Search

Early fault diagnosis is a hotspot and difficulty in the research of mechanical fault diagnosis. An early fault diagnosis method based on the orthogonal neighborhood preserving embedding and Adaboost_SVM algorithm for rolling bearing early fault diagnosis is proposed in this paper. Firstly, the vibration signals of rolling bearings are measured online. The correlation coefficients between the early fault indicators and the performance degradation are deeply analyzed based on the full-lifetime vibration data of rolling bearings so as to select the sensitive fault indicators for further fault diagnosis. Secondly, the orthogonal neighborhood preservation embedding (ONPE) is employed to eliminate the redundant information from the original multi-domain feature set. Finally, the classical SVM is improved to form the Adboost-SVM for the early fault diagnosis of rolling bearings. The feasibility and validity of this method are verified by applying the early fault diagnosis of rolling bearings. The results show that Adaboost_SVM can greatly enhance the diagnosis capability for weak features of early faults. [ABSTRACT FROM AUTHOR]

Highlights • The manuscript proposes a new kind of fault diagnosis method combined RSSD and WT. • The information is fused resonance attribute and local time–frequency characteristic. • The fault feature recognition ability of the method is prior to WPD and EMD. Abstract To solve the problem of early fault diagnosis of rolling bearing under strong background noise, a fault diagnosis method based on integration of Resonance-based Sparse Signal Decomposition (RSSD) and Wavelet Transform (WT) is proposed in this paper. The RSSD method is combined with quality factor optimization using genetic algorithm and sub-band reconstruction. Firstly, the early fault vibration signal of the rolling bearing is decomposed by RSSD. The kurtosis value of the low resonance component is taken as the objective function to optimize the combination of high and low quality factors with genetic algorithm. Then, the master sub-band is selected out to reconstruct the low resonance component based on the principle of energy dominant distribution. It can reduce the noise interference and enhance the impulse characteristic of the fault signal. Finally, characteristics of local optimization and multi-resolution of wavelet analysis considered, the multi-scale wavelet decomposition is applied to the reconstructed low resonance component to extract the fault features of the bearing failure deeply. The effectiveness and application value of the method are proved by two different diagnosis cases of rolling bearing faults. By comparisons, the fault feature extraction ability of the proposed method is prior to WPD method and similar or prior to EMD method for different bearing fault signals. [ABSTRACT FROM AUTHOR]

The class imbalance in the machine monitoring data and the data distribution discrepancy due to the different working conditions make it difficult to build a strong mechanical fault diagnosis model in practice. In this paper, we proposed a high-temperature augmented neighborhood metric-learning network (HNMN) for cross-domain fault diagnosis with imbalanced data. In the proposed network, fault types are classified by comparing the distance from the sample to the prototype. And neighborhood component analysis-based domain adaptation scheme was presented to reduce the distance of similar samples in source and target domains thus eliminating the domain shift and solving the variable working condition problem. A classification boundary optimization strategy based on high-temperature mechanism was applied to avoid the overlap of feature distributions of minor classes and major classes, and finally solve the class imbalance problem. The performance and superiority of HNMN are evaluated and analyzed using three fault diagnosis cases. The experimental results demonstrate that the proposed network can achieve higher fault diagnosis accuracy compared to the state-of-the-art methods in the class imbalance and variable working condition diagnosis scenarios. • A novel high-temperature neighborhood metric-learning network for imbalanced cross-domain fault diagnosis is proposed. • Several fault diagnosis experiments were conducted on three vibration datasets to analyze the effectiveness of the proposed method. • The effect of high-temperature mechanisms and pseudo labels on the network is discussed in depth. [ABSTRACT FROM AUTHOR]

• An incipient fault diagnosis approach for rolling bearings is proposed. • Verified validity of direct fast iterative filtering decomposition processing multi-component amplitude modulation and frequency modulation signals. • The effective component reconstruction method based on the effective weighted sparseness kurtosis index is not limited by the pretreatment decomposition method. • Conducted experiments confirm the validity of fault diagnosis method. • Direct fast iterative filtering and the rolling bearing early fault detection and diagnosis approach have been extensively compared and the superiority of its performance is validated. The fault vibration signal of rolling bearing is typical non-stationary and non-linear signals. It usually exhibits the characteristics of amplitude modulation and frequency modulation (AM-FM) due to the interaction between the various components within the bearing and modulation of the rotor speed. In addition, the incipient weak fault feature information is inevitably masked by the strong background noise and other vibration interference because of the influence of the working environment of mechanical equipment. Therefore, new high efficiency and effective processing methods are indispensable for feature extraction of bearing fault diagnosis signals. Direct fast iterative filtering (dFIF) is a new kind of adaptive mode decomposition method proposed by Antonio Cicone based on iterative filtering (IF) and fast iterative filtering (FIF). dFIF is used to quickly decompose multi-component signals into a set of intrinsic mode functions (IMFs) by means of fast Fourier transform (FFT). In order to study the decomposition performance and examine the feature extraction ability of the dFIF method, a group of harmonic signals with simple linear superposition, multi-components AM-FM signals with different data length and different signal-to-noise ratio (SNR) are simulated and analyzed. For comparison, other mode decomposition methods were also applied to the simulated signals, such as adaptive local iterative filtering (ALIF), local mean decomposition (LMD), and empirical mode decomposition (EMD). The results show that dFIF decomposition methods have faster decomposition speed and better accuracy than other methods. Furthermore, the results also demonstrate excellent performance in AM-FM feature extraction and anti-mode mixing. On the other hand, the effective weighted sparseness kurtosis (EWSK) index integrates the periodicity and intensity of impact in each mode, which can effectively identify the effective mode. Therefore, considering the above merits, EWSK is integrated on the dFIF to effectively accomplish the fault diagnosis for rolling bearing under strong background noise. Firstly, the raw vibration signals of rolling bearing fault are decomposed into a set of IMFs by using dFIF. The EWSK indicator is then utilized to select IMFs with rich bearing fault impulse information for reconstruction. Finally, the Hilbert envelope demodulation analysis is used to analyze the reconstructed signal, extract the bearing fault feature and then judge the fault type. This proposed method is applied to analyze the simulated and field measured vibration signals of rolling bearing faults. Simultaneously, the other above-mentioned adaptive TFA methods are also adopted to analyze the signals. The results compared show that the proposed method has excellent performance in the aspects of decomposition speed and accuracy under the influence of strong background noise. It demonstrates the effectiveness and applicability of the proposed method for signal processing and fault pattern recognition of rolling bearings. [ABSTRACT FROM AUTHOR]

• Multi-domain feature set is constructed from roller bearing original vibration signal. • Correlation analysis is improved to select sensitive features from multi-domain feature set. • Multi-kernel relevance vector machine is designed to diagnose the roller bearing early fault. • The roller bearing experiment shows that this method has more excellent diagnosis performance. Since the vibration signal of mechanical equipment with early faults is highly similar to that of mechanical equipment under the normal state, it is still a great challenge to extract sensitive features from the original vibration signal to execute the intelligent fault diagnosis for mechanical equipment. An early fault diagnosis method for rolling bearings based on multi-kernel relevance vector machine and multi-domain features was proposed in this paper. In order to reflect the time-varying characteristics, the vibration signals and operation status of rolling bearings in time series were incorporated into the process of early fault diagnosis. The three steps of the early fault diagnosis method for rolling bearings were as follows. Firstly, the vibration signals of rolling bearings during operation were measured online. Secondly, the original vibration signals were decomposed by wavelet packet transformation. The fault features were extracted from sensitive frequency band by time domain statistical analysis as well as, frequency domain statistical analysis, and then the multi-domain feature set was constructed to fully characterize the intrinsic properties of vibration signals. The correlation analysis was adopted to eliminate insensitive features from original multi-domain feature set. The low-dimensional feature set that are highly sensitive to early failures was reconstructed to improve the computational efficiency for subsequent fault diagnosis. Finally, the intelligent fault diagnosis was carried out based on the multi-kernel relevance vector machine model. The performance of this proposed method has been validated in practical rolling bearing fault diagnosis. The results show that the proposed method can achieve higher diagnosis accuracy for rolling bearing under different working conditions than traditional single-kernel model and is effective in early fault diagnosis. [ABSTRACT FROM AUTHOR]