Monitoring system for condition of stored - grain is very important to the safety of grain storage. However, sensors in monitoring system for condition of stored - grain tend to more faults due to the complexity of the sys- tem and poor work environment. A fault diagnosis and data reconstruction strategy under the using of principal component analysis（ PCA）has been presented in this paper for sensors in monitoring system condition of stored -grain. The measured data under operation condition was used to build principal component analysis models. The PCA model was utilized to partition the measurement space into the principal component subspace （PCS）and the residual subspace （RS）. Square prediction error（SPE） statistic was utilized to detect sensor faults. Sensor validity index（SVI） was eraployed to identify and locate faulty sensors. Faulty data was recovered by sliding the faulty data to PCS via iteration. Finally, the strategy proposed was validated using data from a real monitoring system for condition of stored - grain. The validation results showed the PCA -based sensor fault diagnosis and data reconstruction strategy is accurate and effective.
Journal of the Chinese Cereals and Oils Association
monitoring system for condition of stored - grain