Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasing attention. However, the existing methods in the literature are generally lower compared to environmental noise and data availability, and it is difficult to achieve promising performance under harsh practical conditions. This paper proposes a new cross-domain fault diagnosis method with enhanced robustness. Noisy labels are introduced to significantly increase the generalization ability of the data-driven model. Promising diagnosis performance can be obtained with strong noise interference in testing, as well as in practical cases with low-quality data. Experiments on two rotating machinery datasets are carried out for validation. The results indicate that the proposed algorithm is well suited to be applied in real industrial environments to achieve promising performance with variations of working conditions.

Existing intelligent gearbox fault diagnosis approaches have two shortcomings: (a) their performance is mostly confined to manual handcrafted features, and (b) they follow a general assumption that the distribution of the data in the source domain (labeled data on which the model is trained) is similar to the target domain (unlabeled data on which the model is tested), which might not be the case in real-world applications. Substantial human expertise and domain knowledge is required for manual feature extraction, and moreover, deploying the same model for a target domain whose distribution is different from the source domain would lead to poor generalization. Since deep learning methods can automatically learn high dimensional feature representations from raw measurement data, this paper proposes a novel deep learning-based domain adaptation (DA) method for gearbox fault diagnosis under significant speed variations. A deep convolutional neural network is used as the main architecture. The paper proposes to minimize the summation of cross-entropy loss (between the labeled source domain data) and maximum mean discrepancy loss (between the labeled source and unlabeled target datasets) simultaneously to adapt the source domain model to be applied in the target domain. The proposed deep learning DA approach is evaluated using experimental data from a gearbox under variable speeds and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other DA methods demonstrate the superiority of the proposed method.

Intelligent data-driven machinery health identification has been attracting increasing attention in the manufacturing industries, due to reduced maintenance cost and enhanced operation safety. Despite the successful development, the main limitation of most existing methods lies in the assumption that the training and testing data are collected from the same distribution, i.e. the same machine under identical condition. However, this assumption is difficult to be met in the real industries, since the diagnostic model is generally expected to be applied on new machines. In order to address this issue, a deep learning-based cross-machine health identification method is proposed for industrial vacuum pumps, which are of great importance in the manufacturing industry but have received far less research attention in the literature. Generalized diagnostic features can be learnt using the proposed domain adaptation technique with maximum mean discrepancy metric. The health identification model learnt from the training machines can be well applied on new machines. Experiments on a real-world vacuum pump dataset validate the proposed method, which is promising for industrial applications.

With the advancement of intelligent manufacturing, different kinds of industrial robots have been applied in modern factories. The liquid crystal display transfer robot (LCDTR) has been widely used in LCD production lines to transport panels. Effective fault diagnosis and prognosis of the industrial robots are of great importance, since unplanned downtime caused by faulty robots significantly reduces the production capacity. Specifically, the ball screw is the critical component in the LCDTR. The failure of the ball screw can cause long downtime. Conventionally, the fault diagnosis of the ball screw is usually based on the vibration signals. However, it is extremely difficult to install the vibration sensors in the industrial robots. Therefore, in order to address this issue in condition monitoring, this paper proposes a data-driven fault diagnosis methodology using the motor current signals of the ball screw. Two time-frequency domain analysis methods are investigated, including short-time Fourier transform (STFT) and wavelet packet decomposition (WPD). The statistical features are extracted, and Fisher score is used to select features. Furthermore, the logistic regression and k-nearest neighbors are applied for the final fault diagnosis. Experiments on a real-world industrial robot dataset are carried out for validation. 100% diagnosis accuracy can be basically achieved by the proposed method, which indicates the non-stationary current signal can be effectively used to identify the health states of the ball screw in the LCDTR.

In the recent years, the intelligent data-driven fault diagnosis methods on the rotating machines have been popularly developed. Especially, deep learning algorithms have been adopted in several studies and promising results have been obtained. However, the cross-domain fault diagnostic problem still remains a challenging issue, where the training and testing data are collected from different operating conditions of the machine. To bridge the domain gap in the training and testing data and increase the model generalization ability, a domain adaptation approach is proposed in this paper within the deep learning framework. Multiple convolutional operations are employed for automatic feature extraction. The maximum mean discrepancy is used to optimize the distributions of the learned features for accurate diagnosis. To further enhance the model generalization ability, the health condition labels of the data are injected with additional noise during model training. Experiments on a bearing fault diagnosis dataset are carried out for validation. The results show that, compared with the popular transfer learning algorithms, the proposed method with noisy health labels can successfully improve the cross-domain diagnosis performance on rotating machines.