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Early bearing failure detection

Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic, resulting in costly downtime. One of the key issues in bearing prognostics is to detect the defect at its incipient stage and alert the operator before it develops into a catastrophic failure.

For the sensor-based method, signal de-noising and extraction of the weak signature are crucial to bearing prognostics since the inherent deficiency of the measuring mechanism often introduces a great amount of noise to the signal. In addition, the signature of a defective bearing is spread across a wide frequency band and, hence, can easily become masked by noise and low-frequency effects.

Normally, bearing vibration signals are collected with a vibration sensor installed on the bearing housing where the sensors are often subject to collecting active vibration sources from other mechanical components. The inherent deficiency of the measuring mechanism introduces a great amount of noise to the signal. Therefore, the signature of a defective bearing is spread across a wide frequency band and can easily become masked by noise and low-frequency effects. One of the challenges is to enhance the weak signature at the early stage of defect development. A signal-enhancing method is needed to provide more evident information for bearing performance assessment and prognostics.

The traditional approach for extracting signals from a noisy background is to design an appropriate filter, which removes the noise components and, at the same time, lets the desired signal go through unchanged. Based on noise type and application, different filters can be designed to conduct the de-noising. However, for a situation where the noise type and frequency range are unknown, the traditional filter design could become a computationally intense process.

The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability, which is discussed in detail later in this paper. While most of the signal de-noising approaches intend to detect smooth curves from the noisy raw signals, the vibration signal from mechanical failure, such as gears and bearings, are more impulse-like than smooth. Some researchers have developed a de-noising method based on Morlet wavelet analysis and applied the method to feature extraction of gear vibration signals. These methods seek the optimal wavelet filter that can give out the largest kurtosis value for the transformed signal. However, the defect signature of the bearing is periodic impulses. The periodicity plays an important role in fault identification and should not be ignored in optimal wavelet filter construction.

Another challenge of bearing prognostics is how to effectively evaluate the system performance based on the extracted features. One of the primary difficulties for effective implementation of bearing prognostics is the highly stochastic nature of defect growth. Even though a large variety of features can be extracted to describe the characteristics of signal from different aspects (such as root-mean-square [RMS], kurtosis, crest factor, cepstrum and envelope spectrum), previous work has shown that each feature is only effective for certain defects at certain stages. For example, spikiness of the vibration signals indicated by crest factor and kurtosis implies incipient defects, whereas the high energy level given by the value of RMS indicates severe defects. A good performance assessment method should take advantage of mutual information from multiple features and sensors for system degradation assessment.

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Author:mosbearing   2008-10-13   Source:    View(903)     Comment(0)

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