Study On Real-time Extraction Of Modal Features Based On Stochastic Subspace Identification

As the basic facility of offshore oil and gas resources development,offshore platform is the base of offshore production,operation and life.In order to ensure the safe service of offshore platform structure and avoid major malignant accidents,regular inspection and safety assessment of offshore platform structure must be carried out during its service period.So,the real-time modal feature extraction of offshore platform structure is an important monitoring method derived from this target.At present,the commonly used modal feature extraction methods include peak picking method,natural Excitation Technology,ITD(Ibrahim Time Domain)method,Stochastic Subspace Identification method and so on.Among them,the stochastic subspace identification(SSI)has attracted wide attention because of its advantages such as fewer calculation model parameters,no iteration,good convergence,fast operation and good robustness.Based on a jacket platform structure,this paper presents a method for real-time extraction of modal features based on covariance-driven stochastic subspace identification.Firstly,based on the finite element model of a large-scale jacket platform,the feasibility of the theory and algorithm of stochastic subspace identification is verified,and through the discussion and analysis of the calculation parameters involved in the method: system order,Hankel matrix dimension and calculation data length,the optimal parameter ratio is preliminarily selected.The next,the traditional stochastic subspace identification could not reflect the whole structure characteristics and is insensitive to the real structure under the limited measuring points.So a new method of Hankel element reconstruction is proposed.That is,the matrix elements are replaced by sub-matrix and the data arrangement of the sub-matrix is consistent with the spatial arrangement of the platform measuring points.The algorithm is highly correlated with the actual spatial structure,which improves the accuracy of modal identification and greatly improves the computational efficiency.And then,the traditional stochastic subspace identification has too many false modes when structures are large and complex.Based on cumulative characteristics of signals,false mode identification method and elimination criterion are established.In this way,the real modal of the structure can be identified effectively.not only the false modes can be effectively eliminated,but also the utilization rate of response data can be greatly increased.In addition,when the traditional random subspace method is used to calculate the modal information of real jacket platform structure,there are too many interference components(non-stationary signal sources)in the response signals and the time cost is too large.A new signal processing method are proposed to enhance the efficiency of data condensation calculation.Through the modal identification calculation of the response data after the signal smooth processing under a single rule,not only the modal information of the structure can be accurately identified,but also the calculation efficiency is greatly improved.Finally,By calculating and processing the response signals of the finite element model and field data of the target platform,it is proved that the method has good applicability and robustness.The improved stochastic subspace identification can not only provide parameterized indexes for structural model optimization,performance evaluation,damage prediction and structural safety early warning,but also evaluate the long-term modal change of the structure,so as to predict the future stiffness degradation of the structure.