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A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data. However, machines often operate with various working conditions, or the target task has different distributions with the collected data used for training (the domain shift problem). Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning (UDTL)-based IFD problem. Although it has achieved huge development, a standard and open source code framework and a comparative study for UDTL-based IFD are not yet established. In this article, we construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD, which is rarely studied, including transferability of features, the influence of backbones, negative transfer, physical priors, and so on. To emphasize the importance and reproducibility of UDTL-based IFD, the whole test framework will be released to the research community to facilitate future research. In summary, the released framework and comparative study can serve as an extended interface and basic results to carry out new studies on UDTL-based IFD. The code framework is available at https://github.com/ZhaoZhibin/UDTL .

Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

A survey of transfer learning for machinery diagnostics and prognostics

In engineering practice, mechanical equipment is usually in polytropic working conditions, where the data distribution of training set and test set is inconsistent, resulting in insufficient generalization ability of the intelligent diagnosis model. Simultaneously, different tasks often need to be modeled separately. Domain adaptation, as one of the research contents of transfer learning, has certain advantages in solving the problem of inconsistent feature distribution. This article designs and establishes a domain adaptation framework based on multiscale mixed domain feature (DA-MMDF) for cross-domain intelligent fault diagnosis of rolling bearings under polytropic working conditions. The proposed method first uses the MMDF extractor to obtain features from the collected data, which constructs a complete feature space through variational mode decomposition (VMD) and mixed domain feature extraction to fully mine the state information and intrinsic attributes of the vibration signal. Second, the dimensionality reduction and optimization of features are achieved through extreme gradient promotion, and meaningful and sensitive features are selected according to the importance of features to eliminate redundant information. The optimized important features are combined with the manifold embedded distribution alignment method to realize the distribution alignment of data in different fields and cross-domain diagnosis. In order to verify the effectiveness of the proposed approach, the rolling bearing data sets gathered from the laboratories are employed and analyzed. The analysis result confirms that DA-MMDF is able to achieve effective transfer diagnosis between polytropic working conditions. Compared with traditional intelligent fault diagnosis methods and DA methods, the method proposed in this article achieved the state-of-the-art performances.

An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions

Deep learning (DL) theory has made great progress in the field of intelligent fault diagnosis, and the development of domain adaptation has greatly promoted fault diagnosis under polytropic working conditions (PWC). Extensive studies have been conducted to solve the problem of fault diagnosis under PWC. However, the existing fault diagnosis methods based on domain adaptation have the following shortcomings. First, multi-source information fusion is rarely considered. Second, the utilization of inherent labels is also insufficient in classification problems. To deal with the above problem, a novel multi-source dense adaptation adversarial network (MDAAN) is proposed, which leverages multi-sensor vibration information and classification label information. Specifically, the frequency spectrum of multi-sensor data are firstly employed to make full use of fault information. Afterwards, the dense convolution and fusion convolution blocks are used for deep feature extraction and fusion. Finally, a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously. The experimental results from various working conditions, including still distant working conditions, all demonstrate that the proposed method can achieve state-of-the-art performances, which has shown great promise as an intelligent fault diagnosis method.

A multi-source dense adaptation adversarial network for fault diagnosis of machinery

Anomaly detection is the cornerstone for the health management of rolling element bearings. The unsupervised learning model for anomaly detection driven only by normal data has received increasing attention in recent years. In this article, an innovative deep-learning-based model, namely, memory residual regression autoencoder (MRRAE), is developed to improve the accuracy of anomaly detection in bearing condition monitoring. The memory module and autoregressive estimator are applied to calculate the probability density distribution of the latent memory residual representation. The reconstruction errors and surprisal values of the proposed model are used to detect the abnormal condition of bearing. To verify the superiority of the proposed method in anomaly detection, two sets of run-to-failure experimental data set gathered from the laboratories are studied and analyzed. The result demonstrates that the proposed MRRAE model achieves superior performance compared with several conventional and deep-learning-based anomaly detection methods. Furthermore, the proposed method pays close attention to the special structure of bearing vibration signal and provides a new way for explaining the decision-making processes of deep neural networks.

Memory Residual Regression Autoencoder for Bearing Fault Detection

Bearing design, production and complicated operating condition can lead to the scattered life cycle degradation distribution, which will bring a challenge of generalization for performance degradation assessment models. And it is costly and time-consuming to collect a large amount of labeled data for supervised diagnosis, especially when the task comes from a new operating condition. Thus in this paper, a novel bearing degradation assessment model is proposed based on transfer learning and deep hierarchical features extraction. The research of degradation assessment is transformed to the classification task of degradation pattern, which divides degradation process into the normal, slight fault, fault development and damage patterns. The hierarchical network with random weight parameters is introduced to extract the local sub-band characteristics of spectrum, in which the multiple alternately convolution and pooling layers without supervised fine-tuning are employed. Joint Geometrical and Statistical Alignment method is then utilized to obtain projected sharing feature space, and thus the knowledge of bearing degradation process is transferred to accomplish degradation pattern assessment under different operating conditions. Results of the experiments on bearing fault severity and degradation process show that the proposed method reduces the feature distribution divergence between the degradation processes and accomplishes bearing performance degradation assessment in different operating condition.

Transfer learning for bearing performance degradation assessment based on deep hierarchical features.

The condition monitoring (CM) of rotating machinery (RM) is an essential operation for improving the reliability of mechanical systems. For this purpose, an efficient CM method that possesses simple and intuitive attributes is required for industrial applications. For condition monitoring that connects fault detection, degradation assessment, and prognosis applications, health indicators (HIs) have been developed in the past few decades. The construction of a HI is the decisive procedure for extracting informative fault information from the monitoring signal. From the initial statistical parameter-based construction methods to the introduction of data-oriented intelligent methods such as deep learning in recent years, HIs construction methods have ranged from fault mechanism-based approaches to a data-based approach, which involve two different technologies regardless of superiority or inferiority. This paper provides a systematic review of the HIs construction methods for rotating machinery proposed in the literature. It emphasizes the classical technical approaches and recent interesting research trends and analyzes the benefits and potential of efficient HIs for condition monitoring. The current challenges and future research opportunities are also presented in this paper. The Engineers and researchers interested in this research can be informed of current research ideas and directions in the field by reading this paper, as well as inspiring potentially excellent research work in the future. 

Zihao Lei

Papers

An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions

A multi-source dense adaptation adversarial network for fault diagnosis of machinery

Memory Residual Regression Autoencoder for Bearing Fault Detection

Transfer learning for bearing performance degradation assessment based on deep hierarchical features.

Construction of health indicators for condition monitoring of rotating machinery: A review of the research