A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

Shao, Haidong; Lin, Jing; Zhang, Liangwei; Galar, Diego; Kumar, Uday

doi:10.1016/j.inffus.2021.03.008

Cited by 196 publications

(62 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The filter size L is generally in the range of [100,500]. M is generally in the range of [1,7], because the iterative method can reduce the numerical precision if we select a lager M. From experience, L is selected as 300, and M is selected as 5. For the simulation signal with the noise (SNR=-13.2dB), the MCKD is further used to process the reconstructed signal, the power spectrum of processed signal is shown in FIGURE 9(c).…”

Section:        mentioning

confidence: 99%

See 1 more Smart Citation

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

126

View full text Add to dashboard Cite

In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

show abstract

Section:        mentioning

confidence: 99%

“…As one of the core components in industrial production, rolling bearings are widely applied in various rotating machinery. Therefore, it is very necessary to accurately diagnose faults of rolling bearings [1][2][3]. Rolling bearing faults mainly occur in the inner race, outer race and rolling element [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

126

View full text Add to dashboard Cite

show abstract

“…When µ = 4, 0 ≤ Z ij ≤ 1, logistics mapping is completely chaotic. In order to avoid local optimization problems in the process of ACO, chaos disturbance was added to the process of pheromone adjustment so as to realize the whole optimization [59][60][61]. The pheromone adjustment of the CACO algorithm is described as follows:…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

View full text Add to dashboard Cite

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

show abstract

“…Sensors are the sensory organs of various mechanical and electronic devices. Without sensors to capture and convert the original information accurately and reliably, all measurement and control cannot be realized [32,33]. The sensor type is PCB 333B32 SN 25222.…”

Section: Experimental Verificationmentioning

confidence: 99%

An Adaptive Operational Modal Analysis Method Using Encoder LSTM with Random Decrement Technique

et al. 2021

View full text Add to dashboard Cite

A new parameter identification method under non-white noise excitation using transformer encoder and long short-term memory networks (LSTMs) is proposed in the paper. In this work, the random decrement technique (RDT) processing of the data is equivalent to eliminating the noise of the raw data. In general, the addition of the gate in LSTM allows the network to selectively store data, which avoids gradient disappearance and gradient explosion to a certain extent. It is worthwhile mentioning that the encoder can learn the essence of data, which reduces the burden for the LSTM. More specifically, establish as simple LSTM structure as possible to learn the data of this essence to achieve the best training effect. Finally, the proposed method is used for simulation and experimental verification, and the results show that the method has the advantages of high recognition accuracy, strong anti-noise ability, and fast convergence rate. Specially, the results indicated appropriate accuracy proposed by deep learning combined with traditional method for parameter identification as well as proper performance of the proposed method.

show abstract

A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

Cited by 196 publications

References 29 publications

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

An Adaptive Operational Modal Analysis Method Using Encoder LSTM with Random Decrement Technique

Contact Info

Product

Resources

About