In order to solve the problem of random noise in rolling bearing vibration signals under complex working conditions, this paper use a symmetry VMD theory to set up a rolling bearing vibration signal noise reduction processing algorithm using the fusi...
Bearing vibration response studies are crucial for the condition monitoring of bearings and the quality inspection of rotating machinery systems. However, it is still very difficult to diagnose bearing faults, especially rolling element faults, due t...
Skidding damage mechanism of rolling bearings is not clear, due to the influence of various coupling factors. To solve this problem, it is important to identify and diagnose skidding damage and study the vibration characteristics in rolling bearings....
A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address t...
Since the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecti...
Permanent magnet synchronous motors (PMSMs) are widely used due to their unique advantages. Their transmission system mainly relies on rolling bearings; therefore, monitoring the motor’s working status and fault diagnosis for the rolling bearin...
Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted....
The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itsel...
In industrial applications, the operating state of bearings can directly affect the performance of mechanical equipment. Local defects, flexible raceways and lubrication have a great impact on the vibration characteristics of rolling bearings. Howeve...
Reliable fault diagnosis of rolling bearings is an important issue for the normal operation of many rotating machines. Information about the structure dynamics is always hidden in the vibration response of the bearings, and it is often very difficult...
The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is...
Rolling element bearings are commonly used in rotating machines. Bearing fault detection and diagnosis play a critical role in machine operations to recognize bearing faults at their early stage and prevent machine performance degradation, improve op...
The fault diagnosis classification method based on wavelet decomposition and weighted permutation entropy (WPE) by the extreme learning machine (ELM) is proposed to address the complexity and non-smoothness of rolling bearing vibration signals. The w...
To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improve...
The generation of synthetic vibration signals enables the testing of novel machine diagnostic methods without the costly introduction of real failures. One of major goals of vibration-based condition monitoring is the early detection of bearing fault...
Remaining useful life (RUL) prediction plays a core role in industrial prognostics and health management (PHM), requiring data-driven models with higher predictive capability for accurate long time series prediction. Developing reliable deep learning...
In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rol...
Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signa...
The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs...
To solve the problem of a low signal-to-noise ratio of fault signals and the difficulty in effectively and accurately identifying the fault state in the early stage of motor bearing fault occurrence, this paper proposes an early fault diagnosis metho...
In this paper, a comparison of signal analysis techniques for the diagnostics of rolling element bearings is carried out. Specifically, the comparison is performed in terms of fault detection, diagnosis and prognosis techniques with regards to the fi...
The extraction of rolling bearings’ degradation features has been developed for decades. However, the degradation features always present different trends of different run-to-failure data. To find a consistent indicator of different data will b...
In view of the rolling bearing fault signal non-stationarity, strong noise can lead to low fault diagnosis accuracy. A Swin Transformer and generalized S Transform fault diagnosis method is proposed to solve the problems of difficult signal feature e...
Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and f...
To address the computational redundancy, inadequate multi-scale feature capture, and poor noise robustness of traditional deep networks used for bearing vibration and acoustic signal feature extraction, this paper proposes a fault diagnosis method ba...
Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symm...
The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decom...
Aiming to solve the problem of accurate diagnosis of the size and location of rolling bearing faults, a novel quantitative and localization fault diagnosis method of the rolling bearing is proposed based on the quantitative mapping model (QMM). The f...
The opportunities of impedance-based condition monitoring for rolling bearings have been shown earlier by the authors: Changes in the impedance signal and the derived features enable the detection of pitting damages. Localizing and measuring the pitt...
An enhanced fault diagnosis approach for rolling bearings with composite faults using an optimized Squeeze and Excitation ResNet (SE-ResNet) model is proposed. This method integrates grid search (GS), support vector regression (SVR), ensemble empiric...
Due to the interference of various strong background signals, it is often difficult to extract effective features by using conventional methods such as envelope spectrum analysis when early weak fault arises in rolling bearing. Inspired by the curren...
In the fault monitoring of rolling bearings, there is always loud noise, leading to poor signal stationariness. How to accurately and efficiently identify the fault type of rolling bearings is a challenge. Based on multivariate multiscale sample entr...
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and...
It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance de...
A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity...
Rolling bearings are a vital component of mechanical equipment. It is crucial to implement rolling bearing fault diagnosis research to guarantee the stability of the long-term action of mechanical equipment. Conversion of rolling bearing vibration si...
The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an imp...
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and...
Considering the problem that the early fault signal of rolling bearing is easily interfered with by background information, such as noise, and it is difficult to extract fault features, a method of rolling bearing early fault diagnosis based on the t...
The rolling element bearing is a fundamental component of any rotating machinery. During operation, wear debris and lubricant impurities create dents and bumps on the bearing raceway surfaces. Such localized defects produce transient vibration impuls...
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibra...
Bearing condition monitoring and prognosis are crucial tasks for ensuring the proper operation of rotating machinery and mechanical systems. Vibration signal analysis is one of the most effective techniques for bearing condition monitoring and progno...
The operating environment of rolling bearings in mine hoists is complicated, and detecting their faults is hindered by a weak and unstable initial vibration signal. This directly affects the ability to extract pertinent fault features. This paper put...
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 informati...
Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller beari...
As rolling bearings are widely used in various machines, there is a strong need to detect any problems as early as possible. Although vibration analysis is commonly used in the diagnosis of rolling bearings, it is possible that the failure of such be...
In order to achieve accurate fault diagnosis of rolling bearings, a hierarchical decision fusion diagnosis method for rolling bearings is proposed. The hierarchical back propagation neural networks (BPNNs) architecture includes a fault detection laye...
The vibration signals collected by acceleration sensors are interspersed with noise interference, which increases the difficulty of fault diagnosis for rolling bearings. For this reason, a rolling bearing fault diagnosis method based on complete ense...
Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault...
As failures of rolling bearings lead to major failures in rotating machines, recent vibration-based rolling bearing fault diagnosis techniques are focused on obtaining useful fault features from the huge collection of raw data. However, too many feat...
of 8