Modified Label Propagation on Manifold With Applications to Fault Classification

Ying Xie

doi:10.1109/access.2020.2995399

Abstract

In process monitoring, fault classification performance heavily relies on the labels of training data. However, the labeled data are inadequate and difficult to obtain because they require experienced human annotators. In this paper, a modified label propagation (MLP) method is proposed to propagate labels from labeled data to unlabeled data. The proposed label propagation algorithm has the following advantages: (1) It constructs a global and local consistency framework with the aid of a data graph, manifold learning, and data labels. This framework follows the assumption that data on the manifold will have similar structures, and nearby data will have similar labels. (2) Considering the inner relationship between the unlabeled data and historical data, a new definition for the initial label matrix is offered, which is significant for label propagation. (3) The new method propagates labels in a low-dimensional manifold space, which is different from most existing label propagation methods that propagate them in the original space. The results reveal that under the global and local consistency framework, soft labels of unlabeled data are given more effective predictions. With additional soft labels of unlabeled data, the MLP-based fault classification method is introduced. The simulation results obtained using a toy example demonstrate the label propagation performance of the MLP, and those obtained for the penicillin fermentation process verify the effectiveness of the MLP-based fault classification method.

Highlights

For process monitoring operations in control engineering, fault classification plays a very important role in locating the fault and helping operators take correct remedial measures [1]–[5]
The simulation results obtained using a toy example demonstrate the label propagation performance of the modified label propagation (MLP), and those obtained for the penicillin fermentation process verify the effectiveness of the MLP-based fault classification method
Because data generated under different initial conditions and operation modes have different categories, the penicillin fermentation process (PFP) is a good candidate for evaluating the performance of the MLP-based fault classification method

Summary

INTRODUCTION

For process monitoring operations in control engineering, fault classification plays a very important role in locating the fault and helping operators take correct remedial measures [1]–[5]. Data collected from an industrial process are usually difficult to classify because of the high-dimensional data characteristics and complex data relationships involved Based on these facts, some classification methods have been proposed, such as Fisher discriminant analysis (FDA) [6]–[8], support vector machine (SVM) [9], [10], and the k nearest neighbor (kNN) classification [11], [12]. These features may lead to inaccurate label prediction results To address these drawbacks, a modified label propagation (MLP) method is proposed in this paper. 2) In the initialization phase of label propagation, considering the inner relationship between the unlabeled data and historical data, a new definition of the initial label matrix is proposed based on the similarity and weight of each class, which is significant in label propagation processes.

MODIFIED LABEL PROPAGATION METHOD

CASE STUDY

Findings

CONCLUSIONS