Bearing Fault Diagnosis Approach Research Articles

An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model

Bearing fault vibration signals collected in real engineering cases often contain environmental noise which can easily mask the fault type characteristics of vibration signals, making it difficult to determine the corresponding fault type when traditional deep learning methods are used for fault diagnosis. To solve the above problem, a neural network model named multiscale CNN-LSTM (convolutional neural network-long short-term memory) and a deep residual learning model was designed, which combines a multiscale wide CNN-LSTM module and a deep residual module for rolling bearing fault diagnosis. In this model, a wide convolution kernel CNN-LSTM structure with different convolution scales is used to extract a variety of different types of frequency and sequential features from vibration signals. It is worth noting that the wide convolution kernel CNN-LSTM structure not only has stronger feature extraction performance compared with the common convolution layer but can also reduce the interference of high-frequency noise. Moreover, the deep residual module with a wide convolution kernel CNN-LSTM structure is used to further improve the feature expression ability of the proposed model. The above algorithm enables the proposed model to better extract the fault features hidden in the noise signal. When compared with some state-of-the-art methods, the experimental results showed that this model has better anti-noise performance and better generalization ability for rolling bearing fault diagnosis.

An intelligent self-adaptive bearing fault diagnosis approach based on improved local mean decomposition

An intelligent self-adaptive bearing fault diagnosis approach based on improved local mean decomposition

The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.

Novel Fault Diagnosis Approach for Rolling-element Bearings Based on Bispectral Analysis

Novel Fault Diagnosis Approach for Rolling-element Bearings Based on Bispectral Analysis

Bearing Fault Diagnosis Under Small Data Set Condition: A Bayesian Network Method With Transfer Learning for Parameter Estimation

Bearings are broadly applied in various types of industrial systems. Fault diagnosis, as a promising way for reliability of modern industrial internet of thing applications, has attracted increasing attention from both academia and industry fields. Being ideal modeling and inference tool in uncertainty situations, Bayesian network (BN) is becoming increasingly popular in many systems. However, in practical uncertain and complicated engineering surroundings, it’s difficult or expensive to collect massive labeled fault data for the sake of fault diagnosis model learning. To address the issue of BN parameter learning under small data set conditions, this paper proposes a Varying Coefficient Transfer Learning (VCTL) algorithm based on aggregation and transfer learning, that considers both knowledge from the resource domain and the resource relevance contributions. The balancing weight function is designed to determine whether the learning task in the resource domain is activated. Relevance weight factors are proposed to measure the relevance of resource and target parameters quantitatively, by combing parameter information from resource domains with those obtained from the target domain, using maximum a posterior (MAP) or maximum likelihood estimation (MLE). Finally, the target parameters are aggregated with both the target initial parameters and the parameter knowledge from the resource domain. Based on VCTL, a bearing fault diagnosis approach is proposed and verified. The experimental results show that, under the condition of the small data set, learning accuracy of VCTL algorithm with varying coefficient aggregation is better than MLE algorithm, MAP algorithm or state-of-the-art parameter transfer method, local linear pooling transfer learning (LoLP) algorithm. Under the condition of sufficient data set, learning accuracy of VCTL algorithm approaches the classical MLE or MAP, and the correctness of the proposed algorithm is verified. Moreover, we illustrate the successful application to real-world bearing fault diagnosis case with VCTL, where we had access to expert-provided resource knowledge and real fault diagnosis data.

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings.

We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

Bearing Fault Diagnosis Approach under Data Quality Issues

In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the fractal characteristics of non-stationary vibration signals collected from bearings under different health conditions. Using the detrended fluctuation analysis (DFA), we proposed a novel method to diagnose the bearing faults based on the scaling exponent (α1) of vibration signal at the short-time scale. In vibration data with high sampling rate, our results showed that the proposed measure, scaling exponent, provides an accurate identification of the health state of the bearing. At the end, we evaluated the performance of the proposed method under different data quality issues, data loss and induced noise.

An optimized adaptive PReLU-DBN for rolling element bearing fault diagnosis

Rolling element bearings are critical components in industrial rotating machines. Faults and failures of bearings can cause degradation of machine performance or even a catastrophe. Therefore, it is significant to perform bearing fault diagnosis accurately and effectively. Deep Learning based approaches are promising for bearing diagnosis. They can extract fault information efficiently and conduct accurate diagnosis. However, the structure of deep learning networks is often determined by trial and error, which is time consuming and lacks theoretical guidance. Besides, the traditional deep learning approaches have low diagnosis accuracy and learning efficiency. To address these problems, this paper proposes a rolling element bearing fault diagnosis approach based on principal component analysis and adaptive deep belief network with Parametric Rectified Linear Unit activation layers. In the proposed approach, particle swarm optimization is integrated to obtain an optimal DBN structure with high accuracy and convergence rate. Experiments on tapered roller bearings and comparison studies with state-of-the-art methods are conducted to demonstrate the effectiveness and accuracy of the proposed approach.

A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method

Obtaining the sensitive feature vectors from the vibration signal is crucial to indicate the bearing’s actual condition. Most often, weak feature vectors are the consequence of heavy noise in the original signal and the incompatibility of the methods to deal with the nonlinear and non-stationary nature of vibration signals. In this paper, the first issue is addressed by employing a complementary ensemble empirical mode decomposition method. A new method based on improved multiscale permutation entropy method and dominant statistical parameters is proposed to deal with the second issue. The proposed approach’s denoising capability is first verified on an amplitude modulated and frequency modulated simulated signal. On the experimental front, the proposed method is investigated under a wide range of operating conditions to simultaneously recognize bearing fault type and severity. Since the experimental investigation includes identifying dominant statistical parameters and classifying different bearing faults, a recent method named XGBoost is explored comprehensively. The results show that the classification accuracy with features extracted by the proposed method exceeds 3% to 18% compared to features extracted by other state-of-the-art permutation-based feature extraction methods.

Pseudo fourth-order moment based bearing fault feature reconstruction and diagnosis

In this paper, a novel bearing fault diagnosis approach based on pseudo fourth-order moment is proposed and verified. Based on the wavelet decomposition of different vibration signals, the pseudo fourth-order moment is calculated, and a new fault feature—the feature angle is proposed. Feature angle between these pseudo fourth-order moments is obtained, and corresponding working condition maybe characterized by this feature angle. The ranges of these feature angles are determined by simulation experiments with a large amount of data. An assessment index (namely selection index of optimal data size) is constructed to select the optimal amount of computational data. Meanwhile, extreme learning machine (ELM) model is established to classify different working conditions. According to the ELM model obtained from training data, the accuracy of test data classification reaches 95.42%, which proves the effectiveness of present bearing fault diagnosis method.

Bearing Fault Diagnosis Method Based on Ensemble Composite Multi-Scale Dispersion Entropy and Density Peaks Clustering

For bearing fault diagnosis, how to effectively extract informative fault information and accurately diagnose faults is still a key problem. To this end, this study proposes a novel bearing fault diagnosis approach based on ensemble composite multi-scale dispersion entropy (ECMDE), local preserving projections and density peaks clustering. Specifically, ECMDEs are developed to capture multi-scale fault features from the raw vibration signals. The goal of ECMDEs is to synthesize different kinds of composite multi-scale dispersion entropies to find more effective fault information. Subsequently, the local preserving projections method is utilized to reduce high-dimensional feature set and extract the effective fault information. Finally, the reduced features are fed into the density peaks clustering method to obtain the fault diagnosis results. Two experimental cases and extensive comparisons are applied to validate the effectiveness and noise robustness of the proposed method. Experimental results demonstrate that the proposed method is capable to reliably extract effective fault information of raw vibration signals and accurately diagnose bearing faults even under low signal-to-noise ratio conditions.

A new transferable bearing fault diagnosis approach with adaptive manifold embedded distribution alignment

The conditional and marginal distributions of bearing vibration signal data collected under different working conditions generally may not obey the same distribution. Moreover, feature distortions are difficult to eliminate when performing distribution alignment in the original space. Based on these challenges, this study proposes a novel transferable fault diagnosis approach with adaptive manifold embedded distribution alignment (AMEDA). AMEDA can learn transformed feature representations in the Grassmann manifold space by constructing a geodesic flow kernel to avoid feature distortions. In addition, this paper initially aims to construct an adaptive factor defined by -distance to adjust the relative importance of the conditional and marginal distributions dynamically. After manifold feature learning and dynamic distribution alignment, a cross-domain classifier f with structural risk minimization can be learned to predict unlabeled target domain data. Experimental results from multiple indicators based on two datasets demonstrate the superiority of AMEDA over the other existing methods.

Unsupervised deep transfer learning with moment matching: A new intelligent fault diagnosis approach for bearings

Deep learning has redefined state-of-the-art performances in the research of intelligent fault diagnosis, however, most studies assumed that the training and testing data have the same distributions. In this paper, an unsupervised deep transfer network with moment matching (UDTN-MM) is proposed, aiming to realize fault diagnosis under different working conditions. Grayscale time-frequency images are utilized as the network input, and two adaptive methods are employed to reduce the distribution discrepancy. First, a deep transfer network is developed to extract transferrable features of the images. Then, two regularization terms expressed by moment matching, i.e., marginal distribution adaptation and statistical feature transformation, are designed to guarantee accurate distribution matching and domain adaptation. To prove the superiority of proposed moment matching method, two network structures with different bottlenecks are constructed. The results of case studies show that the approach is competitive on unlabeled samples in terms of diverse rotating speeds and fault severities.

On the Accuracy of Fault Diagnosis for Rolling Element Bearings Using Improved DFA and Multi-Sensor Data Fusion Method.

Rolling element bearings are widely employed in almost every rotating machine. The health status of bearings plays an important role in the reliability of rotating machines. This paper deals with the principle and application of an effective multi-sensor data fusion fault diagnosis approach for rolling element bearings. In particular, two single-axis accelerometers are employed to improve classification accuracy. By applying the improved detrended fluctuation analysis (IDFA), the corresponding fluctuations detrended by the local fit of vibration signals are evaluated. Then the polynomial fitting coefficients of the fluctuation function are selected as the fault features. A multi-sensor data fusion classification method based on linear discriminant analysis (LDA) is presented in the feature classification process. The faults that occurred in the inner race, cage, and outer race are considered in the paper. The experimental results show that the classification accuracy of the proposed diagnosis method can reach 100%.

Coordinated approach fusing time-shift multiscale dispersion entropy and vibrational Harris hawks optimization-based SVM for fault diagnosis of rolling bearing

To fully mine the effective fault information and improve the fault diagnosis accuracy, a novel fault diagnosis approach for rolling bearings is proposed by integrating variational mode decomposition (VMD), time-shift multiscale dispersion entropy (TSMDE) and support vector machine (SVM) optimized by vibrational Harris hawks optimization algorithm (VHHO). Firstly, vibration signals with different fault types are decomposed into several intrinsic mode functions (IMFs) by VMD. Subsequently, the proposed TSMDE aggregating time-shift procedure and dispersion entropy is employed to extract multiscale fault features from IMFs. Afterwards, the proposed VHHO that adopts a periodic mutation mechanism to enhance the original Harris hawks optimization (HHO) is devoted to search the optimal parameters of SVM, with which different faults are recognized. Finally, simulations and applications are conducted to evaluate the proposed coordinated VMD-TSMDE-VHHO-SVM approach, and the results reveal that the proposed approach can achieve better diagnosis performance than other comparative ones.

Kurtosis Based Empirical Mode Decomposition for Rolling Bearing Fault Detection

A bearing fault diagnosis approach based on spectral kurtosis and empirical mode decomposition (EMD) is proposed. EMD is a signal decomposition technique, which can adaptively separate a number of intrinsic mode functions (IMFs) from the vibration signal according to the architectural characteristics of the data. The spectral kurtosis parameter takes as signal impulsive indicator. Firstly, EMD is utilized to process the sampling vibration signal. And then spectral kurtosis is calculated to select the optimal intrinsic mode functions, so as to suppress the noise and highlight the transient impact feature. Finally, the envelope spectrum is computed and the fault characteristic is recognized. The experimental results show that the proposed approach can identify bearing defects effectively and provide a reliable method for gearbox fault monitoring and diagnosis.

Weak fault diagnosis of rolling bearings based on singular spectrum decomposition, optimal Lucy–Richardson deconvolution and speed transform

Singular spectrum decomposition (SSD) has attracted considerable attention in the field of fault diagnosis in rotating machinery. However, few investigations have been carried out on its performance in processing the signals, which are composed of sub-components with time-varying frequencies. This paper attempts to introduce SSD to process the fault vibration signals of a rolling bearing under variable speed conditions. Moreover, SSD is combined with the optimal Lucy–Richardson deconvolution (OLRD) method and speed transform (ST) to construct a novel weak fault diagnosis approach for bearings under variable speed operation. Specifically, SSD is firstly introduced to separate the fault feature signal from the original signal of the bearing. An OLRD method based on Shannon entropy is then applied to the fault feature signal to further inhibit the interference and demodulate the fault information. Finally, ST is used to recognize the fault characteristic order (FCO) of the output signal of the OLRD, and the fault type is determined by comparing the recognized FCO and the theoretical FCOs of different components of the bearing. The validity of SSD for decomposing multi-component signals with time-varying frequencies is studied using a simulation signal, and the results imply that SSD is more accurate at extracting the sub-components compared to the existing algorithms, such as empirical mode decomposition and variational mode decomposition (VMD). The proposed method demonstrates good performances in detecting the fault signatures of simulated and experimental fault signals of rolling bearings under variable speed operation.

A Multi-scale Fuzzy Measure Entropy and Infinite Feature Selection Based Approach for Rolling Bearing Fault Diagnosis

Fuzzy measure entropy (FuzzyMEn) is a recently improved non-linear dynamic parameter for evaluating the signals’ complexity. In comparison with fuzzy entropy (FuzzyEn), which only emphasizes the local characteristics of the signal but neglects its global trend, FuzzyMEn can reflect not only the local but also the global characteristics of the signal. Therefore, by calculating the FuzzyMEn values in different scales, the multi-scale fuzzy measure entropy (MFME) method is put forward in this paper and used for extracting the fault features from vibration signals of rolling bearing. After the feature extraction, the newly developed infinite feature selection (Inf-FS) method is employed to choose the most representative features from the original ones of high dimension. Finally, a new rolling bearing fault diagnosis approach is presented based on MFME, Inf-FS and support vector machine (SVM). The experimental analysis indicates that the presented approach can realize the rolling bearing fault diagnosis effectively.

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed in external sensors, the VS-based approach is impracticable. Otherwise, motor current signals (CSs) are easily measured by the inverters that are the available components of those systems. Therefore, the motor CS-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the VS-based approach, especially in the case of fault diagnosis for external bearings (the bearings that are installed outside of the electric motors). Accordingly, this article proposes a motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, and the features are extracted from the CSs of each phase. Then, each feature set is classified separately by a convolutional neural network (CNN). To enhance the classification accuracy, a novel decision-level IF technique is introduced to fuse information from all of the utilized CNNs. The problem of decision-level IF is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.

Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.