With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly ...necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.
This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and ...hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on fault diagnosis techniques and their applications. Comments on the advantages and constraints of various diagnosis techniques, including model-based, signal-based, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of fault diagnosis is presented.
With the rapid development of industry, fault diagnosis plays a more and more important role in maintaining the health of equipment and ensuring the safe operation of equipment. Due to large-size ...monitoring data of equipment conditions, deep learning (DL) has been widely used in the fault diagnosis of rotating machinery. In the past few years, a large number of related solutions have been proposed. Although many related survey papers have been published, they lack a generalization of the issues and methods raised in existing research and applications. Therefore, this paper reviews recent research on DL-based intelligent fault diagnosis for rotating machinery. Based on deep learning models, this paper divides existing research into five categories: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN). This paper introduces the basic principles of these mainstream solutions, discusses related applications, and summarizes the application features of various solutions. The main problems of existing DL-based intelligent fault diagnosis (IFD) research are summarized as small-size sample imbalance and transfer fault diagnosis. The future research trends and hotspots are pointed out. It is expected that this survey paper can help readers understand the current problems and existing solutions in DL-based rotating machinery fault diagnosis, and effectively carry out related research.
•Review existing deep learning-based intelligent fault diagnosis of rotating machinery.•Introduce basic principles and discuss existing applications.•Summarize existing solutions of imbalanced small-size samples and transfer problems.
In recent years, unsupervised domain adaptation-based methods have been widely developed for intelligent bearing fault diagnosis across various working conditions. However, a considerably more ...challenging and practical fault diagnosis scenario in which the source and target domains are respectively collected from bearings across different positions and machines is urgent to be addressed. To solve this issue, an innovative end-to-end domain conditioned joint adaptation network (DCJAN), which is composed of a domain conditioned feature extractor, two classifiers, and a domain discriminator is presented. On the one hand, the domain conditioned feature extraction structure is designed to relax totally-shared network assumptions in feature extraction and learn more domain-specialized features for cross-domain fault diagnosis of bearings. On the other hand, a joint adaptation strategy is implemented for diagnostic knowledge transfer across domains, in which domain-level and class-level adaptations are respectively achieved by domain-adversarial training and bi-classifier adversarial training. Extensive experiments including cross-position fault diagnosis and cross-machine fault diagnosis of bearings indicate the validity and superiority of the proposed method.
•A zero-shot intelligent diagnosis method for mechanical compound faults is proposed.•A label description space is built to represent the semantic relationship among different fault patterns.•A new ...method is proposed to calculate prototypes of health conditions in the label description space.•A label description space embedded model is proposed for unseen compound fault diagnosis.
It has always been an issue of significance to diagnose compound faults of machines. Existing intelligent diagnosis methods have to be trained by sufficient data of each compound fault. However, both labeled and unlabeled data of mechanical compound faults are usually difficult to collect or even completely inaccessible for training in real scenarios. Therefore, compound faults are usually unseen fault patterns. Unseen fault patterns are those that have no labeled or unlabeled training data. Without training data of compound faults, the current intelligent diagnosis methods usually fail in recognizing compound faults. This paper proposes a zero-shot intelligent diagnosis method for unseen compound faults of machines. The proposed method contains three stages, i.e., the feature learning, pre-judgment and fault recognition. The key to this method is a label description space embedded model for intelligent fault diagnosis (LDS-IFD) in Stage 3. In LDS-IFD, a label description space (LDS) is built to construct the relationship among different fault patterns. LDS is embedded between the feature space (FS) and the health condition label space (HCLS). Then the projection between FS and LDS is constructed by a linear supervised autoencoder (LSAE). By similarity evaluation in LDS or FS, LDS-IFD is able to recognize mechanical compound faults when only the data of single faults are accessible for training. The proposed method is demonstrated on a bearing dataset and a planetary gearbox dataset. Results show that the proposed method is effective in diagnosing unseen compound faults of machines.
•Applications of machine learning to machine fault diagnosis are reviewed.•Traditional machine learning brought intelligence to fault diagnosis in the past.•Deep learning focuses on further enhanced ...benefits in the present.•Transfer learning promotes achievements to engineering scenarios in the future.•A roadmap of intelligent fault diagnosis is pictured to provide research trends.
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to machine fault diagnosis. This is a promising way to release the contribution from human labor and automatically recognize the health states of machines, thus it has attracted much attention in the last two or three decades. Although IFD has achieved a considerable number of successes, a review still leaves a blank space to systematically cover the development of IFD from the cradle to the bloom, and rarely provides potential guidelines for the future development. To bridge the gap, this article presents a review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective. In the past, traditional machine learning theories began to weak the contribution of human labor and brought the era of artificial intelligence to machine fault diagnosis. Over the recent years, the advent of deep learning theories has reformed IFD in further releasing the artificial assistance since the 2010s, which encourages to construct an end-to-end diagnosis procedure. It means to directly bridge the relationship between the increasingly-grown monitoring data and the health states of machines. In the future, transfer learning theories attempt to use the diagnosis knowledge from one or multiple diagnosis tasks to other related ones, which prospectively overcomes the obstacles in applications of IFD to engineering scenarios. Finally, the roadmap of IFD is pictured to show potential research trends when combined with the challenges in this field.
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.
The engine is the core component of the power system, and the health status of the components of the engine is very important for the normal operation of the power system. Most of the exist-ing fault ...diagnosis methods diagnose the engine fault type without further analysis of the sever-ity of the fault. Different fault severity requires different maintenance measures. Therefore, this paper proposes a cascading fault diagnosis model based on Gated Recurrent Unit (GRU) to di-agnose the fault type and the severity of the corresponding fault type.Firstly, the effective fea-tures are extracted from the vibration signals, and then the features are input into the GRU for fault type diagnosis to obtain the sub-fault diagnosis model. After training, one fault type diag-nosis model and four fault severity diagnosis models are obtained. Then the obtained model is cascaded to obtain the total fault diagnosis network. Fault type diagnosis is located at the first level, and four fault severity diagnosis is located at the second level. The effectiveness of the proposed method is verified by experimental data.
As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development ...and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning.
A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be ...unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely and effectively in this study. Firstly, by analysing the open-circuit fault features of IGBTs in the three-phase PWM rectifier, it is found that the occurrence of the fault features is related to the fault location and time, and the fault features do not always appear immediately with the occurrence of the fault. Secondly, different data-driven fault diagnosis methods are compared and evaluated, the performance of random forests algorithm is better than that of support vector machine or artificial neural networks. Meanwhile, the accuracy of fault diagnosis classifier trained by transient synthetic features is higher than that trained by original features. Also, the random forests fault diagnosis classifier trained by multiplicative features is the best with fault diagnosis accuracy can reach 98.32%. Finally, the online fault diagnosis experiments are carried out and the results demonstrate the effectiveness of the proposed method, which can accurately locate the open-circuit faults in IGBTs while ensuring system safety.
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