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Researchers have also modeled many practical machine tests, such as bearing fault diagnosis, gearbox fault detection, and rotating machinery identification. Yu and Berg [3] demonstrated a novel algorithm to correlate SVHN with histograms as an efficient and robust feature extraction scheme for the automated fault diagnosis of rotating machines. This algorithm is found to be robust to errors caused by impulsive noise. They proposed a machine fault activity signature based on the relative position of histograms as a new algorithm for fault diagnosis, and the achieved sensitivity and specificity reached 93.32% and 100% respectively. Yu et al. [82] extended the SVHN-based model to a new heterogeneous learning-based model for fault diagnosis of rotating machinery, and an accuracy of 93.00% in the absence of nonlinearity was achieved. The researchers modeled the gearbox test in [29] and achieved better results than the previous studies without including any hardware enhancements. A real-time testbed to acquire acceleration and vibration signals was used to normalize the obtained acceleration and vibration data by the learned model. They also accomplished a diagnosis accuracy of 95.97% for gearbox and electrical machines in normal conditions. Other related works can be found in [77, 78, 80] respectively.
Repetitive analysis is required to collect a significant amount of data, e.g., from driving cycles or various failure conditions on a rotating machine. The repetitive analysis process may take a long period of time to extract the needed feature parameters. This approach may also not provide a realistic representation of the real operation conditions. Many other research works that are discussed in this study have used the experimental data, and the results demonstrated the effectiveness of the methods used. The recognition rate of low-speed rotating machines was above 95 percent in [53] using frequency domain features; features extracted from the time signals were used to detect gearbox related faults in [63]; advanced fault symptoms were extracted from the time series in [57]; advanced fault symptoms were extracted from the time domain signals in [61]; and finally, the high frequency signal characteristics were used in the feature extraction process in [69]. For fault classification, a new algorithmic technique was proposed in [12] for the fault classification of electrical machines by combining the temporal and frequency domain features. d2c66b5586
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