TY - GEN
T1 - Wind turbine main bearing diagnosis - A proposal of data processing and decision making procedure under non stationary load condition
AU - Zimroz, Radoslaw
AU - Bartelmus, Walter
AU - Barszcz, Tomasz
AU - Urbanek, Jacek
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Condition Monitoring of bearings used in Wind Turbines (WT) is an important issue. In general, bearings diagnostics is well-recognized field; however it is not the case for machines working under non-stationary load. An additional difficulty is that the Main Bearing (MB) discussed here, it is used to support low speed shaft, so dynamic response of MB is not clear as for a high-speed shaft. In the case of varying load/speed a vibration signal acquired from bearings is affected by operation and makes the diagnosis difficult. These difficulties come from the variation of diagnostic features caused mostly by load/speed variation, low energy of sought features and high noise levels. In the paper a novel diagnostic approach is proposed for main rotor bearings used in wind turbines. From a commercial diagnostic system two kind of information have been acquired: peak-to-peak vibration acceleration and generator power that is related to the operating conditions. The received data cover the period of several months, when the bearing has been replaced due to its failure and the new one has been installed. Due to serious variability of the mentioned data, a decision-making regarding the condition of bearings is pretty difficult. Application of classical statistical pattern recognition for data from the period A (bad condition) and the period B (after replacement, good condition) is not sufficient because the probability density functions of features overlap each other (pdf of peak-to-peak feature for bad and good conditions). Proposed approach is based on an idea proposed earlier for planetary gearboxes, i.e. to analyse data for bad/good conditions in two-dimensional space, feature - load. It is shown that the final data presentation is a good basis to the very successful classification of data (i.e. recognition of damaged and undamaged bearings).
AB - Condition Monitoring of bearings used in Wind Turbines (WT) is an important issue. In general, bearings diagnostics is well-recognized field; however it is not the case for machines working under non-stationary load. An additional difficulty is that the Main Bearing (MB) discussed here, it is used to support low speed shaft, so dynamic response of MB is not clear as for a high-speed shaft. In the case of varying load/speed a vibration signal acquired from bearings is affected by operation and makes the diagnosis difficult. These difficulties come from the variation of diagnostic features caused mostly by load/speed variation, low energy of sought features and high noise levels. In the paper a novel diagnostic approach is proposed for main rotor bearings used in wind turbines. From a commercial diagnostic system two kind of information have been acquired: peak-to-peak vibration acceleration and generator power that is related to the operating conditions. The received data cover the period of several months, when the bearing has been replaced due to its failure and the new one has been installed. Due to serious variability of the mentioned data, a decision-making regarding the condition of bearings is pretty difficult. Application of classical statistical pattern recognition for data from the period A (bad condition) and the period B (after replacement, good condition) is not sufficient because the probability density functions of features overlap each other (pdf of peak-to-peak feature for bad and good conditions). Proposed approach is based on an idea proposed earlier for planetary gearboxes, i.e. to analyse data for bad/good conditions in two-dimensional space, feature - load. It is shown that the final data presentation is a good basis to the very successful classification of data (i.e. recognition of damaged and undamaged bearings).
KW - Diagnosis
KW - Main bearing
KW - Non-stationary operation
KW - Vibration analysis
KW - Wind turbine
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U2 - 10.4028/www.scientific.net/KEM.518.437
DO - 10.4028/www.scientific.net/KEM.518.437
M3 - Conference contribution
AN - SCOPUS:84864722512
SN - 9783037854433
T3 - Key Engineering Materials
SP - 437
EP - 444
BT - Structural Health Monitoring II
PB - Trans Tech Publications Ltd
T2 - 2nd International Conference on Smart Diagnostics of Structures
Y2 - 14 November 2011 through 16 November 2011
ER -