Fault diagnosis of chemical process data becomes one of the most important directions in research and practice. Conventional fault diagnosis and classification methods first extract features from the raw process data. Then certain classifiers are adopted to make diagnosis. However, these conventional methods suffer from the expertise of feature extraction and classifier design. They also lack the adaptive processing of the dynamic information in raw data. This paper proposes a fault diagnosis method based on long short-term memory (LSTM) neural network. The novel method can directly classify the raw process data without specific feature extraction and classifier design. It is also able to adaptively learn the dynamic information in raw data. First, raw process data are used to train the LSTM neural network until the cost function of LSTM converges below certain predefined small positive value. In this step, the dynamic information of raw process data is adaptively learned by LSTM. Then testing data are used to obtain the diagnosis results of the trained LSTM neural network. The application of LSTM to fault identification and analysis is evaluated in the Tennessee Eastman benchmark process. Extensive experimental results show LSTM can better separate different faults and provide more promising fault diagnosis performance.

Sequential Fault Diagnosis Based on LSTM Neural Network

Fault diagnosis, which identifies the root cause of the observed out-of-control status, is essential to counteracting or eliminating faults in industrial processes. Many conventional data-driven fault diagnosis methods ignore the fault tendency of abnormal samples, and they need a complete retraining process to include the newly collected abnormal samples or fault classes. In this article, 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 it then extracts both fault tendency and nonlinear structure from the obtained data matrix by using convolutional operation. After that, the weights in fully connected layers can be trained based on the obtained features and their corresponding fault labels. Because of the architecture of this network, the diagnosis performance of the 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 the BCNN model can update itself to include new coming abnormal samples and fault classes. The proposed method is applied both to a simulated process and a real industrial process. 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 classes.

Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability

Landslide prediction is important for mitigating geohazards but is very challenging. In landslide evolution, displacement depends on the local geological conditions and variations in the controlling factors. Such factors have led to the “step-like” deformation of landslides in the Three Gorges Reservoir area of China. Based on displacement monitoring data and the deformation characteristics of the Baishuihe Landslide, an additive time series model was established for landslide displacement prediction. In the model, cumulative displacement was divided into three parts: trend, periodic, and random terms. These terms reflect internal factors (geological environmental, gravity, etc.), external factors (rainfall, reservoir water level, etc.), and random factors (uncertainties). After statistically analyzing the displacement data, a cubic polynomial model was proposed to predict the trend term of displacement. Then, multiple algorithms were used to determine the optimal support vector regression (SVR) model and train and predict the periodic term. The results showed that the landslide displacement values predicted based on data time series and the genetic algorithm (GA-SVR) model are better than those based on grid search (GS-SVR) and particle swarm optimization (PSO-SVR) models. Finally, the random term was accurately predicted by GA-SVR. Therefore, the coupled model based on temporal data series and GA-SVR can be used to predict landslide displacement. Additionally, the GA-SVR model has broad application potential in the prediction of landslide displacement with “step-like” behavior.

Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model

In this article, 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. Specifically, the method learns to determine the fault categories using the human-defined fault descriptions instead of the collected fault samples.The defined description consists of arbitrary attributes of the faults, including the fault positions, the consequences of the fault, and even the cause of the fault, etc. For the attribute knowledge of target faults, they can be prelearned and transferred from some readily available faults occurred in the same process. Afterwards, the target faults can be diagnosed based on the defined fault descriptions without the need for any additional data based training. Besides, the supervised principle component analysis is adopted in our method to extract the attribute related features to offer an effective attribute learning. We analyze and interpret the feasibility of the fault description based method theoretically. Also, the zero-sample fault diagnosis experiments are designed and conducted on the benchmark Tennessee–Eastman process and the real thermal power plant process to validate the effectiveness. The results show that it is indeed possible to diagnose target faults without their samples.

Xin Gao

Papers

An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process