Abstract
Rotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the reliability and security of rotating machinery. The early defect occurring in the REB is too weak and manifests itself in heavy surrounding noise, thus leading to the inefficiency of the fault detection techniques. Aiming at the vibration signal purification and promoting the potential of defects detection, a new method is proposed in this paper based on the combination of singular value decomposition (SVD) technique and squared envelope spectrum (SES). The kurtosis of SES (KSES) is employed to select the optimal singular component (SC) obtained by applying SVD to vibration signal, which provides the information of the REB for fault diagnosis. Moreover, the rolling bearing accelerated life test with the bearing running from normal state to failure is adopted to evaluate the performance of the SVD‐KSES, and results demonstrate the proposed approach can detect the incipient faults from vibration signal in the natural degradation process.
Highlights
Rotating machinery has various applications in modern industries such as wind power, marine, and helicopter among which rolling element bearing is one of the most commonly used components to support the rotating parts
According to the surveys conducted by the electric power research institute, the rolling element bearing (REB)-related faults account for 40% failures in induction motors [1] and 64% of gearbox failures [2]. e occurrence of any REB defects, as well as the performance deterioration, affects the working performance of other parts and causing a deficiency of the entire machine, unscheduled shutdowns, economic loss, and even industrial casualties [3]. erefore, it becomes significant to implement effective techniques to monitor the condition of bearings. e in-time failure detection of bearing can ensure the reliability and security of the machinery and personnel security as well
During the early stage of fault development, the background vibration and noise are so strong and bury the fault-related impulses, which makes it difficult to carry out vibration-signal-based fault diagnosis. erefore, reliable signal processing methods are under high demand to extract and distinguish the fault features manifested in the raw signals with high accuracy promptly
Summary
Rotating machinery has various applications in modern industries such as wind power, marine, and helicopter among which rolling element bearing is one of the most commonly used components to support the rotating parts. By applying SVD to the instantaneous amplitude matrices, which is obtained by using Hilbert-Huang transform (HHT) to rolling bearing signals, singular value vectors were considered as the fault features [10]. Motivated by thoughts of protrugram and SE, SES infogram, the kurtosis of squared envelope spectrum amplitudes, is employed to evaluate the effect of each SC to the final one, which is expected to extract incipient fault from REB vibration signal. The raw vibrational signal of fault bearing is decomposed into several SCs by performing Hankel matrix SVD, and each SC in time series format is obtained as well. Hilbert-transform-based envelope analysis is one of the widely used approaches to present the fault characteristic frequencies in bearing fault diagnosis. The kurtosis of SES amplitudes outperforms kurtosis of temporal signal since the latter one is disturbed by noise and sensitive to random knocks [30]
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