Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Wang, Yanxue; Xiang, Jiawei; Markert, Richard; Liang, Ming

doi:10.1016/j.ymssp.2015.04.039

Cited by 394 publications

(153 citation statements)

References 111 publications

(179 reference statements)

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“…First, signal e(n), which has been prewhitened previously, is decomposed according to Equation (13) to obtain the complex envelope signals c i k (n), which have different center frequencies and bandwidths. Then, the SK of each complex envelope signal is evaluated using Equation (14). Finally, the optimal envelope center frequency f c , bandwidth B w , and optimized complex envelope signal c o (n) are determined according to Equation (15).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Adaptive spectral kurtosis (ASK) was proposed to identify multiple faults in bearings and successfully extracted the features of multiple faults buried by strong noise components [13]. In 2016, Wang et al [14] summarized the researches on the application of SK to fault diagnosis and prediction for rotating machinery after the method had been proposed in 1983. At the same time, experts suggested that SK had reached its full potential in areas such as operational modal analysis and fault diagnosis of electronic machinery.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Jiang¹,

HU²,

Feng³

et al. 2017

Applied Sciences

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Abstract:Because both the inner and outer rings rotate, the intershaft bearings used in gas turbines do not have fixed bearing housings. As a result, the vibration of intershaft bearings cannot be measured directly. Therefore, a vibration signal can only be collected through indirect measurement. First, it must be transferred to adjacent bearings through the shafting. Then, it should be transferred by the elastic supports and complex structure of the thin-walled strut. The vibration signal is severely weakened during transmission under the influences of the transfer path. In the meantime, in the vibration of other components, a huge amount of noise is produced by the air flow, and the variable speeds of the inner and outer rings of the intershaft bearings make it harder to analyze the signal. Hence, it is very difficult to extract the vibration fault features of intershaft bearings. To deal with the variable speed of dual rotors, as well as the weak signal, a fault feature extraction scheme for the weak fault signals of intershaft bearings is proposed in this paper. This scheme is based on linear prediction, spectral kurtosis, and order tracking in the rotation speed difference domain. First, a prewhitening process, based on linear prediction, is applied to the fault signal of the intershaft bearings to eliminate the stationary component. Thus, the remaining components, including the impulse signal of faulty bearings and nonstationary noise, can retain the features of the vibrational bearings, in addition to reducing the noise. Second, the optimal center frequency and bandwidth of the band-pass filter, applied to resonant demodulation, are selected by spectral kurtosis. Subsequently, the enveloped signal containing the features of the faults found in the intershaft bearings is obtained by resonance demodulation. The quasi-stationary signal in the angle domain is acquired by the even angle resampling of the nonstationary envelope signal, as a result of the variable speed. The final order spectrum is obtained through a Fourier transform. Fault diagnosis can be conducted for the intershaft bearings by comparing this spectrum with the feature order of the bearing fault. Experiments were conducted to verify the validity of the proposed scheme.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Jiang¹,

HU²,

Feng³

et al. 2017

Applied Sciences

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“…The spectral kurtosis technique adopts the concept of kurtosis to capture the impulsiveness of a signal. It uses a combination of short-time Fourier transform-based SK, kurtogram, adaptive SK, and protrugram [42]. However, despite such specific situations, the wavelet transform has been the most popular denoising technique for the extraction of the defect vibratory signature from the measured signal in which the random noise and other parameters of the bearing are immersed [43][44][45].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Pino

Ribas

Guimarães

2018

Shock and Vibration

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The contribution of a medium-sized hydro power plant to the power grid can be either at base load or at peak load. When the latter is the most common operation mode, it increases the start and stop frequency, intensifying the hydro turbine components' degradation, such as the guide bearings. This happens due to more frequent operation in transient states, which means being outside the service point of the machines' nominal condition, consisting of speed, flow, and gross head. Such transient state operation increases the runner bearings' mechanical vibration. The readings are acquired during the runner start-ups and filtered by a DC component mean value and a wavelet packet transform. The filtered series are used to estimate the relationship between the maximum orbit curve displacement and the accumulated operating hours. The estimated equation associated with the ISO 7919-5 vibration standards establishes the sojourn times of the degradation states, sufficient to obtain the transition probability distribution. Thereafter, a triangular probability function is used to determine the observation probability distribution in each state. Both matrices are inputs required by a hidden Markov model aiming to simulate the equipment deterioration process, given a sequence of maximum orbit curve displacements.

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“…Compared with the classical approach, SK can automatically indicate the optimal frequency at which to perform amplitude envelope demodulation to obtain an envelope signal without requiring historical data or a priori knowledge. Thus, SK has become one of the powerful techniques for vibration signal analysis, especially for extracting periodic impulses induced by localized fault in rotating machine components, such as bearings [93][94][95][96] and gears [97,98].…”

Section: Spectral Kurtosismentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Cited by 394 publications

References 111 publications

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Basic research on machinery fault diagnostics: Past, present, and future trends

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