Abstract
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches.
Highlights
Bearings are one of the most important components in rotating machinery [1]
Time domain features often involve statistical features that are sensitive to impulse faults [13], especially in the incipient stage, so we calculated some dimensional features, such as root mean squares (RMS), square root of the amplitude (SRA), kurtosis value (KV), skewness value (SV) and peak-peak value (PPV), in addition, some dimensionless features, such as crest factor (CF), impulse factor (IF), margin factor (MF), shape factor (SF) and kurtosis factor (KF)
Samples from each fault states, and 300 samples are collected for the test bearing, 50% of samples are used as the training set to construct K-means model, while the remaining 50% of samples are used as the testing set to test the classification accuracy rate of K-means using the first three extracted features corresponding to the largest eigenvalues
Summary
Bearings are one of the most important components in rotating machinery [1]. Many of the faults of rotating machinery relate to the bearings, whose running conditions directly affect the precision, reliability and life of the machine [2]. Bearings fault prognosis technology has received more and more attention, in fault feature extraction (FFE) of bearing accelerometer sensor signals has become more and more important in order to avoid the occurrence of accidents. Bearing accelerometer sensor signal analysis-based techniques, which are the most suitable and effective ones for bearing, have been extensively used since in machine prognosis it is easy to obtain sensor signals containing abundant information. These techniques mainly include three categories, namely, time domain analysis, frequency domain analysis and time-frequency domain analysis.
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