Lagerschade Analyse

7/29/2019 Lagerschade Analyse

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1/03 evolution.skf .com

technology

SummaryThe SKF Panloc bearing unit

offers customers a simple way to

adjust internal clearance/pre-

load,high rigidity,low operating

temperature and easy replace-

ment of the bearing unit.In terms

of overall costs,this unit is an

optimal bearing unit that can be

adjusted to specific applications

and customer needs.In addition,

the advantages of the SKF Panloc

ensure increased operating relia-

bility for the customers system.

Research and development

within SKF in terms of this bearing

unit are still in progress.

Investigations are pointed in

the following direction:

I seal and lubrication -> lifetime

lubrication

I increasing the basic load rating

-> greater service life and rigidity

I a new concept for rubber

cylinder bearings in printing

machines.

Over the course of this year,addi-

tional and more detailed results

in this regard will be

available.

E V O L U T I O N I 2 5

Decision-supportsystem for bearingfailure mode analysisGaining insight and information from rolling bearing

damage and failures is of strategic importance for SKF

and its customers. The knowledge collected on bearing

damage is accessible for SKF engineers as a web-enabled

decision-support system called SKF Bearing Inspector.

byGERARD SCHRAM, SKF Reli abil ity Syst ems, andBAS VAN DER VORST, SKF Engi nee ring &

Research Center B.V.,Nieuwegein,the Netherlands

Heavy wear on the outer ring of a

cylindrical roller bearing oper-

ating in an electric motor of a

paper winder in the reel section

of a tissue paper machine.

Small pitting is observed after

further microscopic inspection of

the raceway.It also shows a small

layer of rehardened material,due

to local high temperature.

http://evolution.skf.com

Read more at

The decision-support system SKF Bear-

ing Inspector is aimed at offering

increased speed,consistency and

higher quality in the bearing decision-

making process. It should help to prevent

bearing damage or failure from reoccurring.

As with any knowledge-based computersystem, SKF Bearing Inspector gathers all the

relevant information and experience avail-

able about rolling bearing damage from

basic principles to practical engineering

results.

Current knowledge-based systems have

benefited from the experience of expert

systems developed in the 1980s, although

these suffered major flaws in aspects of

reasoning capacity and computer power.

These systems were often structured as

decision trees that led from symptoms to

possible causes. Causal relations betweensymptoms and possible reasons do not

exist in reality and can easily lead to wrong

conclusions. This is simply because the

reasons (e.g.,wrong bearing mounting)

result in the damage symptoms (e.g., fret-

tingsigns), and not the other way around. A

modeling of a relationship from causes to

symptoms where uncertainty is attached to

possible failure states fits much better with

the physical phenomena that occur during

bearing service life. With the aid of


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state-of-the-art computational intelligence

techniques, this approach has been followed

for the development of the program.

Knowledge system

Within a knowledge system, one generally

distinguishes between modeling the know-

ledge with a certain knowledge representa-

tion and the reasoning principle, in order to

derive problem-solving capacity. Regarding

knowledge representation, several forms

exist, such as:

ICases: Many bearing failure experiences

can be found in case examples.

Unfortunately, many practical cases arenot well documented, and no uniformity

regarding the documented parameters

or failure mode conclusions exists.

Example cases can,however, be used to

model or verify other knowledge

representations.

IRules: It is possible to generalize if-then

rules between observed symptoms and

possible causes. However, this is not

appropriate because different causes

can have similar effects that appear as

similar symptoms.

IArtificial Neural Networks:

Mathematical relationships between

symptoms and causes can be derived by

using example failure cases. However,

there are not sufficient number of

discriminating cases to do this.

Furthermore, system users require

additional explanations rather than

black box artificial neural network

relationships that do not carry such

explanations.

IProbabilistic Networks: It is possible to

derive visual networks in which nodes

are connected by causal relationships,

based on bearing failure theory and

experience. Furthermore, probabilities

are assigned indicating the weakness or

strength of those relationships. By intro-

ducing correct causality from conditions

to observations, this knowledge repre-

sentation best fits the bearing failure

diagnosis problem.

Analysis of bearing damage and failure is

principally a diagnostic task. Imagine a

patient visiting his doctor with a specific

complaint. The doctor first questions the

patient about specific body and life-style

parameters such as weight, smoking,etc.

(conditions). Based on that, the doctor

makes hypotheses about likely diseases

(failure modes). The doctor verifies or

rejects these hypotheses through further

questioning and inspection of the patient

(symptoms).

The process of a damage or failure analy-

sis is similar to the doctors approach. In a

correct diagnosis, there are two reasoning

steps:1. Hypotheses generation is where possible

failure hypotheses are generated based on

data. For example,the doctor starts asking

questions to get an idea (hypothesis) of

what could be wrong;

2.Verifying or rejecting hypotheses. One by

one, the generated hypotheses are investigated

and verified or rejected. For example, the

doctor starts investigating the most probable

diseases by conducting specific medical

tests (blood pressure, heart rate, etc.).

With a probabilistic network, the two-

step reasoning is implemented by forward

and backward probability calculations.

Probabilistic network

The probabilistic network is a visual net-

work in which nodes are connected by

causal relationships, and probability calcu-

lations are applied. The network for bearing

failure analysis has four node categories:

conditions,internal mechanisms,failure

modes and observed symptoms. Conditionsrepresent the conditions from and under

which the bearing operates. Examples are

speeds, bearing type, load, temperature,

installation details,environmental factors,

etc. Internal mechanisms represent the

physical phenomena that happen during

operation,such as lubrication,film disruption,

2 6 I E V O L U T I O N evolution.skf .com 1/03

CONDITIONAL PROBABILITY THAT SLIDING CONTACT IS TRUE OR FALSE,GIVEN ACCELERATION IS TRUE OR FALSE

P(sliding contact | accelerations) Sliding contact = TRUE Sliding contact = FALSE

Accelerations = TRUE 0.6 0.4

Accelerations = FALSE 0.2 0.8

ROLLING BEARING FAILURE MODES

Fatigue Subsurface-initiated fatigue

Surface-initiated fatigue (surface distress)

Wear Abrasive wear

Adhesive wear

Corrosion Moisture corrosion

Frictional corrosion Fretting corrosion

False brinelling

Electrical erosion Excessive voltage

Current leakage

Plastic deformation Overload

Indentation Indentation from debris

Indentation by handling

Fracture Forced fractureFatigue fracture

Thermal cracking

Table 2.

Table 1.


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sliding contact,etc. Failure modes repre-

sent the types of failure, such as subsurface-

initiated fatigue and fretting corrosion. In

table 1, the various failure modes are

listed. Observed symptoms represent the

observable phenomena inside and outside

the bearing, including discoloration,

spalling, rust, etc.

About 150 nodes are connected by causal

relations between conditions of the bearing

application,hidden mechanisms,(physical)

failure modes and observed symptoms.

In the modeling of the network, various

sourcesof information were used. Apart

from defining the nodes, the causal relations

and probabilities, explanation texts (foreach node) including examples and pictures

are developed. In total, about 250 pictures

have been included in the system.

Calculation step 1

Hypothesis generation:Once the network

is modeled, the reasoning process can start.

The initial nodes (no inputs) have two or

more states. Each state is assigned a prior

probability between 0 and 1, with the total

over the states being 1. For example:

IP (accelerations = TRUE) = 0.05

IP (accelerations = FALSE) = 0.95In the systems user interface, the user can

state that acceleration is true. This will

change the above probabilities into 1.0 and

0.0, respectively. In the network, condi-

tional probability tables are defined (table 2).

When nodes have more states (more than

only true and false) or when they have more

input relations, the tables grow. With the

conditional probability tables, the probabil-

ities of the other nodes can be calculated by

the formula:

IP(B) = P(B|Ai) P(Ai), for all i

with P(B|Ai) being the conditionalprobability given the condition Ai. In the

example:

IP(sliding contact = TRUE) = 0.6 0.05 +

0.2 0.95 = 0.22

IP(sliding contact = FALSE) = 0.4 0.05

+ 0.8 0.95 = 0.78

In this way,all the probabilities of the

nodes are calculated,given the prior prob-

abilities of the start nodes. By considering

the application conditions as the start nodes,

the probabilities of the failure modes can

be determined and ranked. This is the

failure hypotheses generation. Notice that

uncertainties are attached to the node states

rather than to if-then rules in a classical

expert system.

Calculation step 2

Verification or rejection by inversion: After

hypotheses are generated, we have to verify

or reject them by investigating the bearing.

This ranges from visual inspection of the

bearing to simple or complex laboratory

tests. For this purpose, one first has to

explain how probabilities of observations

will influence the probabilities of the failure

hypotheses. As this is causally different,

one has to reason backwards. Without

going into detail, the heart of this reasoning

lies in the formula:

IP(B|C) = P (C|B) P (B) / P(C)

This states that the belief in hypothesis B

obtaining evidence C can be computed by

1/03 evolution.skf .com E V O L U T I O N I 2 7

technology

Example: step 2:Inspection on symptoms for current leakage failure mode.After inspection

and enlargement of the runway surface,small pitting is confirmed. Several examples are pro-

vided under the information button.

Example:Final diagnosis:results based on the provided application conditions (step 1) and bear-

ing system inspections (step 2).Both the probabilities of the most relevant failure modes and

related internal mechanisms are listed.The results can be printed out as MS Word or HTML

report.

Example: step 1:Application conditions are filled by loading the electric motor winder data

among other bearing type,coating,speeds,etc.Detailed information and

examples are provided under the information button.


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multiplying our previous belief P(B) by the

conditional probability P(C|B) and that C is

true if B is true. The conditional probabil-

ities P(C|B) are modeled in the network as

causal relation,P(B) comes from forward

reasoning (step 1),and P(C) are set during

the bearing investigations. These are the

observations that serve as evidence for the

failure hypotheses. P(B|C) is called the

posterior probability, while P(B) is the prior

probability.

Instead of investigating all possible

observations and non-filled-in conditions,

the most relevant ones are suggested,depen-

dent upon the failure hypothesis (or internal

mechanisms) that need to be investigated.In other words, these are the application

conditions or observations that have the

most discriminating effect on the failure

hypothesis. The discriminating effect is

determined by a mathematical measure.

For all possible not-filled-in conditions or

observations, this measure is scaled

between 0 and 100. An example is given in

the illustrations. Eventually, by investigat-

ing the application conditions and obser-

vations,the likelihood of the failure

hypotheses and internal mechanisms are

determined and ranked. These then formthe conclusions of the bearing damage

analysis.

The system is further extended with

various functions that can help the user.

A simple file with user instructions is

provided for getting started. The system

offers translation of key terms into English,

German,French and Swedish. Session

data control is available for session data

storage and retrieval. Also, through a file

marked Typical Examples, users can be

guided through the application of the

program. For convenience, an extensivereport can be generated in MS Word or

HTML format, including the relevant con-

ditions,observations and failure mode

probabilities.

Practical example

The SKF Bearing Inspector contains several

common bearing damage cases located under

Typical Cases. These can be used as

training material to demonstrate how the

SKF Bearing Inspector supports the analysis

of a bearing damage investigation. One

exampleis of an electric motor in a paper mill.

In this case, an electrically insulated cylindrical

roller bearing NU 322 ECM/C3VL024 is

used in an electric motor of a paper winder

in the reel section of a tissue paper machine.

The electric motor speed is variable (400

VAC with frequency converter) and running

between 1000 and 1500 min-1. After only a

month of operation however,heavy wear

was observed on the inner and outer rings.

Loading the example case in SKF Bearing-

Inspector sets all known application condi-

tions (step 1). The first hypothesis of possi-

ble failure modes is calculated based on

these application conditions. At this pointin the analysis, SKF Bearing Inspector gives

a high likelihood of false brinelling, adhesive

wear and current leakage. At first sight

current leakage and false brinelling seem

unlikely because the machine uses insulated

bearings and all machines are properly

supported with rubber pads.

The user then has to perform the second

step of the analysis by inspecting the bear-

ing on failure symptoms. Clicking inspect

results in a list of damage symptoms most

relevant to the selected failure mode. The

bearing is first inspected for false brinelling.

Because no shallow depressions are found

that can verify false brinelling, this failure

mode is rejected. The analysis is continued

with inspecting of symptoms of adhesive

wear. None of the symptoms related to

adhesive wear are found either. Finally,by

inspecting electrical current leakage symp-

toms, the presence of small pitting is found

after magnification of the raceway surface.

This verified the current leakage failure

mode. Subsequently, the customer indeed

discovered an earthing problem in the

winder construction causing the electricalcurrent leakage.

Conclusions

SKF Bearing Inspector meets the need for a f

more consistent,high-quality decision-

making process for bearing damage and

failure investigations. This web-enabled system

is available for SKF engineers to support

customers in bearing damage and failure

investigations.

2 8 I E V O L U T I O N evolution.skf .com 1/03

SummarySKF has put its bearing expertise and knowledge into a decision-support system

available for SKF engineers.The system has been

developed to meet the need for a fast,more consistent,high-quality

decision-making processfor bearing damage and failure investigations.

The system draws its experience from the wealth of information available

from experts,customers,practical research and published documentation on

bearing performance and failure modes. The system overcomes the short-

comings of previous expert systems and incorporates improved decision-making

processes that help identify the true causes of bearing failures.

Documents

Lagerschade Analyse