This paper studied an integrative fault diagnostic system on the power transformer. On-line monitor items were grounded current of iron core, internal partial discharge and oil dissolved gas. Diagnostic techniques wer...This paper studied an integrative fault diagnostic system on the power transformer. On-line monitor items were grounded current of iron core, internal partial discharge and oil dissolved gas. Diagnostic techniques were simple rule-based judgment, fuzzy logistic reasoning and neural network distinguishing. Considering that much faults information was interactional, intellectualized diagnosis was implemented based on integrating the neural network with the expert system. Hologamous integrating strategies were materialized by information-based integrating monitor devices, shared information database on several levels and fusion diagnosis software along thought patterns. The expert system practiced logic thought by logistic reasoning. The neural network realized image thought by model matching. Creative conclusion was educed by their integrating. The diagnosis example showed that the integrative diagnostic system was reasonable and practical.展开更多
Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete en...Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.展开更多
文摘This paper studied an integrative fault diagnostic system on the power transformer. On-line monitor items were grounded current of iron core, internal partial discharge and oil dissolved gas. Diagnostic techniques were simple rule-based judgment, fuzzy logistic reasoning and neural network distinguishing. Considering that much faults information was interactional, intellectualized diagnosis was implemented based on integrating the neural network with the expert system. Hologamous integrating strategies were materialized by information-based integrating monitor devices, shared information database on several levels and fusion diagnosis software along thought patterns. The expert system practiced logic thought by logistic reasoning. The neural network realized image thought by model matching. Creative conclusion was educed by their integrating. The diagnosis example showed that the integrative diagnostic system was reasonable and practical.
基金Project supported by the National High-Tech R&D Program(863)of China(No.2014AA041501)
文摘Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.
