Maintworld 4/2019

PARTNER ARTICLE
failure. The challenges of this application
are (1) mastering large volumes of
data, (2) understanding complex analysis
processes, and (3) generating transparent
forecasts. Therefore, this model has
not yet become prevalent in roller bearing
condition diagnostics in the wind
industry.
With that being said, “Smart data”
applications aim to extract useful characteristics
from large data volumes
using often similar analysis methods,
which users can understand and use as
a starting point for additional analyses.
There are statistical methods for extracting
characteristics, which transfer
overall reading trends from the time
domain into the frequency domain. Ultimately,
determining the representative
parameters from this information.
This statistical processing model offers
a high level of added value for wind turbines
with highly transient operating
behaviour.
However, in order to implement
a large-scale cost-effecting system
to monitor roller bearings, it makes
sense to prioritize the characteristics
extracted as an irregularity. This can
be done by prioritizing limited values
of the DIN ISO 13373-3 (broadband
overall values) or characteristics of the
VDI 3832.
Figure 4 depicts the sequences of
data-driven condition monitoring. The
CMS act as an additional data supplier
for overall readings and diagnostic
characteristic values. If large scales or
varieties of bearings are to be monitored,
the classification is followed by
a weighted process that assigns predefined
meanings to extracted features
depending on their characteristics.
These predefined weighted parameters
are put in place by knowledgeable experts
and experience.
For this procedure to function, the
CMS must quickly and synchronically
collect measurements across various
channels. Allowing it to issue frequencyselective
and order-selective overall
readings and diagnostic characteristics.
An incredibly powerful online CMS is
the VIBGUARD IIoT from PRUFT-
ECHNIK. The VIBGUARD IIoT gains
its edge in its ability to offer its users
data reduction options while supporting
IoT-relevant protocols, such as MQTT.
Ultimately, enabling the evaluation of
vibration and diagnostic priorities in the
control center.
Roller bearing
monitoring
Approach
Monitored
characteristic
Diagnostic
procedure
Figure 3 Approaches to data-driven condition monitoring not only on rolling bearings
Data collection
Broadband overall readings
Narrow band overall readings
Diagnostic characteristic
values amplitude spectrum
Diagnostic characteristic
values envelope spectrum
Diagnostic characteristic
values time signal
Traditional
Exceeding limit values
for characteristic value
amplitudes
Diagnoses in the
case of exceeding
Preparation and
extraction of
characteristics
Rotor blade bearings
“Big Data”
Characteristics in the
time / frequency range
Statistical methods /
Machine learning
Classification
DIN ISO 13373
& VDI 3832
Gearbox bearing PCS
Figure 4. Diagram for the data-driven condition monitoring process
Main bearing
Generator bearing
Damage frequency
“Smart Data”
Gearbox bearing
HSS
Figure 2. Connection between frequency of roller bearing damage in wind turbines with
gears and the resulting financial expenditure caused
Pre-processed
characteristic values
Hybrid diagnosis and
criticality assessment
Vibration priority number
prioritization level 1
Diagnostic priority number
prioritization level 2
Other diagnosis according to
priority
Table 1: Relationship between diagnostic features and damage stages (criticalities) of a
roller bearing based on VDI 3832
10 maintworld 4/2019