Lagerschade Analyse
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Transcript of 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.