Cyclostationarity: New trends and applications

Advanced bearing diagnostics: A comparative study of two powerful approaches

Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data

Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference

An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds

Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine

Use of acoustic emissions (AE) has been shown to be of aid for bearing damage detection. Rolling element bearings with localized faults produce transient AE activity formed by bursts that repeat in an apparently periodic way. However, the produced signal is actually not periodic, but rather pure cyclostationary at the second order. Since AE are typically measured on the bearing housing, there is high attenuation of the waves, because of the dry metal–metal contact between the outer ring and the housing. In addition, AE instrumentation can be very sensitive to contamination by electromagnetic sources, such as frequency converters usually found near the measurement zones. Both situations combine and create adverse conditions for analysis. In this work a situation such as the described is presented. The measured AE is a corrupted signal where the valuable information is both weak and hidden. A filtering method by means of cyclostationary tools is presented and contrasted with other known methods. The proposed method is used subsequently for defect size estimation.

Damage Assessment of Rolling Element Bearing Using Cyclostationary Processing of AE Signals with Electromagnetic Interference

The use of vibrations and other variables to infere the mechanical condition of machines is a common practice nowadays. Several equipments for measurement and processing are available. Most of them offer FFT spectrum as the main tool for analysis, thus allowing the assessment of the stationary part of the signal only. Although this might be sufficient in some cases, it is certainly inappropriated in others. Recent advances in signal processing have opened the possibilities for analyzing a special type of non-stationary signals, called cyclostationary signals. It has also been shown that the behaviour of machines can be highly cyclostationary in some cases. Moreover, being stationarity a special case of cyclostarionarity, the advantages of the cyclostationary approach become evident. Still, even under consideration of these facts, the use of cyclostationarity appears to be still restricted to the scientific community, being its use in the industry far from being a reality. This chapter presents the concept of cyclostationarity, its terminology and its relation with traditional signal processing tools in a descriptive way. Two examples of real data analysis from the cyclostationary viewpoint are also presented.

David Quezada Acuña

Papers

Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine

Damage Assessment of Rolling Element Bearing Using Cyclostationary Processing of AE Signals with Electromagnetic Interference

Cyclostationary Processing of Vibration and Acoustic Emissions for Machine Failure Diagnosis