A tool for validating and benchmarking signal processing techniques applied to machine diagnosis

Buzzoni, Marco; D’Elia, Gianluca; Cocconcelli, Marco

doi:10.1016/j.ymssp.2020.106618

Cited by 19 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The numerical implementation of equation ( 1) in the defective REBS vibration model is based on the method proposed in [36]. The main problem in the modeling of local REBS defects is how to correctly define impact events under nonstationary conditions.…”

Section: Numerical Implementation Of the Defective Rebs Vibration Modelmentioning

confidence: 99%

A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis

Zhu

Dong

Pan

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

In recent years, more and more deep learning methods for fault diagnosis of rolling element bearings (REBS) have been proposed. However, in industry, the scarcity of available data to monitor the health condition of REBS leads to a low recognition accuracy of the trained intelligent diagnostic models. To solve this problem, we propose a simulation data driven subdomain adaption adversarial transfer learning network (SAATLN). Firstly, a defect vibration model is introduced to simulate vibration signals of different types of REBS faults. And the real signal and simulated signal are used as the target domain and source domain of the transfer learning fault diagnosis methods, respectively. Secondly, SAATLN uses the designed residual Squeeze-and-Excitation (Re-SE) blocks to extract transfer features between different domains. Meanwhile, it combines adversarial learning and subdomain adaptation to adapt the marginal distribution and conditional distribution discrepancy of high-level features. And the local maximum mean discrepancy (LMMD) is introduced as the subdomain adaptation metric criterion. Finally, different transfer tasks are performed on the artificially damaged and run-to-failure REBS data sets. The results demonstrate the effectiveness and superiority of the SAATLN in the simulation data driven REBS fault diagnosis.

show abstract

Section: Numerical Implementation Of the Defective Rebs Vibration Modelmentioning

confidence: 99%

A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis

Zhu

Dong

Pan

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In this section, a simulated vibration signal of a faulty rolling bearing is constructed to evaluate the efficiency of the proposed method. Considering a rolling bearing running with constant speed and assuming its inner race, outer race, or rollers have local damage, the excited vibration signal can be modeled as a series of periodic transient impulse features [ 38 , 39 ], and the vibration acceleration signal x ( t ) measured from the rolling bearing can be modeled as Equations (18) – (20):

where s(t ) is an ideal impulsive vibration signal with no noise; n ( t ) is white Gaussian noise; M is the number of the fault impulses induced by the local damage; A m is the amplitude of the m th fault impulse; a m is the amplitude modulation coefficient, where 0 < a m < 1; f r is the rotating frequency of the bearing; ζ is the structural damping coefficient; T p is the time period between two consecutive fault impulses, and T p = 1/ f c , in which f c represents the FCF of inner race, outer race, or balls; τ i ( i = 1, 2, …, M ) represents the effect of random slippage of the balls and can be assumed to be a zero mean, uniformly distributed random sequence with a standard deviation of 0.01 T p ~0.02 T p ; ω r is the excited resonance frequency; and u ( t ) represents the unit step function.…”

Section: Simulation Analysismentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Guo

Yang

Jiang

et al. 2022

Sensors

View full text Add to dashboard Cite

Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

show abstract

“…It is a basic task to extract impulsive sources from noisy observation signals in the field of fault diagnosis for rolling bearing health monitoring [ 1 , 2 , 3 , 4 ]. Where there are localized faults on the elements of rolling bearings, impulses are generated sequentially and periodic structures are also embedded into the impulses due to rotational movement [ 5 , 6 ]. After that, periodic transient impulses propagate along the complex transfer function of the transmission path to the position of the vibration sensors; observation signals are captured by sampling equipment.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-D-Norm-Driven Sparse Unfolding Deconvolutional Network for Bearing Fault Diagnosis

Lin,

Zhang,

et al. 2024

Sensors

View full text Add to dashboard Cite

Impulsive blind deconvolution (IBD) is a popular method to recover impulsive sources for bearing fault diagnosis. Its underpinnings are in the design of objective functions based on prior knowledge of impulsive sources and a transfer function to describe transmission path influences. However, popular objective functions cannot retain waveform impulsiveness and periodicity cyclostationarity simultaneously, and the single convolution operation of IBD methods is insufficient to describe transmission paths composed of multiple linear and nonlinear units. Inspired by the MaxPooling period modulation intensity (MPMI) and convolutional sparse learning (CSL), an adaptive multi-D-norm-driven sparse unfolding deconvolution network (AMD-SUDN) is proposed in this paper. The core strategy is that one target vector with simultaneous impulsiveness and cyclostationarity is constructed automatically through the MPMI; then, this vector is substituted into the multi D-norm to design objective functions. Moreover, an iterative soft threshold algorithm (ISTA) for the CSL model is derived, and its iterative steps are unfolded into one deconvolution network. The algorithm’s performance and the hyperparameter configuration are investigated by a set of numerical simulations. Finally, the proposed AMD-SUDN is applied to detect the impulsive features of bearing faults. All comparative results verify that the proposed AMD-SUDN achieves a better deconvolution accuracy than state-of-the-art IBD methods.

show abstract

A tool for validating and benchmarking signal processing techniques applied to machine diagnosis

Cited by 19 publications

References 29 publications

A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis

A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

An Adaptive Multi-D-Norm-Driven Sparse Unfolding Deconvolutional Network for Bearing Fault Diagnosis

Contact Info

Product

Resources

About