Comparison of advanced signal-processing methods for roller bearing faults detection

Jacek Urbanek, Tomasz Barszcz, Tadeusz Uhl

Research output: Contribution to journalArticlepeer-review

Abstract

Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging. The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach. The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties.

Original languageEnglish (US)
Pages (from-to)715-726
Number of pages12
JournalMetrology and Measurement Systems
Volume19
Issue number4
DOIs
StatePublished - 2012
Externally publishedYes

Keywords

  • Condition monitoring
  • Fault detection
  • Rolling element bearings
  • Wind turbine

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation

Fingerprint Dive into the research topics of 'Comparison of advanced signal-processing methods for roller bearing faults detection'. Together they form a unique fingerprint.

Cite this