Hilbert-Huang transform based methodology for bearing fault detection

Rotating machinery is of great importance for manufacturing industry, and therefore huge investments for their acquisition are made every year. Machine preservation plays an important role in the exploitation of this resource. Rotating machines are more susceptible to certain types of faults, investigations report that at least 42 % of the root causes of failure in rotating machinery are related with bearings. To detect the bearing condition many techniques have been developed. One of the most reliable is vibration analysis. The Hilbert-Huang transform (HHT) has been used for vibration analysis and has gained attention in recent years, a topic of controversy in this method is the selection of the Intrinsic Mode Functions (IMFs) with fault information. Statistical parameters can be used to describe the characteristics of vibration signals, this attribute can be exploited to select the IMFs. There are many time domain features used for signal analysis. In this research, a study of 17 statistical parameters was made to determine which one is the best to represent IMFs with fault information. As a result of this analysis a new methodology based on HHT is proposed. This methodology deals with the IMF selection with the use of KR (Kurtosis x RMS) to detect the IMFs with fault information, and can be used to detect incipient bearing faults. The proposed methodology was validated with 18 signals from the Case Western Reserve University (CWRU), Tian-Yau Wu, and the society for Machinery Failure Prevention Technology (MFPT Society) databases. For the 18 analyzed signals, only one IMF was wrongly selected. The cause of this error was the end defect produced in the EMD, this caused the KR amplitude to increase even tough the IMF did not have fault information. The results on the Envelope spectrum from 14 signals were clear with fault components with large amplitude. For the remaining four signals the results on the Envelope spectrum was noisy, but the fault fault components were distinguishable.Rotating machinery is of great importance for manufacturing industry, and therefore huge investments for their acquisition are made every year. Machine preservation plays an important role in the exploitation of this resource. Rotating machines are more susceptible to certain types of faults, investigations report that at least 42 % of the root causes of failure in rotating machinery are related with bearings. To detect the bearing condition many techniques have been developed. One of the most reliable is vibration analysis. The Hilbert-Huang transform (HHT) has been used for vibration analysis and has gained attention in recent years, a topic of controversy in this method is the selection of the Intrinsic Mode Functions (IMFs) with fault information. Statistical parameters can be used to describe the characteristics of vibration signals, this attribute can be exploited to select the IMFs. There are many time domain features used for signal analysis. In this research, a study of 17 statistical parameters was made to determine which one is the best to represent IMFs with fault information. As a result of this analysis a new methodology based on HHT is proposed. This methodology deals with the IMF selection with the use of KR (Kurtosis x RMS) to detect the IMFs with fault information, and can be used to detect incipient bearing faults. The proposed methodology was validated with 18 signals from the Case Western Reserve University (CWRU), Tian-Yau Wu, and the society for Machinery Failure Prevention Technology (MFPT Society) databases. For the 18 analyzed signals, only one IMF was wrongly selected. The cause of this error was the end defect produced in the EMD, this caused the KR amplitude to increase even tough the IMF did not have fault information. The results on the Envelope spectrum from 14 signals were clear with fault components with large amplitude. For the remaining four signals the results on the Envelope spectrum was noisy, but the fault fault components were distinguishable.