# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
## 1 Introduction  
With the deepening of the industrialization process, the revolution of industry 4.0 is emerging in many industry areas [1]. A variety of intelligent equipment and intelligent equipment management technologies have been created, so as to redefine the interaction between humans and machines [2]. With the increased complexity of equipment, fault diagnosis has become a key technology to ensure the safety and efficiency of the production process [3]. Smart fault diagnostic technologies will be essential in the age of industry 4.0. With the development of sensing and information technology, massive multi-source data can be obtained automatically from equipment, which will become the foundation of developing advanced fault diagnosis methods [4]. Gas turbines are widely used in the defense and energy industry. It is essential to perform fault diagnosis on a regular basis to maintain the reliability of gas turbine. However, fault diagnosis is generally a challenging task due to the structural complexity, which involves various fault modes [5]. In traditional diagnostic processes [6; 7], measurement data represent the steady-state values, while the trending patterns in the transient phase are neglected. However, it is hard to determine steady-state values in the transient phase. For example, the time for military aircrafts fighting in unsteady conditions, deviating from steady states, accounts for 70% of the total fight time [8]. It is not feasible to obtain the steady-state data for a military aircraft. Additionally, the effects appear to be remarkable in transient processes compared to the steady-state condition for some kinds of fault modes [9; 10]. Compared with the study of fault diagnosis with steady-state data, this lacks the study of fault diagnosis with transient process data. Therefore, the objective of this thesis is to study the fault diagnosis of gas turbines based on transient process data. Many gas turbine performance-analysis-based diagnostic technologies have been developed since [PERSON] [11] introduced the first gas path analysis method in 1967. Gas turbine engine fault diagnosis during transient processes was first analyzed by [PERSON] [9]. [PERSON] developed a non-linear-model-based diagnostic method, combined with a genetic algorithm, and applied it to a model gas turbine engine to diagnose engine faults by using the accumulated deviation obtained from transient measuring data [8]. [PERSON] proposed a data-driven fault diagnosis and estimation scheme. Fault detection, isolation and, estimations filters was developed using system Input/Output (I/O) data in operation [12]. [PERSON] [13] provided an overview of the use of both performance and mechanical transient analysis as a means to detect gas turbine problems, to express the importance of transient analysis. [PERSON] [14] utilized engine data acquired during takeoff to trend the performance of a modern turbofan engine. Analytical redundancy methods have been applied to gas turbine engine transient data with a view to extracting the desired fault information [15]. [PERSON] and [PERSON] presented a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine process, which integrated linear model identification and output estimation [16]. The system was used in a single-shaft industrial gas turbine plant to experimentally verify the robustness of the solution obtained [17]. [PERSON] proposed a data hierarchical fusion method, using improved, weighted Dempster-Shaffer evidence theory, to integrate data-driven and physics-based models for the gas-path fault diagnosis of engines [18]. Dynamic, process-based gas turbine fault diagnosis requires special parameters, which can describe the dynamic process of gas turbines. Due to the accumulative effects of the transient process, it will be more sensitive to choosing transient process data than steady-state data for fault diagnosis.