Fault Diagnosis Method Of Rolling Research Articles

A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings

In modern manufacturing processes, requirements for automatic fault diagnosis have been growing increasingly as it plays a vitally important role in the reliability and safety of industrial facilities. Rolling bearing systems represent a critical part in most of the industrial applications. In view of the strong environmental noise in the working environment of rolling bearing, its vibration signals have nonstationary and nonlinear characteristics, and those features are difficult to be extracted. In this article, we proposed a new intelligent fault diagnosis method for rolling bearing with unlabeled data by using the convolutional neural network (CNN) and fuzzy $C$ -means (FCM) clustering algorithm. CNN is first utilized to automatically extract features from rolling bearing vibration signals. Then, the principal component analysis (PCA) technique is used to reduce the dimension of the extracted features, and the first two principal components are selected as the fault feature vectors. Finally, the FCM algorithm is introduced to cluster those rolling bearing data in the derived feature space and identify the different fault types of rolling bearing. The results indicate that the newly proposed fault diagnosis method can achieve higher accuracy than other existing results in the literature.

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness.

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

PurposeThe purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.Design/methodology/approachThe vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.FindingsThe effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.Originality/valueThe combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

Multi-label fault diagnosis of rolling bearing based on meta-learning

In practical applications, it is difficult to acquire sufficient fault samples for training deep learning fault diagnosis model of rolling bearing. Aiming at the few-shot issue and multi-label attributes of single-point faults, a novel fault diagnosis method of rolling bearing based on time–frequency signature matrix (T–FSM) feature and multi-label convolutional neural network with meta-learning (MLCML) is proposed in this paper. At the beginning, the T–FSM features sensitive to few-shot fault diagnosis of measured vibration signal are extracted. Subsequently, a designed multi-label convolutional neural network (MLCNN) with a specific architecture is employed to identify faults. Crucially, the meta-learning strategy of learning initial network parameters susceptive to task changes is incorporated to MLCNN for addressing the few-shot problem. Ultimately, the publicly available rolling bearing dataset is utilized to demonstrate the effectiveness of the proposed method. The experimental results exhibit that the trained MLCML has the capability of learning to learn few-shot fault attributes with outstanding diagnosis accuracy and generalization. More concretely, the model can adapt to new fault categories rapidly owing to that only a few samples and update steps are required to fine-tune the network.

A fault diagnosis method of rolling bearing based on VMD Tsallis entropy and FCM clustering

A new fault diagnosis method of rolling bearings was presented based on variational mode decomposition (VMD), Tsallis entropy and Fuzzy C-means clustering (FCM) algorithm. Firstly, the measured vibration signals were decomposed with VMD in different scales to obtain a series of band-limited intrinsic modal function (BIMF). The VMD parameters were determined according to the change of the BIMF center frequency. Then, the Tsallis entropy of BIMF components were calculated and used as the signal features. Finally, the features were put into FCM classifier to recognize different fault types. It is proved by experiments that this method is feasible and the proposed approach could obtain better result compared with the method based on mode decomposition (EMD) and local mean decomposition (LMD).

Fault diagnosis method of rolling bearing based on the multiple features of LMD and random forest

The occurrence of rolling bearing fault not only affects the operation efficiency of mechanical equipment, but also leads to the safety accidents. The research on fault diagnosis of rolling bearing is of great practical significance to improve the reliability of mechanical equipment, avoid unexpected faults and ensure safety in production. In this paper, a fault diagnosis method of rolling bearing based on the multiple features of local mean decomposition (LMD) and random forest is proposed. It is suitable for processing non-stationary vibration signals generated by rolling bearings and adaptively decomposing the characteristic information excited by faults into a series of product function (PF). Then, the mixed feature vectors of each PF are constructed from different angles to realize the extraction and quantification of the rolling bearing fault feature information. Finally, combined with the random forest algorithm, accurate identification of the type of rolling bearing failure is achieved. The overall recognition rate reached 94.4%. It proves that the method proposed in this paper is effective.

An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks

In recent years, the technique of machine learning or deep learning has been employed in intelligent fault diagnosis methods to achieve much success using massive labeled data. However, it is generally difficult or expensive to label the monitoring data in practical engineering due to its complex working conditions. Therefore, an unsupervised fault diagnosis method is proposed in this paper for rolling bearings, which incorporates short-time Fourier transform (STFT) as well as categorical generative adversarial networks (CatGAN). The proposed method first adopts STFT to transform raw 1-D vibration signals into 2-D time-frequency maps to serve as the input of CatGAN. Then, it obtains a CatGAN model via an adversarial training process to generate fake samples with a similar distribution to the maps extracted by STFT and cluster the input samples into certain categories. Furthermore, the performance of the proposed ST-CatGAN method is verified using a classic rotating machinery dataset, and the experimental results demonstrate its high diagnosis accuracy and strong robustness against the motor load changes.

An Intelligent Fault Diagnosis Method of Rolling Bearing Under Variable Working Loads Using 1-D Stacked Dilated Convolutional Neural Network

Data-driven fault diagnosis is critical for the rolling bearing to improve its healthy states and save invaluable cost. Nowadays, various intelligent fault diagnosis methods using machine learning (ML) or deep learning (DL) techniques have achieved much success. The convolutional neural network (CNN) based method, as a representative DL technique, can extract the features of raw data automatically for its excellent sparse connectivity and weight sharing properties. In this paper, a novel data-driven intelligent fault diagnosis method of rolling bearing under variable working loads has been proposed by using 1-D stacked dilated convolutional neural network (1D-SDCNN). First, 1-D vibration signals were used as input data without additional signal processing and diagnostic expertise. Second, the stacked dilated convolution, which can capture larger scale associated information and achieve large receptive fields with a few layers, was used to replace the traditional convolution and pooling structure. Third, the 1D-SDCNN architecture was flexible which is based on the relationship between receptive fields and the length of the input signal. And the number of network layers can be adjusted according to signal length. Further, it can adapt to the changing working loads of the mechanical environment. Finally, the effectiveness of the proposed method was confirmed through the experiment. And the results demonstrated that 1D-SDCNN was able to learn in-deep features under three variable working loads and the average accuracy was 96.8%.

Intelligent Fault Diagnosis of Rolling Bearing Using FCM Clustering of EMD-PWVD Vibration Images

Rolling bearing is key component of rotating machinery and its fault diagnosis is of great significance for reliable operation of machine. In this paper, an intelligent fault diagnosis method of rolling bearing based on FCM clustering of vibration images obtained by EMD-PWVD is presented. Firstly, vibration signals with different fault degrees are transformed into contour time-frequency images by EMD-PWVD. Secondly, vibration images are divided into sections and their energy distribution values are used as image feature. Then, feature vectors are constructed for known signals, which are standardized as inputs of FCM clustering to obtain classification matrix and clustering center. Finally, proximity between tested samples and clustering centers of known samples are calculated to realize identification of bearing faults. Experimental results show that identification accuracy of this proposed method is high. When adding noise, the proposed method is more stable than other vibration images such as grayscale and symmetrical polar coordinate image, and when the added noise with SNR of 5, the reduction rate of identification accuracy is obviously smaller than those of other two methods.

A novel faults detection method for rolling bearing based on RCMDE and ISVM

The rolling bearing is an essential element widely used in the rotating machinery. Bearing failures are among the main reasons for breakdown of rotating machinery. Therefore, fault detection of bearing is necessary to reduce the probability of breakdown and safety accidents. A novel fault diagnosis method for rolling bearing based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Improved Support Vector Machine (ISVM) is presented in this paper. The RCMDE is a new irregular index in biomedical signal analysis, which has lower computational cost and more stable results. Therefore, the RCMDE is introduced as fault feature to represent the bearing fault characteristics. After feature extraction, an improved support vector machine based on whale optimization algorithm (WOA) and support vector machine (SVM) is proposed as a fault classifier, which has the advantages of less training samples and good classification effect. The effectiveness of the proposed method in bearing fault diagnosis is verified by using bearing fault experimental data.

Rolling bearing fault diagnosis based on EEMD sample entropy and PNN

A fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) sample entropy and probabilistic neural network (PNN) is proposed for non-steady and non-linear signals. First, the rolling bearing signals are decomposed into intrinsic mode function (IMF) using EEMD. Then, the kurtosis of each component is calculated. Five components with large kurtosis are selected and the sample entropy is extracted to form the feature vectors. Finally, the feature vectors are input to the PNN for fault diagnosis. The method is used to classify the type of the rolling bearing fault. The results show that the accuracy of fault diagnosis of the proposed method is 100%, which proves the effectiveness of the proposed method.

A fault diagnosis method of bearing using energy spread spectrum and genetic algorithm

Considering the shortcomings of the traditional energy spectrum algorithm applied to the rolling bearing fault diagnosis, which can only represent the tendency of fault feature transformation with a certain scale, but not adjacent scales contained. In this paper, we propose a fault diagnosis method of rolling bearing based on Support Vector Machine, combining energy spread spectrum and genetic optimization. The extracted signal is denoised and decomposed using wavelet packets, the energy spectrums and energy spread spectrums are calculated based on the decomposed different frequency signal components. The genetic algorithm is used to select the important parameters of the Support Vector Machine and bring the determined parameter values into the Support Vector Machine to generate the GA-SVM model. Then, energy spectrums and energy spread spectrums are inputted into GA-SVM as the characteristic parameters for identification. The experimental results show the two new points of energy spread spectrums and GA-SVM improve the diagnostic rate by up to 28.5 %, it can effectively improve the fault recognition rate of the rolling bearing.

Early fault diagnosis of rolling bearing based on noise-assisted signal feature enhancement and stochastic resonance for intelligent manufacturing

It is of great significance for intelligent manufacturing to study condition monitoring and diagnosis methods to realize early diagnosis of mechanical equipment faults. An early fault diagnosis method for rolling bearing based on noise-assisted signal feature enhancement and stochastic resonance was proposed. The advantage of this algorithm is that it avoids the shortage of useful information filtering in traditional noise reduction methods. The generalized multi-scale permutation entropy screening criterion was used to screen the vibration signal, and the parameters of the Duffing vibration subsystem were optimized by the particle swarm optimization algorithm to achieve the optimal matching among the Duffing vibration subsystem, the input signal, and noise, thereby improving the stochastic resonance effect. Part of the background noise energy is transferred to the early weak fault signal feature of the rolling bearing, which enhances the feature of the early weak fault signal. The proposed method was applied to the early fault diagnosis of rolling bearing life state and compared with the adaptive morphology method based on variational mode decomposition (VMD). The results show the effectiveness and feasibility of the proposed method.

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Rolling bearing is of great importance in modern industrial products, the failure of which may result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is significant and necessary and can enhance the reliability and efficiency of mechanical systems. Therefore, a novel fault diagnosis method for rolling bearing based on semi-supervised clustering and support vector data description (SVDD) with adaptive parameter optimization and improved decision strategy is proposed in this study. First, variational mode decomposition (VMD) was applied to decompose the vibration signals into sets of intrinsic mode functions (IMFs), where the decomposing mode number K was determined by the central frequency observation method. Next, fuzzy entropy (FuzzyEn) values of all IMFs were calculated to construct the feature vectors of different types of faults. Later, training samples were clustered with semi-supervised fuzzy C-means clustering (SSFCM) for fully exploiting the information inside samples, whereupon a small number of labeled samples were able to provide sufficient data distribution information for subsequent SVDD algorithms and improve its recognition ability. Afterwards, SVDD with improved decision strategy (ID-SVDD) that combined with k-nearest neighbor was proposed to establish diagnostic model. Simultaneously, the optimal parameters C and σ for ID-SVDD were searched by the newly proposed sine cosine algorithm improved with adaptive updating strategy (ASCA). Finally, the proposed diagnosis method was applied for engineering application as well as contrastive analysis. The obtained results reveal that the proposed method exhibits the best performance in all evaluation metrics and has advantages over other comparison methods in both precision and stability.

Fault diagnosis method of rolling bearing using principal component analysis and support vector machine

To effectively extract the fault feature information of rolling bearings and improve the performance of fault diagnosis, a fault diagnosis method based on principal component analysis and support vector machine was presented, and the rolling bearings signals with different fault states were collected. To address the limitation on effectively dealing with the raw vibration signals by the traditional signal processing technology based on Fourier transform, wavelet packet decomposition was employed to extract the features of bearing faults such as outer ring flaking, inner ring flaking, roller flaking and normal condition. Compared with the previous literature on fault diagnosis using principal component analysis (PCA) and support vector machine (SVM), one-to-one and one-to-many algorithms were taken into account. Additionally, the effect of four kernel functions, such as liner kernel function, polynomial kernel function, radial basis function and hyperbolic tangent kernel function, on the performance of SVM classifier was investigated, and the optimal hype-parameters of SVM classifier model were determined by genetic algorithm optimization. PCA was employed for dimension reduction, so as to reduce the computational complexity. The principal components that reached more than 95 % cumulative contribution rate were extracted by PCA and were input into SVM and BP neural network classifiers for identification. Results show that the fault feature dimensionality of the rolling bearing is reduced from 8-dimensions to 5-dimensions, which can still characterize the bearing status effectively, and the computational complexity is reduced as well. Compared with the raw feature set, PCA has a higher fault diagnosis accuracy (more than 97 %), and a shorter diagnosis time relatively. To better verify the superiority of the proposed method, SVM classification results were compared with the results of BP neural network. It is concluded that SVM classifier achieved a better performance than BP neural network classifier in terms of the classification accuracy and time-cost.

Fault diagnosis method of rolling bearing based on deep belief network

A method based on the theory of deep learning and feature extraction and a fault diagnosis model of a rolling bearing based on deep belief network are proposed in this study considering the complex, nonlinear, and non-stationary vibration signal of the rolling bearing. To some extent, the method avoids the complex structure of deep neural network and can be easily trained. Experimental results show that the recognition rate of the method reaches 100 %. The method can identify various types of faults accurately and has good fault diagnosis capability, which can provide the convenience for maintenance.

Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

This paper presents a fault diagnosis method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing vibration data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing fault diagnosis accuracy compared with traditional signal analysis methods.

Fault Diagnosis of Rolling Bearing Based on the PSO-SVM of the Mixed-Feature

The rolling bearing is one of the most important and widely used parts in the rotating machinery. It is necessary to establish a reliable condition monitoring program which can avoid serious fault in the runtime and diagnose failure timely and accurately when it happens. This paper puts forward to a fault diagnosis method of rolling bearing based on the PSO-SVM of the mixed-feature. Firstly, we extract features in time domain, frequency domain, and order quenfrency domain. Secondly, select both Support Vector Machine (SVM) parameters by Particle Swarm Optimization (PSO) algorithm and kernel function of SVM classification model. Finally, classification model of SVM is designed by using the extracted salient features, kernel function and optimal parameter of PSO. The result verifies the effectiveness of the proposed method.

Rolling Bearing Fault Diagnosis Based on Blind Source Separation

This document presents fault diagnosis method of rolling bearing based on blind source separation. The algorithm based on fast ICA is improved to separate fault signals according to the rolling bearing’s fault characteristics. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

Fault Diagnoise of Rolling Bearing Based on EEMD and Instantaneous Energy Density Spectrum

A new fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) and instantaneous energy density spectrum is proposed here. The intrinsic mode functions (IMFs) generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The characteristic frequencies were found in the instantaneous energy density of Hilbert spectrum. The effectiveness of this method was demonstrated by analysis the vibration signals of a rolling bearing with inner-race fault.