Study On Methods And System Realization Of Sensor Data Validation By Soft Computing Technique For Engine Tests

Sensor data validation is severely concerned by engineers of engine test domain, which calls forth the study on methods and system realization of sensor data validation by soft-computing technique for engine tests in this paper.A center weighted idempotent median (CWIM) filter was introduced to removing high-amplitude impulsive noise in signals of gas turbine test, and a fast algorithm for the specific CWIM filter was devised. The filtering function and algorithmic efficiency were evaluated using a test signal, which representing the sensor response to engine abrupt faults, linear deterioration and/or regime transients, while contaminated with random noise and high-amplitude impulsive noise. Results confirmed that median filter is good at removing impulsive noise while preserving real steep edges in signals, also expected effect was obtained when applied to gas turbine test data.A subspace model based approach was presented for detection of errors in signals with linear correlation. The normal subspace model was generated through principal component analysis (PCA), and statistics in conventional principal component subspace and residual subspace were quantitatively connected with error magnitude and subspace characteristics. The characteristics of statistics varying with data faults development in multi-scale PCA was analyzed, and a multi-scale subspace based method was developed for detection of small errors. The time lag of error detection was shortened, and false alarm rate was reduced when applied to practical gas turbine test data.A neural network model based approach was presented to handle the bias detection and correction problem with data correlated by nonlinear and time-varying relations in gas turbine test. A special architecture of the auto-associative neural network was defined with different input and output parameters. Novel estimators and predictors based on auto-associative neural network were devised and evaluated with practical test data. A scheme integrating operations of error detection, fault isolation and data reconstruction was presented based on auto-associative neural network.A method for fusing evidence information using Bayesian belief network was introduced into sensor data validation. Uncertainty expression of sensor state and relations in engine and its components test were defined. The algorithms for automatic generation of the Bayesian belief network files, belief probability calculation and network update after a abruption of one faulty sensor were developed. Real-time running feasibility was analyzed, and criteria for Bayesian belief network evaluation was presented.The mathematical models for fluid and mechanical characteristics of high pressure gas turbine test system in steady regimes were developed. The effectiveness of analytical redundancy technique based on first principles models was evaluated. A scheme based on moving window technique that continuously computes auto-correlation function of samples of sensor data in time domain was devised. A solution for validating gas turbine test sensor data was presented, which answered critical problems about the construction of a data validation system, including check relation number, residual thresholds, single cycle decision logic, multi-cycle decision strategy, system scaleable capacity to any sensor set, etc. The methods for sensor selection, relations definition and system test were presented in detail. A sensor validation network development system and a real-time kernel was developed in software. A prototype system was realized, which well demonstrated the versatility and effectiveness for post validation of practical high pressure gas turbine test data.