Intelligent Machinery Fault Diagnosis Research Articles

Intelligent fault diagnosis of machinery based on hybrid deep learning with multi temporal correlation feature fusion

AbstractWith the advent of intelligent manufacturing era, higher requirements are put forward for the fault diagnosis technology of machinery. The existing data‐driven approaches either rely on specialized empirical knowledge for feature analysis, or adopt single deep neural network topology structure for automatic feature extraction with compromise of certain information loss especially the time‐series information's sacrifice, which both eventually affect the diagnosis accuracy. To address the issue, this paper proposes a novel multi‐temporal correlation feature fusion net (MTCFF‐Net) for intelligent fault diagnosis, which can capture and retain time‐series fault feature information from different dimensions. MTCFF‐Net contains four sub‐networks, which are long and short‐term memory (LSTM) sub‐network, Gramian angular summation field (GASF)‐GhostNet sub‐network and Markov transition field (MTF)‐GhostNet sub‐network and feature fusion sub‐network. Features of different dimensional are extracted through parallel LSTM sub‐network, GASF‐GhostNet sub‐network and MTF‐GhostNet sub‐network, and then fused by feature fusion sub‐network for accurate fault diagnosis. Two fault diagnosis experimental studies on bearings are implemented to validate the effectiveness and generalization of the proposed MTCFF‐Net. Experimental results demonstrate that the proposed model is superior to other comparative approaches.

A Novel Interpretable Model via Algorithm Unrolling for Intelligent Fault Diagnosis of Machinery

A Novel Interpretable Model via Algorithm Unrolling for Intelligent Fault Diagnosis of Machinery

Intelligent Machinery Fault Diagnosis With Event-Based Camera

Event-based cameras are the emerging bio-inspired technology in vision sensing. Different from the traditional standard cameras, the event-based cameras asynchronously record the brightness change per pixel, and have the great merits of high temporal resolution, low energy consumption, high dynamic range, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . While the event-based cameras have been initially exploited in several common vision-based tasks in the recent years, the investigation on machine condition monitoring problem is quite limited. This paper offers the first attempt in the current literature on exploring the contactless event vision data for machine fault diagnosis. A vibration event representation is proposed to transform the event records into typical data samples, and a deep convolutional neural network model is used for processing the event information. To enhance the model robustness against environmental noisy vision events, an event data augmentation method is proposed to introduce variations of the event patterns. A deep representation clustering method is further proposed to improve the pattern recognition performance with respect to different machine health conditions. Experiments on the event vision-based rotating machine fault diagnosis problem are carried out. It is extensively validated that high fault diagnosis accuracies can be obtained using the vision data from the event-based cameras, which are competitive with the popular accelerometer data. Considering the properties of flexibility, portability and data recognizability, the event-based cameras thus provide a promising new tool for contactless machine health condition monitoring and fault diagnosis.

Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis

Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions.

Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions. To address these problems, a novel fault diagnosis method combining a generative adversarial network and transfer learning is proposed in this paper. Dummy samples with similar fault characteristics to the actual engineering monitoring data are generated by the generative adversarial network to expand the dataset. The transfer fault characteristics of monitoring data under different working conditions are extracted by a deep residual network. Domain-adapted regular term constraints are formulated to the training process of the deep residual network to form a deep transfer fault diagnosis model. The bearing fault data are used as the original dataset to validate the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the influence of insufficient original monitoring data and enable the migration of fault diagnosis knowledge under different working conditions.

Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis

Due to the limitations of data quality and quantity of a single industrial user, the development of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the perspectives of both academic research and engineering applications in the recent years. Collaborative fault diagnosis model development has been receiving increasing attention, where the distributed data at different users are explored simultaneously. However, data security and privacy are the major industrial concerns, which have not been well addressed in the literature. In this paper, a blockchain-based decentralized federated transfer learning method is proposed for collaborative machinery fault diagnosis. A tailored committee consensus scheme is designed for optimization of the model aggregation process, and a source data-free transfer learning method is further proposed. After global model initialization, the fault diagnosis model can be built through iterations of committee member selection, performance evaluation, transfer learning, model aggregation and blockchain updates. The experiments on two decentralized fault diagnosis datasets are implemented for validations, and higher than 90% testing accuracies can be generally achieved. The experimental results indicate the proposed method is effective in data privacy-preserving collaborative fault diagnosis of multiple users. It offers a promising tool for applications in the real industrial scenarios.

Task-incremental broad learning system for multi-component intelligent fault diagnosis of machinery

Broad learning system (BLS) is widely used in intelligent fault diagnosis (IFD) since its high computation efficiency and incremental learning ability. However, its applicability is limited to the single-task learning scenario in which only one diagnosis task needs to be learned. Generally, a machinery system contains multiple critical components that need to be diagnosed. The fault data of different components will be collected at different times for model training. It is essentially a task-incremental learning scenario in which the diagnosis model needs to learn a series of diagnosis task for different components at different times. Existing BLS methods cannot meet this requirement due to the catastrophic forgetting issue. Therefore, this paper proposes a task-incremental broad learning system (TiBLS) for multi-component IFD. The TiBLS is developed as a multi-head configuration with a series of BLS blocks to learn different diagnosis tasks sequentially. Then, the catastrophic forgetting is prevented via parameter isolation. Finally, the structure-incremental learning ability is developed for the TiBLS to enhance the diagnosis performance of each task without retraining. In this way, the TiBLS will gain more and more functions to diagnose different components over time. The experiment validation is implemented on a simulated machinery system including three critical components. The diagnosis accuracies of the three tasks are 94.75%, 93.02%, and 92.00%, respectively. The training times of the three tasks are 22.2 s, 34.5 s, and 7.6 s, respectively. The satisfying results demonstrate that the TiBLS is an effective and efficient method for multi-component IFD.

Dual-Weight Consistency-Induced Partial Domain Adaptation Network for Intelligent Fault Diagnosis of Machinery

Domain adaptation-based methods have been broadly developed for cross-domain fault diagnosis of machinery, in which the target and source label spaces are identical. However, a more challenging and practical diagnostic scenario in which the source label space subsumes target one is remaining to be solved. To address this issue, an innovative two-stage dual-weight consistency-induced partial domain adaptation (DCPDA) network, consisting of two feature extractors, a classifier, a domain discriminator, a dual-weight consistency-induced reweighting (DCR) module, and a Wasserstein distance based-domain adaptation (WDA) module, is proposed. In the pre-trained stage, a well-trained model on the source domain is acquired. In the fine-tuned stage, the DCR, which conducts a dual-weight consistency-induced weighting strategy, obtains a bi-level compound weight from both class-level and sample-level weights. Embedded with the bi-level weight, the WDA is encouraged to transfer the source knowledge with mutual agreement from double-level weights across domains. Accordingly, the DCPDA is capable of alleviating negative transfer dramatically and mapping the source diagnostic knowledge into the target domain selectively. Extensive experiments indicate the validity and advantage of the proposed DCPDA.

Remaining Useful Life Prediction of Rolling Bearings Based on Segmented Relative Phase Space Warping and Particle Filter

Predictive maintenance plays a crucial role in the field of intelligent machinery fault diagnosis, which improves the efficiency of maintenance. This paper focuses on the extraction of real-time damage feature and the prediction of remaining useful life (RUL) in predictive maintenance of rolling bearings. Some RUL prediction approaches lack dynamic foundations and require large amounts of data and prior knowledge. This paper proposes the algorithm of segmented relative phase space warping (SRPSW) and a strategy combining double exponential model (DEM) and particle filter (PF) to predict the RUL. SRPSW provides a dynamic basis for real-time RUL prediction in different stages. The DEM-based PF reduces the need for prior knowledge and improves accuracy. The analysis results from normal and accelerated degradation experiments show that the proposed SRPSW overcomes the failure of the original PSW in depicting the later operating stage of bearings. Further, the relative damage indicators (RDIs) extracted by SRPSW are more accurately than commonly used indicators. The predicted results show that the DEM-based PF does not require professional and prior information whilst ensuring the accuracy of RUL prediction. The proposed approach in this paper provides a new avenue for predictive maintenance of bearings.

Self-learning transferable neural network for intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data

As a promising tool for intelligent diagnosis of rotating machinery with unlabeled data, transfer learning (TL) has attracted considerable attentions from academia and industry. However, mechanical data in real-case have obviously unlabeled and imbalanced characteristics, which are not simultaneously concerned enough by existing intelligent TL fault diagnosis methods. Specially, the highly imbalanced mechanical data lead to the skewed marginal distribution and classifier, which makes a huge challenge for the TL-based intelligent fault diagnosis method. Self-updating knowledge via tradeoff of believing, doubting and rectifying is a main learning process of human being, from which the previous experience is of great importance to enhance the learning ability. Inspired by the learning strategy of human being, a self-learning transferable neural network (STNN) is proposed for the intelligent machinery fault diagnosis with unlabeled and imbalanced data in this study. Three novel loss terms are constructed into STNN for realizing the self-believing, doubting and rectifying in the prediction of health conditions. First one is the self-believing loss term, which uses the conditional distribution adaptation to align the learned cross-domain features proactively. Second one is the self-doubting loss term that provides the ability of freeing from the false experience for STNN. Third one is the self-rectifying loss term, in which the information entropy is employed to regulate the learning process of STNN. Two experimental cases of rotating machinery show that the effectiveness and superiority of the proposed STNN in enhancing the intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data.

Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity DT model of the physical assets can produce system performance data that is close to reality, which provides remarkable opportunities for machine fault diagnosis when the measured fault condition data are insufficient. This paper presents an intelligent fault diagnosis framework for machinery based on DT and deep transfer learning. First, the DT model of the machine is built by establishing the simulation model and with further updating through continuously measured data from the physical asset. Second, all important machine conditions can be simulated from the built DT. Third, a new-type deep structure based on novel sparse de-noising auto-encoder (NSDAE) is developed and pre-trained with condition data from the source domain, as generated from the DT. Then, to achieve accurate machine fault diagnosis with possible variations in working conditions and system characteristics, the pre-trained NSDAE is fine-tuned using parameter transfer with only one sample from the target domain. The presented method is validated through a case study of triplex pump fault diagnosis. The experimental results demonstrate that the proposed method achieves intelligent fault diagnosis with a limited amount of measured data and outperforms other state-of-the-art data-driven methods.

An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis

Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis, while the noise mixed in measured signals harms the prediction accuracy of networks. Existing denoising methods in neural networks, such as using complex network architectures and introducing sparse techniques, always suffer from the difficulty of estimating hyperparameters and the lack of physical interpretability. To address this issue, this paper proposes a novel interpretable denoising layer based on reproducing kernel Hilbert space (RKHS) as the first layer for standard neural networks, with the aim to combine the advantages of both traditional signal processing technology with physical interpretation and network modeling strategy with parameter adaption. By investigating the influencing mechanism of parameters on the regularization procedure in RKHS, the key parameter that dynamically controls the signal smoothness with low computational cost is selected as the only trainable parameter of the proposed layer. Besides, the forward and backward propagation algorithms of the designed layer are formulated to ensure that the selected parameter can be automatically updated together with other parameters in the neural network. Moreover, exponential and piecewise functions are introduced in the weight updating process to keep the trainable weight within a reasonable range and avoid the ill-conditioned problem. Experiment studies verify the effectiveness and compatibility of the proposed layer design method in intelligent fault diagnosis of machinery in noisy environments.

Multi-Weight Domain Adversarial Network for Partial-Set Transfer Diagnosis

To realize fault identification of unlabeled data and improve model generalization capability, domain adaptation technology has been increasingly applied to intelligent fault diagnosis of machinery. Nevertheless, traditional domain adaptation diagnosis models generally restrict different domains to have the same label space, which does not always hold in complex industrial scenarios. Consequently, a more practical scenario, i.e., partial-set transfer diagnosis, is explored in this article, where the target label space is a subspace of the source domain. A multiweight domain adversarial network (MWDAN) is proposed to solve this issue, in which the weighting mechanism of class-level and instance-level is jointly designed to distinguish the label space and quantify the transferability of data samples. Based on the proposed strategy, the positive transfer between shared classes is promoted while the negative effect caused by outlier classes is circumvented. As a result, MWDAN can learn discriminative representations for accurate fault diagnosis in the target domain. Extensive experiments constructed on two mechanical systems demonstrate the outstanding performance of MWDAN.

Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery

Intelligent fault diagnosis, which is mainly based on neural network, has been widely used in machinery monitoring. Although such deep learning methods are effective, the new architectures are mainly handcrafted by series of experiments that require ample time and substantial efforts. To automate process of building neural networks and save designing time, a novel differentiable neural architecture search method is proposed. By gradually reducing candidate operations while retaining trained parameters during pruning, computation consumed by each stage of neural architecture search is decreased, which accelerates search process. To improve inferential efficiency of subnetworks, specially designed penalty terms are introduced into the objective function for searching optimal numbers of layers and nodes, which can reduce complexity of subnetworks and save calculation time of signal analysis. In addition, exclusive competition between candidate operations is broken by changing discretization and selection methods of operations, which provides a basis for channel fusion. Effectiveness of the proposed method is verified by two datasets. Experiments show that this method can generate subnetworks of lower complexity and less computational cost than other state-of-art neural architecture search techniques, while achieving competitive result.

Hybrid Pre-Training Strategy for Deep Denoising Neural Networks and Its Application in Machine Fault Diagnosis

Removing or at least attenuating the effect of noise is of significance for machine fault diagnosis under noisy measuring environments. In this paper, deep denoising neural networks are developed to decrease the influence of noise on the modeling accuracy of deep neural networks (DNN). Among the network, a reproducing kernel Hilbert space (RKHS) based denoising layer is designed as the first layer to reduce noise. However, due to the unstable gradients problem of the earlier layers in DNN, weights to-be-optimized in the first denoising layer are tough to update. In addition, the overfitting problem caused by multiple layers also affects the generalization ability of the model. To overcome these dilemmas, a novel hybrid pre-training strategy for deep denoising neural networks is proposed. In this strategy, motivated by the greedy algorithm, a shallow supervised network is firstly adopted to pre-train the weight of the denoising layer for alleviating the learning difficulty of the first layers in deep networks. Secondly, feature extraction layers with a large number of layers are pre-trained by an autoencoder-based unsupervised network. After that, weights of the denoising and feature extraction layers are transferred to a deep classification network, and the entire network is finally fine-tuned via supervised learning. The hybrid pre-training strategy for deep denoising neural networks is applied to machine fault diagnosis. Experiment studies verify the effectiveness of the proposed hybrid pre-training strategy in intelligent fault diagnosis of machinery under noisy measuring environments.

A Robust Weight-Shared Capsule Network for Intelligent Machinery Fault Diagnosis

In practical industrial applications, the working conditions of machinery are changing with long-term operation, and the health status is declining with the degradation of crucial components. When the working condition changes, prior diagnosis models cannot be generalized from one condition to another. To solve this challenging issue, in this article a robust weight-shared capsule network (WSCN) is introduced for intelligent fault diagnosis of machinery under varying working conditions. First, taking raw accelerometer signals as inputs, one-dimensional convolutional neural network is constructed to extract discriminative characteristics. Second, various capsule layers based on multistacked weight-shared capsules are developed to enhance the generalization performance for further fault classification. Finally, margin loss function as well as agreement-based dynamic routing algorithm are employed to optimize the WSCN. In this article, two diagnosis cases are carried out to demonstrate the generalization performance of the WSCN which obtains higher accuracy under varying working conditions than that of other state-of-the-art methods.

Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines Under Imbalanced Data

Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. However, the existing methods use individual deep neural network to extract features and recognize the health conditions under imbalanced dataset, which may easily over-fit the mechanical data and affect the diagnosis accuracy. To deal with this problem, this paper takes the advantages of ensemble learning and proposes an ensemble convolutional neural network (EnCNN) for the intelligent fault diagnosis for machines under imbalanced data. In the proposed method, a convolutional neural network with the input of multi-sensor signals is used as the base classifier. Firstly, the mechanical imbalance dataset is first split into balanced training subsets through under-sampling strategy, and each subset is used to train a base classifier. Then the weight coefficients of each trained base classifier are calculated by G-mean score and anomalous base classifiers are screened using classifier selection. Finally, the base classifiers are integrated into EnCNN through weighted voting strategy. The proposed EnCNN is validated by the imbalanced dataset collected from a machinery fault test bench. By comparing with the related methods, the superiority of EnCNN is verified in intelligent fault diagnosis of machines under imbalanced data.

Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation

Recently, due to the rising industrial demands for intelligent machinery fault diagnosis with strong generalization, transfer learning techniques have been used to enhance adaptability of data-driven approaches. Particularly, the domain shift problem where training and testing data are sampled from different operating conditions of the same machine is well addressed. However, it is still difficult to prepare sufficient labeled data on the tested machine. Therefore, the idea of transferring fault diagnosis knowledge learned from one machine to different but related machines is motivated, and that is realized through a deep learning-based method in this paper. Features of different equipments are first projected into the same subspace using an auto-encoder structure, and cross-machine adaptation algorithm is adopted for knowledge generalization, where the distribution discrepancy between data from different machines is minimized. Experiments on three rolling bearing datasets are implemented to validate the proposed method. The results suggest it is feasible to transfer fault diagnosis knowledge across different machines, and the proposed method offers a novel and promising approach for knowledge generalization.

Deep forest based intelligent fault diagnosis of hydraulic turbine

Deep neural networks (DNNs) for intelligent machinery fault diagnosis require a large amount of training data, powerful computational facilities and have many hyper-parameters that have to be carefully tuned to ensure maximum performance. Deep forest, as a novel alternative to the deep learning framework, has the potential to overcome these shortcomings. In this study, a deep forest-based end-to-end intelligent fault diagnosis method is proposed for hydraulic turbine, in which multi-grained scanning is first used to transform fault feature representations from raw data and enhance fault feature learning ability, and then cascade structure is constructed with different types of random forests to learn fault features level by level and classify faults. The effectiveness of the proposed method is validated using the experimental dataset under twelve conditions, and its practicability is validated using a simulated dataset generated by adding white Gaussian noise to raw experimental signals. The results show that the proposed method is able to adaptively mine available fault features from measured signals, and its diagnosis accuracy is better than that obtained by existing methods. More importantly, the proposed method has better robustness to noise and is less limited to the number of training data.