Based on the influence of circuit element tolerances to the k-fault diagnosis, a method of fault diagnosis is presented which is called minimum tolerance estimation algorithm and has clear physical meaning. Using this...Based on the influence of circuit element tolerances to the k-fault diagnosis, a method of fault diagnosis is presented which is called minimum tolerance estimation algorithm and has clear physical meaning. Using this’method, an effective estimation of the equivalent fault sources can be obtained with less computing time. It is especially worthwhile to point out that an adaptive sub-optimum algorithm, which comes from the above method, requires even less computing-labor and is particularly suitable to more complicated circuits as well as real-time fault location.展开更多
A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to ...A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.展开更多
<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, ...<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, and the parameters are updated via on-line correction. As it can predict the trend of process and determine the existence of malfunctions simultaneously, this method does not need to design problem-specific observer to estimate unmeasured state variables. Application of the proposed method is presented on one water tank and one aromatization reactor, and the results are compared with those from the traditional method.展开更多
The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-i...The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.展开更多
提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合...提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合的感应电动机转子断条数目诊断新方法。模拟了转子断条故障下的瞬时无功功率信号并用其衡量ESPRIT-PSA的性能。结果表明:ESPRIT-PSA只需短时数据就能准确测量瞬时无功功率信号中的转子断条故障特征分量。随后,为解决现有的电机瞬时无功功率信号分析MIRPSA(Motor instantaneous reactive power signal analysis)类方法无法准确诊断转子断条数目的问题,引入LightGBM对转子断条故障进行多分类以准确诊断转子断条数目。最后针对一台异步电动机进行转子断条诊断实验,结果表明:该方法是有效的,并且因将瞬时无功功率作为分析信号而适用于电机低转差率的情况。展开更多
基金Supported by the National Natural Science Foundation of Chilla
文摘Based on the influence of circuit element tolerances to the k-fault diagnosis, a method of fault diagnosis is presented which is called minimum tolerance estimation algorithm and has clear physical meaning. Using this’method, an effective estimation of the equivalent fault sources can be obtained with less computing time. It is especially worthwhile to point out that an adaptive sub-optimum algorithm, which comes from the above method, requires even less computing-labor and is particularly suitable to more complicated circuits as well as real-time fault location.
基金Supported by the joint fund of National Natural Science Foundation of China and Civil Aviation Administration Foundation of China(No.U1233201)
文摘A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.
基金Supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No. 2005-29)Excellent Scholar Research Award Foundation of Shandong Province (No. 2006BS05005)
文摘<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, and the parameters are updated via on-line correction. As it can predict the trend of process and determine the existence of malfunctions simultaneously, this method does not need to design problem-specific observer to estimate unmeasured state variables. Application of the proposed method is presented on one water tank and one aromatization reactor, and the results are compared with those from the traditional method.
基金This paper is supported by National Natural Science Foundation of China under Grant No.50675209 InnovationFund for Outstanding Scholar of Henan Province under Grant No. 0621000500
文摘The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.
文摘提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合的感应电动机转子断条数目诊断新方法。模拟了转子断条故障下的瞬时无功功率信号并用其衡量ESPRIT-PSA的性能。结果表明:ESPRIT-PSA只需短时数据就能准确测量瞬时无功功率信号中的转子断条故障特征分量。随后,为解决现有的电机瞬时无功功率信号分析MIRPSA(Motor instantaneous reactive power signal analysis)类方法无法准确诊断转子断条数目的问题,引入LightGBM对转子断条故障进行多分类以准确诊断转子断条数目。最后针对一台异步电动机进行转子断条诊断实验,结果表明:该方法是有效的,并且因将瞬时无功功率作为分析信号而适用于电机低转差率的情况。
