Unsupervised feature learning for rotating machinery

• A smart sensorized bearing can be described as a bearing with built-in sensors for condition monitoring. These bearings require new methods for the processing of the information coming from the sensors. Smart bearings are expected to become the central components in the condition monitoring sensor system of future rotating machines. Condition monitoring typically requires expert knowledge about the machine that is monitored, making it costly to adapt the methods to the different machines, environment and operational conditions. This approach deals with an unsupervised learning method that allows for automatic characterization of signals with repeating structure in the time domain. The method is sparse coding with dictionary learning. We present the information obtained from time domain and frequency domain techniques and describe the conditions required to make the fault diagnosis possible. In contrast, we describe how our approach can autonomously depict deviations from the normal state of operation of machine by monitoring a dictionary of atomic waveforms learned from a signal. We study the propagation over time of a learned dictionary when the vibration of a rotating machine is monitored in normal and faulty states of operation, and we find that the adaptation rates of some atomic waveforms change significantly when a fault occurs.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Annan teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies (hsv//eng)

Nyckelord

Industriell elektronik

Industrial Electronics

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