In this paper, a challenging fault diagnosis task is studied, in which no samples of the target faults are available for the model training. This scenario has hardly been studied in industrial research. But it is a common problem that massive fault samples are not available for the target faults, which limits the successes of conventional data-driven approaches in practical application. Here, we introduce the idea of zero-shot learning into the industry field, and tackle the zero-sample fault diagnosis task by proposing the fault description based attribute transfer method.

Fault diagnosis, which identifies the root cause of the observed out-of-control status, is essential to the counteraction or elimination of faults in industrial processes. Many conventional data driven fault diagnosis methods ignore the fault tendency of abnormal samples, and need a complete retraining process to include the newly collected abnormal samples or fault classes. In this study, a broad convolutional neural network (BCNN) is designed with incremental learning capability for solving the aforementioned issues. The proposed method combines several consecutive samples as a data matrix, and then extracts both fault tendency and nonlinear structure from the obtained data matrix using convolutional operation. After that, weights in fully connected layer can be trained based on the obtained features and their corresponding fault labels. Because of the architecture of this network, the diagnosis performance of BCNN model can be improved by adding newly generated additional features. Finally, the incremental learning capability of the proposed method is also designed, so that BCNN model can update itself to include new coming abnormal samples and fault classes. Experimental results illustrate that it can better capture the characteristics of the fault process, and effectively update diagnosis model to include new coming abnormal samples and fault classe.

In recent years, machine learning algorithms have been successfully applied to industrial processes. However, the concurrent analysis of static and dynamic representations has not been comprehensively addressed for industrial process fault classification. In the current work, an enhanced random forest algorithm with a concurrent analysis of static and dynamic nodes is proposed to address this issue for fault classification. First, the standard slow feature analysis is modified by designing a new slowness index that is more suitable for a supervised fault classification problem. Second, a feature ranking process is conducted to determine the significant features. These features, which substitute the raw variables in the nodes, are used to build the enhanced random forest. Using this scheme, the significant static and dynamic nodes are selected to enhance the discriminative ability and interpretation. Additionally, the slow features that are uncorrelated are more suitable for training the forest than the initial correlated variables, and the dynamic characteristics of industrial processes are thus comprehensively addressed. The application of the proposed method to fault classification is evaluated by both the Tennessee Eastman benchmark and a real-world three-phase flow process. The experimental results show that the proposed method outperforms the traditional learning algorithms with remarkable accuracy and F1 score that both exceed 70% for the 16-class Tennessee Eastman process and exceed 99% for the 4-class three-phase flow process. The selected significant features reveal that both the static and dynamic information play important roles in fault classification.