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Despite the recent success of deep-learning-based fault diagnosis of rotating machinery, to enable accurate and robust diagnosis models, existing approaches proceed with the assumption that training and test data follow the same distribution. However, in practical industrial settings, variations in operating conditions and environmental noise can cause changes in the characteristics of the training and test data, called domain shift, resulting in performance degradation of the test data. To deal with these issues, this paper proposes an asymmetric inter-intra domain alignments (AIIDA) approach for fault diagnosis under various levels of domain shift. First, inter-domain alignment is conducted by minimizing the maximum mean discrepancy loss and domain adversarial loss. Next, intra-domain alignment is performed by adjusting the inconsistency loss. This approach allows the proposed AIIDA method to learn features that have lower inter-domain distance and higher intra-domain distance; thus, the fault diagnosis performance in the target domain can be significantly improved. Extensive experimental assessment that examines various scenarios across three bearing datasets is performed to validate the effectiveness of the proposed approach. Furthermore, a study comparing the proposed method with other existing methods demonstrates that the proposed method outperforms other methods.

This paper suggests label-based, mini-batch methods for convolutional neural network (CNN) based diagnosis of fluid-film bearing rotor systems. Rather than using random mini-batches in the training process, mini-batches are generated based on the label information. Label information is a critical factor for robust diagnosis. Five different types of label-based mini-batches are proposed and their performance is compared to the conventional random mini-batch method. In addition, sensitivity analysis of kernels in convolutional neural networks is suggested as a method to analyze the performance variation. A case study of a fluid-film bearing rotor system is used to show the effect of the proposed methods. The case study results indicate a wide range of performance variation among the proposed mini-batch methods. Of the examined methods, the equally labeled mini-batch approach presents the best performance. Moreover, the results of the kernel sensitivity analysis show that the use of properly sensitive kernels does positively affect the overall performance of the CNN.

Some anomaly states of journal bearing rotor systems are direction-oriented (e.g., rubbing, misalignment). In these situations, vibration signals vary according to the direction of the sensors and the health state. This makes diagnosis difficult with traditional diagnosis methods. This paper proposes an omnidirectional regeneration method to develop a robust diagnosis algorithm for rotor systems. The proposed method can generate vibration signals in arbitrary directions without using extra sensors. In this method, signals are generated around the entire circumference of the rotor to consider all possible directions. Then, the directionality of each state is proved by mathematically and is evaluated using a proposed metric. When a directional state is determined, the classification is carried out on all of the generated signals. When a non-directional state is found, the classification is performed on only one of the generated signals to minimize computational load without sacrificing accuracy. The proposed ODR method was validated using experimental data. The classification results show that the proposed method generally outperforms the conventional classification method. The results support the proposed concept of using ODR signals in diagnosis procedures for journal bearing systems.

Deep learning-based research has drawn much attention in the field of fault diagnosis of various mechanical systems due to its powerful performance. In deep learning-based methods, signals become the input for a deep learning algorithm. However, the performance of an algorithm can be diminished if there is significant noise in the data. To address the noise issue, this paper proposes a fault diagnosis method called multi-task learning of classification and denoising (MLCD). The proposed method is designed to make a fault diagnosis algorithm robust against the noise in vibration signals by learning the denoising task simultaneously with the classification. Given a noisy input, MLCD can improve test accuracy by implementing denoising as an auxiliary task, using hyperparameters chosen by Bayesian optimization. To validate the proposed MLCD method, it is integrated with the two most commonly used deep learning algorithms: long short-term memory and one-dimensional convolutional neural network. For a case study, these algorithms are tested to classify the states of a rotor testbed data. The results show that the proposed MLCD method extracts noise-robust and meaningful features; ultimately, this improves the fault diagnosis performance.