Rolling bearings are widely used in rotary machinery systems. The measured vibration signal of any part linked to rolling contains fault information when failure occurs, differing only by energy levels. Bearing will cause the other components, and therefore collected bearing signals mixed with parts noise. Using multiple sensors collect at different locations on machine obtain multivariate can avoid loss local information. Subsequently using empirical mode decomposition (multivariate EMD)...

Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, vibration decomposed into multiple components by Wiener filtering in Fourier domain, and center frequency each component updated as gravity mode’s power spectrum. Therefore, compact around pulsation has limited bandwidth. view situation that penalty parameter number affect effect algorithm, novel fault feature extraction based on combination particle swarm optimization (PSO) algorithm...

Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The condition bolt joints has a significant impact on safe reliable operation whole equipment. failure monitoring leads to severe accidents or even casualties. This paper proposes novel method using multivariate intrinsic multiscale entropy (MIME) analysis Lorentz signal-enhanced piezoelectric active sensing. signal is as excitation sensing expose nonlinear dynamical characteristics joints. Multivariate...

In practical engineering applications, the multivariate signal contains more fault feature information than single-channel signal. How to realize synchronous extraction of features from is great significance in diagnosis rotary machinery. Dynamic mode decomposition (DMD) has attracted much attention due its excellent dynamic ability. However, DMD lacks aliasing property when dealing with signal, which may lead loss critical information. Cater this problem, article proposed a (MDMD) algorithm...

Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, fault of rolling bearing can only be identified when it has developed to certain degree. At that moment, there is already not much time for maintenance, could cause serious damage This paper proposes novel approach health degradation monitoring early diagnosis based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)...

Rolling bearings are important components in mechanical, civil, and aerospace engineering. The practical working conditions of rolling complex; hence, fault diagnosis under various operating is very challenging. This paper proposes a novel approach to rotary machinery using phase space reconstruction (PSR) intrinsic mode functions (IMFs) neural network conditions. Complete ensemble empirical decomposition with adaptive noise (CEEMDAN) employed decompose vibration signal component into IMFs...

Abstract Very early bolt looseness monitoring has been a challenge in the field of structural health monitoring. The authors have conducted further study previous researches, with aim detecting very conditions. intrinsic features vibro-acoustic signals contain underlying dynamic characteristics denoting full range Correspondingly, this paper proposes novel ResNet-integrated approach based on feature extraction percussion sounds. percussion-caused sound were extracted by variational mode...

Abstract The planetary gearbox is a key transmission apparatus used to change speed and torque. gear one of the most failure-prone components in gearbox. Due complexity working environments, collected vibration signals contain lot noise interference; fault characteristic frequencies are usually submerged or even lost. Thus, feature extraction from signal beneficial subsequent diagnosis. As identification approach that has been increasingly popular field diagnosis, deep learning requires...

Abstract In real-world industrial applications, bearings are typically operated under variable speeds and loads depending on the production condition, which results in nonstationary vibration signals from bearings. Synchrosqueezing transform is a method that can effectively reflect change frequency with time, suitable for processing bearing signals. However, significant classification features difficult to extract time–frequency information when operation conditions such as speed load...

Monitoring and identifying the health condition of rolling bearings can reduce risk mechanical equipment failure. This paper proposes a novel intelligent diagnosis method bearings: First, vibration signals are decomposed into band-limited instinct mode functions (BLIMFs) by variational decomposition (VMD). Then, proposed high-dimensional common spatial pattern (hdCSP) filter is used to generate eigenvectors representing BLIMFs. Finally, random forests classifier classify obtain results. The...

Abstract Rolling bearings play a crucial role as components in mechanical equipment. Malfunctioning rolling can disrupt the normal operation of equipment and pose safety hazards. Traditional deep learning-based methods for diagnosing faults present several issues, such insufficient feature information fault samples, high model complexity low accuracy. To overcome these challenges, this paper introduces an intelligent approach bearing diagnosis using intrinsic extraction convolutional block...

Effective utilization of signals collected by distributed sensor networks is crucial for tracking degradation and forecasting the remaining useful life (RUL) rolling bearings. The phase space warping (PSW) algorithm constructs hierarchical dynamics to physically describe damage evolution. However, PSW unable handle multivariate signals. To enable synchronous in signals, proposed solution (MPSW) algorithm. First, are embedded reconstructed space. Second, local polynomial receives current...

Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In field fault diagnosis and condition assessment based on vibration signal analysis, denoising emerged as an important tool improve reliability result. A reassignment technique termed synchrosqueezing wavelet transform (SWT) obvious superiority in slow time-varying representation for applications. The SWT uses...

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis rolling bearings, vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors collect signals at different locations on machine obtain multivariate can remedy this problem. The adverse effect a power imbalance between various is inevitable, and unfavorable for processing. As useful, processing method, Adaptive-projection...

olling bearings are crucial components in mechanical, civil and aerospace engineering. The practical working conditions of rolling complex tough, hence fault diagnosis under varying operating is very challenging. This paper proposes a robust approach using multivariate intrinsic multiscale entropy analysis neural network conditions. proposed deals with signal collected from multi-sensor acquisition system to capture much dynamical characteristic information. Multivariate consists adaptive...

A complementary ensemble adaptive local iterative filtering (CEALIF) and enhanced maximum correlation kurtosis deconvolution (EMCKD) approach is proposed for weak fault signals in wind turbine bearings, which are easily concealed by strong background noise susceptible to intermittent interference. The (ALIF), as a novel nonstationary signal processing technique, can perform based on the itself characteristics. However, its mode mixing an annoying problem. To relieve this problem,...

Fault diagnosis of rolling bearings can be a serious challenge, as often work under complex conditions and their vibration signals are typically nonlinear nonstationary. This paper proposes novel approach to diagnosing faults based on variational mode decomposition (VMD) genetic algorithm-optimized wavelet threshold denoising. First, VMD was used decompose the faulty into series band-limited intrinsic functions (BLIMFs). During decomposition, parameters were selected by Kullback–Leibler...

Rolling bearings are crucial components in the fields of mechanical, civil, and aerospace engineering. They sometimes work under various operating conditions, which makes it harder to distinguish faults from normal signals. Nuisance attribute projection (NAP) is a technique that has been widely used audio image recognition eliminate interference information extracted feature space. In constructing weighted matrix NAP, setting value represents degree between vectors. The either taken into...