**Hang Ouyang 1, Jiusun Zeng 1,\* and Yifan Li <sup>1</sup> and Shihua Luo <sup>2</sup>**


Received: 22 February 2020; Accepted: 23 March 2020; Published: 27 March 2020

**Abstract:** It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.

**Keywords:** gated recurrent unit; support vector data description; time sequence prediction; fault detection and identification

#### **1. Introduction**

Maintaining the blast furnace system at a stable status is critical to ensure efficient production of high-quality blast furnace hot metal [1]. Therefore, condition monitoring of the blast furnace ironmaking process becomes a significant issue. During the operation of blast furnace ironmaking process, different kinds of faults may happen, such as hanging, low stockline and abnormal gas flow. If the faults cannot be detected and identified in time and accurately, it may lead to loss in production rate or even a significant accident.

The problem of fault detection and diagnosis for blast furnace ironmaking process is a long lasting and well research topic. Traditional methods like expert knowledge and fuzzy logic have been well developed in different kinds of expert systems [2]. However, constructing and maintaining an up-to-date knowledge base is difficult. Alternatively, classification-based algorithms like support vector machine have been applied to diagnose faults in blast furnaces [3]. Liu et al. [4] proposed a novel strategy based on cost-conscious least squares support vector machine (LS-SVM) to achieve rapid diagnosis of blast furnace faults. An et al. [5] proposed a support vector machine for multiple classification to diagnose blast furnace faults. The main assumption of classification-based methods is that sufficient faulty samples can be collected, which is often not true in a real blast furnace. More recently, multivariate statistical methods became popular in the monitoring of blast furnaces. For example, Vanhatalo applied the principal component analysis (PCA) to monitor the status of an experimental blast furnace [6]. A two-stage PCA is considered to deal with multi-modal distribution

in blast furnace data [7]. Shang et al. [8] developed a recursive transformed component statistical analysis (RTCSA)-based algorithms to monitor incipiently happened faults in the iron-making process. In addition, other kinds of PCA-based approaches have been introduced to monitor process faults, such as robust PCA [9] and convex hull-based PCA [10].

In order to deal with process dynamics, Zeng et al. applied a state space model to extract residuals from the process data and used the support vector data description (SVDD) to detect blast furnace faults [11]. Also, Vanhatalo and Kulahci [12] considered the impact of autocorrelation to statistical methods like PCA. Dynamic principal component analysis (DPCA) [13,14] and dynamic linear discriminant analysis (DLDA) [15] are also used to handle dynamic processes. From the above analysis, it can be seen that how to handle process dynamics has become an important task in fault detection and diagnosis of blast furnace.

In this paper, a new process monitoring method based on the GRU network [16] is considered to detect and identify process faults in blast furnace. The GRU network is a new type of recurrent neural network (RNN). Comparing to conventional RNN methods like long-short term memory network, it has comparable capability to handle process dynamics, however with a simpler structure and fewer parameters. In fault detection, a GRU neural network is used to make prediction for each process variable, so that the process dynamics can be filtered and a series of residuals can be generated. The generated residuals are then monitored using the support vector data description (SVDD) method [11]. Faulty variables are then identified by inspecting the deviation of the residuals from normal operation condition (NOC). The benefits of the proposed method can be summarized as: (i) the introduction of GRU network can fully capture the dynamic characteristics of the blast furnace data; (ii) faulty variables can be identified by investigating the residual of each variable, which greatly simplifies subsequent fault diagnosis task.

#### **2. Methodologies**

This section describes the methodologies applied in fault detection and identification of blast furnace system. Section 2.1 briefly introduces the GRU network, which is an extension of the LSTM network. Section 2.2 describes the SVDD classifer.
