Compressive Sensing: A New Insight to Condition Monitoring of Rotary Machinery

With the development of rotary machinery condition monitoring, challenges have often been encountered due to the cumbersome nature of data monitoring. Common methods in signal processing are primarily based on the Shannon sampling principle, which requires substantial amounts of data to achieve the desired accuracy from on-line monitoring signals. This limits their applications in cases for which only small samples can be collected, or cases for which too much data are generating which needs to be largely reduced with under-sampling. Using the Shannon sampling principle, it seems impossible to significantly reduce the quantity of data while preserving adequate useful information for condition monitoring. A newly developed theory termed compressive sensing provides a new insight to condition monitoring and fault diagnosis. It states that a signal can be perfectly recovered from under-sampled data, which means that useful condition information can still be represented by small samples. This study presents novel methods for rotary machinery fault detection from compressed vibration signals inspired by compressive sensing, which can largely reduce the data collection and detect faults of rotary machinery from only a few signal samples. This will greatly help reduce the amount of monitoring data while still guaranteeing a high accuracy of fault detection. Case studies related to roller bearing fault signals are also presented in this study to illustrate the effectiveness of the present strategy.