Discriminative Fault Features Research Articles

Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants

As an Open Set Recognition (OSR) problem, nuclear power plant fault diagnosis requires not only the correct classification of known faults, but also the effective identification of unknown faults, which cannot be satisfied by most previously developed models. In this study, a novel nuclear power plant OSR fault diagnosis framework based on Convolutional Prototype Learning (CPL) is proposed, in which CPL is used to extract discriminative fault features from raw nuclear power plant data. Besides, two classification methods, One-Class Support Vector Machine (OCSVM) method and Prototype Matching by Distance (PMD) method, are developed to complete the fault diagnosis of open space for the framework. To verify the feasibility and effectiveness of the proposed OSR framework, numerical experiments on 24 OSR tasks with high-dimensional and strong-nonlinear complex nuclear power plant simulation data are conducted, and the OSR performance evaluate by normalized accuracy and Youden's index, feature visualization and convergence rates are analyzed. Results show that compared with adopting traditional Convolutional Neural Network (CNN), the proposed CPL-based framework can significantly boost the OSR diagnostic performance on nuclear power plant fault diagnosis tasks, which benefits from the excellent ability of CPL to extract intra-class compact and inter-class separable feature representation.

Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis

Convolutional neural networks (CNNs) are being utilized for mechanical fault diagnosis, due to its excellent automatic discriminative feature learning ability. However, the poor interpretability and noise robustness of CNNs have plagued both academia and industry. Since traditional signal analysis technology has a sound theoretical basis and physical meaning, it motivates us to use signal processing theory to improve the interpretability and performance of the CNN algorithm. To this end, this paper proposes a multilayer wavelet attention convolutional neural network (MWA-CNN) for noise-robust machinery fault diagnosis. This framework aims to learn discriminative fault features from the wavelet domain, which allows the model to obtain better interpretability and superior performance than conventional time-domain-based CNNs. The proposed Discrete Wavelet Attention Layer (DWA-Layer) is used to map time domain signals to wavelet space, and obtain valuable information through the learnable convolutional layer. By alternately using DWA-Layer and convolutional layer for signal decomposition and feature learning, the proposed framework actually embeds a similar multi-resolution analysis algorithm in CNN. This helps integrate physics-based knowledge into the CNN. Finally, the frequency attention mechanism is proposed to enhance the ability of MWA-CNN to obtain fault-related features from different frequency components. Experiments on high-speed aeronautical bearing and motor bearing datasets prove that the proposed method has excellent fault diagnosis ability and noise robustness. The visual analysis of the attention mechanism contributes to the interpretability of CNN in the field of fault diagnosis.

A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment

Bearing fault diagnosis is a critical and challenging task for prognostics and health management of motors. The ability to efficiently and accurately classify the fault categories based on sensor signals is the key to successful bearing fault diagnosis. Although various data-driven methods have been developed for fault diagnosis in recent years, automatic and effective extraction of discriminative fault features from high-noise vibration signals generated in the real-world industrial environment remains a challenging task. To tackle this challenge, this paper proposes a novel deep learning method based on the combination of residual building Unit, soft thresholding and global context, called RSG, to solve the complex mapping relationship between vibration signals and different types of bearing faults. The proposed RSG integrates the working mechanisms of soft threshold and global context to achieve effective noise reduction and feature extraction. A comparative analysis is performed to demonstrate the advantages of the proposed method. Furthermore, the proposed method is tested on a faulty motor dataset collected by our developed intelligent motor test platform based on Industrial Internet of Things. Experimental results show that our method can achieve an average fault diagnosis accuracy of 98%. Thus, the proposed method proves to be an efficient solution for intelligent bearing fault diagnosis for motors in a high-noise industrial environment.

Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

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 vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure.

Enhanced Lightweight Multiscale Convolutional Neural Network for Rolling Bearing Fault Diagnosis

The vibration signals collected from rolling bearings in industrial systems are highly complex and contain intense environmental noise, which challenges the performance of traditional fault diagnosis methods. Moreover, the applicability of the model in engineering practice, especially in the Industrial Internet of Things context, puts forward higher requirements for its storage and computational costs. Considering these challenges, this article proposes an enhanced lightweight multiscale convolutional neural network (CNN) for rolling bearing fault diagnosis. Our contributions mainly fall into three aspects. Firstly, the proposed model is modular and easy to expand, which combines the idea of multiscale learning with attention mechanism and residual learning, enabling the network to extract more abundant and discriminative fault features directly from the raw vibration signal. Consequently, the proposed model can perform better. Secondly, the interpretability of the multiscale learning mechanism is explored by visualizing the extraction process of multiscale features. Finally, for the first time, we introduce the depthwise separable convolution into multiscale CNN to reduce the storage and computational costs of the model, which realizes the lightweight of the model and improves its applicability in the Industrial Internet of Things context. The experimental results on the rolling bearing dataset demonstrate that, compared with the state-of-the-art multiscale CNN models, the proposed model has better discriminative fault feature extraction ability and anti-noise ability, and is more suitable for practical industrial systems.

Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis.

Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. Therefore, in this paper, we propose multi-scale wavelet Shannon entropy as a discriminative fault feature to improve the diagnosis accuracy of bearing fault under variable work conditions. To compute the multi-scale wavelet entropy, we consider integrating stationary wavelet packet transform with both dispersion (SWPDE) and permutation (SWPPE) entropies. The multi-scale entropy features extracted by our proposed methods are then passed on to the kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. In the end, both the SWPDE–KELM and the SWPPE–KELM methods are evaluated on two bearing vibration signal databases. We compare these two feature extraction methods to a recently proposed method called stationary wavelet packet singular value entropy (SWPSVE). Based on our results, we can say that the diagnosis accuracy obtained by the SWPDE–KELM method is slightly better than the SWPPE–KELM method and they both significantly outperform the SWPSVE–KELM method.

Investigation on the subsynchronous pseudo-vibration of rotating machinery

Subsynchronous pseudo-vibration (SPV) of rotating machinery is one of the primary reasons for fault misdiagnosis. SPV has similar signal signatures to those of a real fault, which usually leads to excessive maintenance or even unscheduled shutdown. For this reason, it is essential to investigate the root causes of SPV so as to reduce unnecessary downtime and maintenance cost. Aiming at this issue, a novel signal model for rotor non-contact vibration is built to describe the generating mechanism of one kind of SPV by considering the combined effects of rotor axial motion and detection surface runout on vibration signal. To obtain more discriminative fault features from the vibration signals, the two-dimension holospectrum (2DH) is employed to integrate the phase information from two perpendicularly installed sensors. The characteristics of precession orbit used to describe the lateral motion of a rotor could be fully extracted by 2DH. It is shown that the precession orbit at the fault feature frequency will degenerate into a straight line when SPV occurs, and thus the eccentricity of precession orbit could be considered as a key feature for discriminating SPV from real subsynchronous vibration (RSV). The effectiveness of proposed method was validated on a rotor test rig. By using this method, the SPV problem of a real gearbox in a blast furnace blower set in a steel mill was successfully diagnosed.

Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis

Currently, vibration analysis has been widely considered as an effective way to fulfill the fault diagnosis task of gearboxes in wind turbines (WTs). However, vibration signals are usually with abundant noise and characterized as nonlinearity and nonstationarity. Therefore, it is quite challenging to extract robust and useful fault features from complex vibration signals to achieve an accurate and reliable diagnosis. This paper proposes a novel feature representation learning approach, named stacked multilevel-denoising autoencoders (SMLDAEs), with the aim to learn robust and discriminative fault feature representations through a deep network architecture for diagnosis accuracy improvement. In our proposed approach, we design an MLD training scheme, which uses multiple noise levels to train AEs. It enables to learn more general and detailed fault feature patterns simultaneously at different scales from the complex frequency spectra of the raw vibration data, and therefore helps enhance the feature learning and fault diagnosis capability. Furthermore, SMLDAE-based fault diagnosis is performed with an unsupervised representation learning procedure followed by a supervised fine-tuning process with label information for classification. Our approach is evaluated by using the field vibration data collected from a self-designed WT gearbox test rig. The results show that our proposed approach learned more robust and discriminative fault feature representations and achieved the best diagnosis accuracy compared with the traditional approaches.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects

To early identify cylindrical roller bearing failures, this paper proposes a comprehensive bearing fault diagnosis method, which consists of spectral kurtosis analysis for finding the most informative subband signal well representing abnormal symptoms about the bearing failures, fault signature calculation using this subband signal, enhanced distance evaluation technique- (EDET-) based fault signature analysis that outputs the most discriminative fault features for accurate diagnosis, and identification of various single and multiple-combined cylindrical roller bearing defects using the simplified fuzzy adaptive resonance map (SFAM). The proposed comprehensive bearing fault diagnosis methodology is effective for accurate bearing fault diagnosis, yielding an average classification accuracy of 90.35%. In this paper, the proposed EDET specifically addresses shortcomings in the conventional distance evaluation technique (DET) by accurately estimating the sensitivity of each fault signature for each class. To verify the efficacy of the EDET-based fault signature analysis for accurate diagnosis, a diagnostic performance comparison is carried between the proposed EDET and the conventional DET in terms of average classification accuracy. In fact, the proposed EDET achieves up to 106.85% performance improvement over the conventional DET in average classification accuracy.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20revolutions-per-minute (RPM), 80RPM, and 140RPM, respectively.

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.