Data-driven Fault Detection Scheme Research Articles

An alternative data-driven fault detection scheme for dynamic processes with deterministic disturbances

This paper proposes an alternative fault detection (FD) scheme, in which the so-called residual signals are generated by means of a projection of process input data. This is the major difference to the existing model-based and data-driven FD schemes, where residual generator is realized based on the process input and output relationship/dynamics. Moreover, this way of residual generation avoids the parameter identification procedure and also allows us to address deterministic disturbances (unknown inputs), which be paid often less attention by data-driven FD methods. In this fashion, the FD issue reduces to detect change of a random matrix. Since it is difficult to directly measure this change, so the trace of a matrix is adopted as the evaluation function. Furthermore, the threshold can be set by considering the boundedness of disturbance. The effectiveness of the proposed method is verified by a simulation study on an inverted pendulum system.

Data-Driven Multimode Fault Detection for Wind Energy Conversion Systems

In this paper a data-driven fault detection scheme for wind energy conversion system is proposed. The method uses the offline measurements of the wind turbine in a wide range of operating points and builds a monitoring system which is able to detect faults with higher detection rate compared to the classical data-driven techniques. The nonlinear characteristics of wind turbine are approximated by multiple piece-wise linear systems and hence the monitoring scheme is robust against model uncertainties which may arise due to nonlinearity of the system. Moreover, the proposed method is able to discriminate the faults which are affecting the power production in the wind energy system. The effectiveness of the monitoring scheme is demonstrated using the data collected from different 2MW wind turbines. The superior performance of the proposed method is further compared with the classical data-driven methods and the results are discussed.

Data-driven design of robust fault detection system for wind turbines

In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model.