy must be

constructed as shown in Fig. 1. In this hierarchy, the

relationship between the goal, criteria, subcriteria and

alternatives is established.

There are three main steps involved in using AHP:

� The relevant criteria and alternatives must be

determined.� Numerical measures must be attached according to

the relative importance (weights) of the criteria and

the relative performance of the alternatives to these

criteria.� The numerical values must be processed in order to

determine a ranking of each alternative.

In a decision making problem, M alternatives Ai

(i = 1, 2, 3,. . .,M) and N criteria Cj ( j = 1,2,3,. . .,N) areconsidered.

In order to determine the relative importance of the

alternatives with regard to each of the criteria or

ierarchy.


between two criteria, linguistic terms are used that

include the judgments of the decision maker. The

linguistic terms are generally associated to numerical

values constituting a scale [34].

The scale proposed by Saaty is shown in Table 2.

The quantified judgment on pair of criteria Ci and

Cj are represented by an N�N matrix A:

A ¼

a11 a12 : : : a1n

a21 a22 . . . a2n

: : : : : : : : : : : :

an1 an2 . . . ann

2666666664

3777777775

ð14Þ

where the aij is the relative importance of Ci to Cj.

The quantified judgment between alternatives with

respect to criteria Ci is represented by an M�M

matrix.

The following rules must be verified:

If aij =a then aji = 1/a, a = 0.If Ci is judged to be of equal relative importance as

Cj, then aij = aji = 1, and aii = 1 for all i.

If all the comparisons are perfectly consistent, then

the relation:

aik ¼ aijajk bi; j; k: ð15Þ

should always be true for any combination of com-

parisons taken from the judgment matrix.

Table 2

Scale of relative importances [28]

Intensity of

importance

Verbal scale

1 Equal importance

3 Weak importance of one over another

5 Essential or strong importance

7 Demonstrated importance

9 Absolute importance

2, 4, 6, 8 Intermediate values between the two adjacent jud

Reciprocals of

above numbers

If activity i has one of the above (nonzero) numb

assigned to it when compared with activity j, then

the reciprocal value when compared with i

When exact measurements of the criteria in a scale

are available for carrying out the comparisons, that is

to say, w1,w2,. . .,wn, a perfectly consistent matrix is

obtained that verifies [28]:

wi

wj

¼ aij i; j ¼ 1; 2; . . . ; n; ð16Þ

From the previous expression, it can be deduced

that:

wj

wi

aij ¼ 1 i; j ¼ 1; 2; . . . ; n; ð17Þ

and then:

Xnj¼1

aijwj

wi

¼ n i ¼ 1; 2; . . . ; n; ð18Þ

or:

Xnj¼1

aijwj ¼ nwi i ¼ 1; 2; . . . ; n; ð19Þ

and is expressed in its matricial form as [28]:

Aw ¼ nw; ð20Þ

where w is an eigenvector of A with eigenvalue n.

That is to say, since the comparisons matrix pos-

sesses a range 1, all the eigenvalues are zero except

one with value n. The sum of the eigenvalues of a

positive matrix is equal to the trace of the matrix, and

Explanation

Two activities contribute equally to the objective

Experience and judgment slightly favour one activity

over another

Experience and judgment strongly favour one activity

over another

An activity is strongly favoured and its dominance

demonstrated in practice

The evidence favouring one activity over another is

of the highest possible order of affirmation

gments When compromise is needed

ers

j has

–


the eigenvalue different to zero is named maximum

eigenvalue (kmax).

If the matrix A is not consistent and k1,. . .,kn is theset of eigenvalues that contribute a solution to the

previous matricial expression, the following expres-

sion is verified:

If aii ¼ 1; biZXni¼1

ki ¼ n ð21Þ

and wi approaches the average of n elements of line i

in the normalized matrix N.

If w is calculated from the procedure described in

Ref. [28]:

a ¼Xni¼1

wi; ð22Þ

and w is replaced by:

1

aw; ð23Þ

is verified [31]:

Aw ¼ kmaxw; ð24Þ

where kmaxz n.

The closer kmax is to n, the more consistent it is

with the comparison matrix A or the more coherent

will be the judgments provided. The consistency

index (CI) is used as a measurement of the consisten-

cy of the judgments expressed [28]:

CI ¼ kmax � n

n� 1ð25Þ

Therefore, the CI represents an average of the

eigenvalues.

The consistency ratio (CR) is obtained by dividing

the CI value by the corresponding random consistency

index (RCI) value as given in Table 3. The RCI was

evaluated by Saaty through the generation of a ran-

dom matrix with different dimensions (n) [14,32].

Table 3

Values of random consistency index

n 1 2 3 4 5 6 7 8

RCI 0 0 0.58 0.90 1.12 1.24 1.32 1.41

In the AHP, the pairwise comparisons in a judg-

ment matrix are considered to be adequately consis-

tent if the corresponding CR is less than 10%. If the

CR value is greater than 0.10, then a re-evaluation of

the pairwise comparisons is recommended. However,

perfect consistency rarely occurs in practice.

Finally, a synthesis must be performed. Synthesis

is the process of weighting and combining priorities

throughout the model that leads to the overall results.

Synthesis from the goal node multiplies the weight of

each parent node times the local priorities of its

children nodes and of those children times the local

priorities of their children. This process continues

down to and including the alternatives.

4. Model for the selection of diagnostic techniques

and instrumentation in a predictive maintenance

program

In the design and planning phase of a PMP, the

model for the selection of diagnostic techniques and

instrumentation in a predictive maintenance program

(MSDT-PMP) can be applied. This decision support

system helps to solve an unstructured problem, in

which the decision maker has doubts as to which

alternative should be selected.

The decision support model proposed can be

extended to any other machines or techniques get-

ting data from the extension or globalisation phase

of a PMP. This phase is characterized by the fact

that the time needed to get a return on the invest-

ment has been reached and, the number of machines

under control is increased or else new objectives are

set.

The PMPs have been categorised at different

technological levels depending on cost and diagnostic

capacity [8]:

� Level 0. Setup carried out using the control of

sensitive variables. The cost is practically zero and

the diagnostic capacity is very low.

9 10 11 12 13 14 15

1.45 1.49 1.51 1.48 1.56 1.57 1.59


� Level 1. Assumes the use of elementary instru-

mentation, like vibrometers or devices to do the

crackle test.� Level 2. Uses more sophisticated instrumentation,

like vibration analyzers, data processing software,

or viscometers.� Level 3. The cost is high as sophisticated analysis

machines are in use; diagnostic capacity is

excellent.

The model is elaborated taking into consideration

the previous technological levels of the predictive

techniques, lubricant and vibration analyses and the

integration of both techniques.

When a solution has been obtained from the

evaluation of the viability of the setting up of the

PMP, the most appropriate diagnostic technique must

be selected according to the type of machinery,

technical and economic characteristics and aspects

related to the human resources required, etc. For this

purpose, the model for the selection of diagnostic

techniques and instrumentation in a predictive main-

tenance program (MSDT-PMP) has been designed.

The selection of lubricant and vibration analyses

from the range of predictive techniques is due to the

fact that these are applied in a higher number of

industrial plants [9]. The introduction of the integra-

tion of both techniques is due to the fact that the

results obtained in this diagnosis are different with

respect to the application of the same techniques in

Fig. 2. Hierarchy of

isolation. This last alternative is the most advanced

step of technological maintenance.

The differentiation between portable and on-line

systems is related to the technological levels of a

PMP. Therefore, in the case of on-line systems, only

the technological level 3 is considered, corresponding

to the most technologically evolved.

With regards to the integration of the diagnostic

techniques, the technological level applied in both

predictive techniques should be similar.

The procedure developed to elaborate the model

consists of carrying out a factor analysis (FA) with the

information supplied by the diagnostic parameters. By

doing this, the aim is to eliminate the redundant

information and to keep the most relevant information

for a later analysis. When several predictive techni-

ques are applied, the factor analysis also allows the

obtaining of the relevant parameters that favour the

integration of techniques. The application of this

technique is due to the fact that the number of

parameters used in predictive maintenance is high

and does not always provide information or this

information is redundant [26]. By using FA, we aim

to get a set of variables that constitutes a coherent

subgroup and with independent elements.

FA is applied to the diagnostic predictive techni-

ques that provide quantitative data. The quantitative

information supplied by the diagnostic techniques

selected by the factors resulting from FA is completed

with the incorporation of qualitative information com-

MSTD-PMP.

Table 4

Pairwise comparison matrix and eigenvectors in a PMP based on

lubricant and vibration analyses

Technological level 3

Criteria Eigenvector

D Q COST SUP

Lubricant analysis (portable system)

D 1 2 2 3 0.424

Q 1/2 1 1 2 0.227

COST 1/2 1 1 2 0.227

SUP 1/3 1/2 1/2 1 0.122

Vibration analysis (portable system)

D 1 2 2 3 0.424

Q 1/2 1 1 2 0.227

COST 1/2 1 1 2 0.227

SUP 1/3 1/2 1/2 1 0.122

Vibration analysis (on-line system)

D 1 2 3 3 0.455

Q 1/2 1 1 2 0.141

COST 1/3 1 1 1 0.263

SUP 1/3 1/2 1 1 0.141

Table 5

Pairwise comparison matrix and eigenvectors in a PMP based on the

integration of lubricant and vibration analyses

Criteria Eigenvector

D Q COST SUP INT

Technological level 0 (portable system)

D 1 2 2 3 1 0.298

Q 1/2 1 1 2 1/2 0.158

COST 1/2 1 1 2 1/2 0.158

SUP 1/3 1/2 1/2 1 1/3 0.089

INT 1 2 2 3 1 0.298


D 1 2 2 3 1 0.298

Q 1/2 1 1 2 1/2 0.158

COST 1/2 1 1 2 1/2 0.158

SUP 1/3 1/2 1/2 1 1/3 0.089

INT 1 2 2 3 1 0.298


D 1 2 3 4 1 0.320

Q 1/2 1 1 3 1/3 0.159

COST 1/3 1 1 3 1/2 0.138

SUP 1/4 1/3 1/3 1 1/4 0.063

INT 1 3 2 4 1 0.320


D 1 2 4 6 1 0.340

Q 1/2 1 2 4 1/2 0.180

COST 1/4 1/2 1 2 1/4 0.090

SUP 1/6 1/4 1/2 1 1/6 0.051

INT 1 2 4 6 1 0.340

Technological level 3 (on-line system)

D 1 3 6 6 1 0.365

Q 1/3 1 4 4 1/3 0.163

COST 1/6 1/4 1 1 1/6 0.053

SUP 1/6 1/4 1 1 1/6 0.053

INT 1 3 6 6 1 0.365


ing from other diagnostic techniques, where the

results of analysis do not give numerical results. The

previous process is applied to each technological level

of a PMP and that will be reviewed in this paper.

The most significant diagnostic techniques regard-

ing each technological level related to the factors

obtained from the FA and the qualitative diagnostic

techniques are incorporated as alternatives in a hier-

archy to which AHP is applied. By means of this

procedure, the techniques that provide redundant

information and the techniques that do not give

relevant information for diagnosis are eliminated and

the integration of vibration and lubricant analysis is

favoured.

The decision variables used to construct the hier-

archy are:

� Diagnostic quality (D).� Quantity of failures that can be analyzed (Q).� Cost of diagnostic technique (COST). This variable

is decomposed in investment cost (INVC), setup

cost (SETC) and maintenance cost (MANC) of the

diagnostic technique.� Supportability of the diagnostic technique (SUP).

This variable includes: quantity of training needed

to apply the technique (T), its portability (P),

negative influences on the human resources due to

its application (HR), its maintainability (M) and

easy use (EU).

In the case of a PMP integrating lubricant and

vibration analyses, the variable capacity of integration

(INT) is incorporated to favour the integration process

of predictive techniques and to avoid the selection of

incompatible techniques.

The hierarchy elaborated with the goal, decision

variables and alternatives is shown in Fig. 2.

As examples, the pairwise matrix and eigenvectors

obtained from the criteria corresponding to techno-


logical level 3 in a PMP based on lubricant analysis

and vibration analysis are in Table 4 As can be

appreciated in Table 4, on-line system is associated

to vibration analysis, because the instrumentation in

lubricant analysis is placed in laboratories and the data

are always periodic.

When a PMP is applied based on the integration of

lubricant and vibration analyses, the pairwise matrix

and eigenvectors obtained from the criteria corres-

ponding to the different technological levels are in

Table 5.

The diagnostic techniques to be applied are depen-

dent on the type of machinery, and therefore the

results shown have been achieved by applying the

MSDT-PMP to screw compressors.

Table 6

Diagnostic techniques analyzed

Diagnostic techniques Code

Lubricant analysis

Water content in lubricant WACONT

Colour of lubricant COLOUR

Density DENSITY

Content in wear metals (iron) WEAR1

Viscosity index VISCINDEX

Content in wear metals (lead) WEAR2

Content in contamination metals (silicon) SI

Total acid number TAN

Viscosity to 100 jC VISC (100)

Viscosity to 40 jC VISC (40)

Vibration analysis

Tendency of global vibration value of RMS

(10–1000 Hz)

TEN1

Spectral analysis/density of spectral power ES

Waterfalls WA

Spike energy SP

Harmonic tendencies/peak values TEN2

Time signal analysis/form and crest factors TEM

Statistical analysis (kurtosis, variance analysis) KV

Bode diagram BO

Polar diagram PO

Orbital analysis OR

Finite modal element/experimental

modal analysis

FEM

Cepstrum/envelope CE+EN

5. Case study of a screw compressor with the

integration of lubricant and vibration analyses

A PMP was designed and set up in a petrochem-

ical plant. The program was applied to three screw

compressors.

The industrial equipment submitted to the analysis

was adapted for the incorporation of an integrated

PMP of vibration and lubricant analysis for the

following reasons [7]:

� The equipment has high criticity; its breakdown

supposes the temporary closedown of the whole

plant of lubricant production.� The equipment is rotary, and therefore, adapted for

the application of a PMP based on vibration

analysis.� Most of the mechanical components of these

compressors are bathed by the same lubricant,

therefore this can gather information about the

condition of all of them.� The compressors are placed in a petrochemical

plant and within the area of influence of the plants

there are two thermal plants. This factor suggests

the possibility of the influence of environmental

pollution, generating phenomena of grazing and

corrosion.� It is possible to trace the development of

deterioration in the machinery from its initial

stage, by means of lubricant analysis, up to

the stage at which the mechanical damage

can be demonstrated, by means of vibration

analysis.

The acquisition of data used in the analysis is

rather complicated due to the specificity of this kind

of data in the industrial plant, the high cost of

acquisition and the restricted access to the data.

Nevertheless, two full years of monthly acquisition

of data were developed.

The model selects a minimum of two alternatives

between which integration can take place. The capac-

ity of integration between lubricant and vibration

techniques and between diagnostic techniques belong-

ing to vibration analysis or lubricant analysis in

isolation has been maintained.

The diagnostic techniques analyzed are in Table 6.

The results of applying FA to diagnostic param-

eters in a screw compressor when integration be-

tween lubricant and vibration analyses is applied is

described.

Table 7

Correlation matrix between lubricant and vibration parameters in a screw compressor

WACONT COLOUR DENSITY WEAR1 VISCINDEX TEN1 WEAR2 SI TAN VISC (100) VISC (40)

WACONT 1.00000

COLOUR 0.55024 1.00000

DENSITY 0.36161 0.97058 1.00000

WEAR1 0.16166 0.51704 0.63661 1.00000

VISCINDEX 0.87632 0.28724 0.04897 � 0.33333 1.00000

TEN1 0.85443 0.27343 0.03393 � 0.37314 0.99889 1.00000

WEAR2 � 0.53558 0.17235 0.24485 � 0.33333 � 0.33333 � 0.29639 1.00000

SI 0.38984 0.98306 0.98786 0.51011 0.13912 0.12986 0.32462 1.00000

TAN � 0.67486 0.05620 0.29491 0.60150 � 0.93486 � 0.94334 0.31162 0.19257 1.00000

VISC (100) 0.40582 0.98133 0.99873 0.62014 0.09914 0.08393 0.22356 0.99067 0.24712 1.00000

VISC (40) 0.18922 0.89371 0.97500 0.73849 � 0.16644 � 0.18363 0.26017 0.93635 0.49708 0.96320 1.00000


The correlation matrix between the quantita-

tive diagnostic techniques analyzed is shown in

Table 7.

The determinant of the correlation matrix is low.

Consequently, there are high intercorrelations between

the variables. This characteristic is necessary in order

to apply factor analysis.

Due to the quantity of factors available being too

high, a factor analysis has been applied, to obtain a set

of variables that form a coherent, independent group.

Three factors get 100% of the accumulated percentage

of variance, as can be appreciated in Table 8. As a

result, only the factors with eigenvalue superior to 1

are preserved (Kaiser rule).

The rotation through varimax simplifies the results

(Table 9) and facilitates interpretation of the data. As

can be appreciated in Table 9, each variable is only

saturated in one factor and each factor has distinct

load distribution. Thus factor 1 is called contamina-

Table 8

Integration of diagnostic parameters in factors

Factor Eigenvalue Percentage of

variance

Accumulated

percentage of

variance

1 5.57269 50.7 50.7

2 4.02062 36.6 87.2

3 1.40669 12.8 100.0

4 0.00000 0.0 100.0

5 0.00000 0.0 100.0

6 0.00000 0.0 100.0

7 0.00000 0.0 100.0

8 0.00000 0.0 100.0

9 0.00000 0.0 100.0

10 0.00000 0.0 100.0

11 0.00000 0.0 100.0

tion due to the fact that it has the highest contribution

in variables such as silicon, content colour, density,

etc, which are indicative of a contamination process in

the compressor. Factor 2 is called degradation due to

its having the highest contribution of the variables

total acid number or water content which are indica-

tive of a degradation process in the lubricant with a

lack of additives. Factor 3 is called wear because it

brings together the two variables that analyzed the

wear process in the compressor such as the lead and

iron content.

The diagnostic techniques that provide more infor-

mation about the contamination, degradation and wear

process (results of factor analysis) in the compressor

are selected as alternatives. These alternatives are

introduced in the hierarchy of Fig. 2 joint with the

alternatives that give qualitative information. So, the

alternatives considered by technological level are in

Table 9

Results provided before applying a rotation through varimax

Factors

Contamination Degradation Wear

WATCONT 0.37138 0.83074 0.41466

COLOUR 0.97404 0.22638 0.00107

DENSITY 0.99987 � 0.01477 0.00615

WEAR1 0.62651 � 0.41484 0.65985

VISCINDEX 0.06332 0.99613 0.06097

TEN1 0.04859 0.99869 0.01623

WEAR2 0.24623 � 0.29375 � 0.92363

SI 0.98992 0.08373 � 0.11419

TAN 0.28052 � 0.95891 0.04237

VISC (100) 0.99929 0.03521 0.01315

VISC (40) 0.97139 � 0.23196 0.05105

Table 10

Diagnostic techniques in each technological level in a PMP based on integrating vibration and lubricant analysis

Portable system On-line system

Level 0 Level 1 Level 2 Level 3 Level 3

Content in wear and

contamination

Content in wear and

contamination metals

Content in wear and


Content in wear and


Content in wear and


metals Spectral analysis Spectral analysis/density

of spectral power

Spectral analysis/density

of spectral power

Vibration analysis Tendency of global

vibration value RMS

(10–1000 Hz)

Viscosity to 40 jC Viscosity to 40 and 100 jC Viscosity to 40 and 100 jC

Colour of lubricant Viscosity to 40 jC Waterfalls Waterfalls Waterfalls

Tendency of global

vibration value RMS

(10–1000 Hz)

Tendency of global

vibration value RMS

(10–1000 Hz)

Tendency of global

vibration value RMS

(10–1000 Hz)

Spike energy Spike energy Spike energy

Water content Water content Harmonic tendencies/

peak values

Cepstrum/envelope Cepstrum/envelope

Water content Water content/total

acid number

Water content/total

acid number

Time signal analysis/form

and crest factors


and crest factors

Polar diagram


Table 10. Each of these alternatives has associated a

particular instrumentation in agreement with their

technological level.

Next, the AHP is applied.

The maximum number of alternatives permitted in

AHP is nine, and therefore, this is the number of

alternatives or diagnostic techniques considered in the

technological levels 2 and 3 of the model. Although

there are other diagnostic techniques, the more repre-

sentatives have been included.

The diagnostic techniques have been adapted to

each technological level. Thus, water content is in-

cluded in level 0 by means of visual inspection and in

level 2 by means of a Karl Fischer device. Therefore,

the results include the instrumentation needed to apply

each of the diagnostic techniques in each technolog-

ical level.

6. Results

In this section, the results of the model after

applying factor analysis to the data obtained in screw

compressors of a petrochemical plant, and by inte-

grating qualitative and quantitative variables from

lubricant and vibration analyses are presented. As

can be appreciated in Table 11, the consistency ratio

has values which are inferior to 0.1 in all the cases,

and therefore, is considered acceptable.

6.1. Portable system

6.1.1. Technological level 0 (Table 11)

The diagnostic techniques analyzed provide infor-

mation about the degradation in colour, water content

in the inspection of free water, contamination by

particles and preferably wear and anomalous mechan-

ical behaviour. The complementary nature of each

technique is demonstrated by the close preferences.

The existence of similar values recommends the

application of all the parameters to the industrial plant,

and this is beneficial when a total productive mainte-

nance is combined with a PMP.

The model selects visual inspection of particles in

lubricant and visual inspection of vibration (or use of

screwdriver), favouring the integration process of

lubricant and vibration analyses.

6.1.2. Technological level 1

Table 11 shows that the global preferences of

alternatives are very close, and therefore the use of

all the techniques applying the concept of comple-

mentarity is recommended, unless the plant is inter-

ested in a limited number of techniques, in which case

Table 11

Hierarchy of the diagnostic techniques in technological levels 0, 1, 2

and 3

Selection of diagnostic techniques in a PMP based on integrated


Diagnostic technique Instrumentation Preferences


Content in wear and


Visual inspection of

particles in lubricant

(no instrumentation)

0.275

Vibration analysis Visual inspection/use

of screwdriver

0.275

Colour of lubricant Visual inspection

(No instrumentation)

0.225

Water content Visual inspection

(No instrumentation)

0.225

Consistency ratio = 0.00


Viscosity to 40 jC Capillary viscometer 0.305

Tendency of global

vibration value RMS

(10–1000 Hz)

Vibrometer 0.262

Content in wear and


Stain of oil 0.229

Water content Crackle test 0.204



Spectral analysis Spectral analyzer 0.200

Waterfalls Spectral analyzer 0.180

Content in wear and


Particle meter 0.172

Viscosity to 40 jC Capillary viscometer 0.120

Tendency of global

vibration value rms

(10–1000 Hz)

Vibrometer/

spectral analyzer

0.087

Spike energy IRD spectral analyzer 0.077

Harmonic tendencies/

peak values

Spectral analyzer 0.068

Water content Karl Fischer 0.053


and crest factors

Spectral analyzer/

oscilloscope

0.044



Content in wear and


Spectrometer of

atomic absorption

0.224


of spectral power


Viscosity to 40

and 100 jCAutomatic viscometer 0.135


Water content/total

acid number

Karl Discher/tritrador 0.109

Cepstrum/envelope Advanced oscilloscope 0.080

Table 11 (continued)





Tendency of global

vibration value RMS

(10–1000 Hz)

Vibrometer/spectral

analyzer

0.060

Time signal

analysis/form

and crest factors

Spectral analyzer/

oscilloscope

0.052



viscosity and tendencies of global vibration value

RMS between 10 and 1000 Hz can be applied. The

use of the stain of oil technique can be considered as a

support technique.


The model supplies (see Table 11) diagnostic

techniques with quality of diagnosis and capacity

for integration with other techniques. The model

suggested the application of spectral analysis and

waterfalls, particle counting and viscosity control.

Therefore, the instrumentation required is: spectral

analyzer, particle meter and capillary viscometer.

The other alternatives have preferences inferior to

the aforementioned. It should be pointed out that the

tendency of global vibration value of RMS between

10 and 1000 Hz owes its classification to the lower

setup and maintenance cost, but considering that the

industrial plant has a high technological level, the

cost variable should not influence the selection of

alternatives.


The classification of alternatives shown in Table

11 suggests the application of more technological

techniques to provide better quality in the diagnosis

and a superior capacity for the protection of machin-

ery, because they detect the failures and the deterio-

ration of elements and lubricants in early phases of

development. The use of spectrografy, a technique

that defines the factors that are contributing to wear

and contamination, together with spectral analysis

that detects wear effects are the techniques that

supply the most reliable and fast diagnoses. These

diagnostic techniques also provide information about

Table 12

Hierarchy of the diagnostic techniques in technological level 3 in a

PMP based on continuous integrated lubricant and vibration

analyses




Content in wear and


Spectrometer of

atomic absorption

0.222


of spectral power


Viscosity to 40 and 100 jC Automatic viscometer 0.131


Water content/total

acid number

Karl Fischer/tritrador 0.107

Polar diagram Continuous acquisition

system/displacement

sensors/key phasor

0.079

Cepstrum/envelope Advanced oscilloscope 0.079


Tendency of global

vibration value RMS

(10–1000 Hz)

Vibrometer/spectral

analyzer

0.055



the most suitable moment in which to carry out the

change of lubricant due to contamination or loss of

protection capacity. These techniques can be comple-

Fig. 3. Sensitivity analysis corresponding to t

mented with viscosity and waterfalls, as the model

suggests.

The model minimizes the repetitive information in

the process of selection of diagnostic techniques.

6.2. On-line system


The procedure used is similar to that in techno-

logical level 3 in a portable system, although it

rejects the alternatives with the lower preferences

obtained in the level 3 of a PMP based on vibration

analysis. This means the analysis can be limited to

nine alternatives. As can be seen in Table 12, the

classification is very similar to that obtained in a

portable system, although the technology applied is

superior in this case, because it provides a more

exact control of the state of machinery. The quantity

of techniques applied in the plant depends on eco-

nomic variables and the criticality of industrial

machinery, which is generally very elevated in this

technological level. Therefore, the use of the follow-

ing instrumentation is suggested: spectrometer of

atomic absorption, spectral analyzer and automatic

viscometer. This allows the application of diagnostic

techniques: content in wear and contamination met-

echnological level 3 in on-line system.


als, spectral analysis and density of spectral power,

viscosity to 40 and 100 jC and waterfalls. The

greater weight given to the polar diagram rather than

the cepstrum/envelope is due to the capacity of the

first to provide information about the behaviour of

axis, an aspect that cannot be analyzed with any

other alternatives.

The sensitive analysis corresponding to the setup

of a PMP based on integrating lubricant and vibration

analyses provides stable results in all the technolog-

ical levels. Fig. 3 shows an example of the sensitivity

analysis corresponding to technological level 3 in an

on-line system.

7. Conclusions

In the decision support model designed, technolog-

ical and organizational issues have been incorporated

that until now had not been sufficiently researched in

the topic of a predictive maintenance program.

Vibration analysis and lubricant analysis are the

most frequently applied predictive techniques at pres-

ent, as a result of which the integration of both

techniques in a single predictive maintenance program

can provide significant benefits for the company.

A model of selection of diagnostic techniques and

instrumentation in a predictive maintenance program

(MSDT-PMP) has been developed. Factor analysis

and AHP have been combined. The model is applied

to different technological levels in PMPs based on

integrated lubricant and vibration in screw compres-

sors placed in a petrochemical plant.

The results obtained will facilitate the decision

making of the planner of the predictive maintenance

program, as well as favour the development of the

integration of predictive techniques, an aspect that

currently lacks models for making decisions, due to

the technical and organizational difficulties that its

application represents, aspects in which this article

aims to contribute.

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http://www.wearcheck.com/info/about_interpretation.asp

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