Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.

In this work, a novel data-driven fault diagnostic framework is developed by using hybrid multi-mode machine learning strategies to monitor system health status. The coexistence of multi-mode and concurrent faults and their adverse coupling effects pose serious limitations for developing reliable diagnostic methodologies. A novel framework is proposed by exploiting inherent embedded health information contained in the I/O sensor data. The proposed hybrid strategies consist of optimal integration of recurrent neural network-based feature generation and self-organizing map diagnostic modules. To construct reliable fault diagnostic modules, a systematic clustering and modeling methodology is developed that has two primary advantages: (i) it does not require any a priori knowledge of data set characteristics or system mathematical model, and (ii) it does address and resolve the key limitations and challenges in conventional self-organizing map approaches. The effectiveness of our proposed framework is validated by utilizing sensor data including healthy and various degradation modes in application to compressor and turbine of an aircraft gas turbine engine. Comparisons with other machine learning-based methods in the literature are provided to demonstrate the performance and superiority of our proposed framework in fault diagnostic accuracy, false alarm rates, and in dealing with multi-mode and concurrent fault scenarios.

A Negative Selection Immune System Inspired Methodology for Fault Diagnosis of Wind Turbines.

High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into fault diagnosis of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing fault detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring faults common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various fault severities.

Optimal consensus seeking in a network of multiagent systems: an LMI approach.

In this paper, an optimal control design strategy for guaranteeing consensus achievement in a network of multiagent systems is developed. Minimization of a global cost function for the entire network guarantees a stable consensus with an optimal control effort. In solving the optimization problem, it is shown that the solution of the Riccati equation cannot guarantee consensus achievement. Therefore, a linearmatrix-inequality (LMI) formulation of the problem is used to address the optimization problem and to simultaneously resolve the consensus achievement constraint. Moreover, by invoking an LMI formulation, a semidecentralized controller structure that is based on the neighboring sets, i.e., the network underlying graph, can be imposed as an additional constraint. Consequently, the only information that each controller requires is the one that it receives from agents in its neighboring set. The global cost function formulation provides a deeper understanding and insight into the optimal system performance that would result from the global solution of the entire network of multiagent systems. Simulation results are presented to illustrate the capabilities and characteristics of our proposed multiagent team in achieving consensus.