In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.

Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application.

In recent years, deep learning has become an emerging research orientation in the field of intelligent monitoring and fault diagnosis for industry equipment. Generally, the success of supervised deep models is largely attributed to a mass of typically labeled data, while it is often limited in real diagnosis tasks. In addition, the diagnostic model trained with data from limited conditions may generalize poorly for conditions not observed during training. To tackle these challenges, adversarial learning is introduced as a regularization into the convolutional neural network (CNN), and a novel deep adversarial convolutional neural network (DACNN) is accordingly proposed in this paper. By adding an additional discriminative classifier, an adversarial learning framework can be developed to train the convolutional blocks with the split data subsets, leading to a minimax two-player game. This process contributes to making the feature representation robust, boosting the generalization ability of the trained model as well as avoiding overfitting with a small size of labeled samples. The comparison studies with respect to conventional deep models on two fault datasets demonstrate the applicability and superiority of proposed method.

A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults

Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification

Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery Some popular classifica

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery:

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Vibration response characteristics of a dual-rotor with unbalance-misalignment coupling faults: Theoretical analysis and experimental study

Casing vibration response prediction of dual-rotor-blade-casing system with blade-casing rubbing

Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification

In order to study the dual-rotor system’s rubbing fault, a new dynamic model is established. The unbalance and the rubbing faults are modeled, respectively. Considering the softening characteristics of casing, the Lankarani–Nikravesh model is utilized to describe the impact force between the disk and fixed limiter. The numerical integral method is applied to obtain system’s dynamic behavior, and the characteristics of the rubbing faults are analyzed by time-domain waveform, 3D waterfall plot and spectrum cascades. The influences of rotational speed ratio, initial clearance, mass eccentricity and inter-shaft bearing stiffness on the dynamic characteristics are investigated. The vibration displacement of the low-pressure rotor is collected from the impact experiment performed on a dual-rotor test rig. The analysis result of simulation is identical with the experiment result. Consequently, this method can be used to study characteristics of rubbing faults of dual-rotor bearing system efficiently.

Vibration response analysis of rubbing faults on a dual-rotor bearing system

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

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Nanfei Wang

Papers

Vibration response characteristics of a dual-rotor with unbalance-misalignment coupling faults: Theoretical analysis and experimental study

Casing vibration response prediction of dual-rotor-blade-casing system with blade-casing rubbing

Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification

Vibration response analysis of rubbing faults on a dual-rotor bearing system

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine