A false alarm fault frequently appeared in antenna-servo system of the CINRAD/SA weather radar of Shanwei in the second half of 2011, so possible reasons for the false alarm fault were listed firstly using method of e...A false alarm fault frequently appeared in antenna-servo system of the CINRAD/SA weather radar of Shanwei in the second half of 2011, so possible reasons for the false alarm fault were listed firstly using method of exhaustion, and then the main reason was determined using exclusive method. That is, the fault was closely related to the signal transmission channel from the antenna mount to servo system in RDA cabinet. After ex- amining questionable nodes in the transmission channels of the alarm signal, we found that the false alarm fault might result from the interference of a burr in the temperature sensing circuit of the elevation motor. In actual operation, a filter capacitor was connected with the corresponding pin in the upper optical board to screen the interference of a burr, thereby successfully eliminating the false alarm fault in antenna-servo system of the CIN- RAD/SA radar of Shanwei.展开更多
在电网故障诊断领域,传统的诊断方法主要依赖于专家经验设计诊断规则,由此提出一种新的电网故障诊断方法,从电网告警信息中提取故障特征,从而实现对电网故障类型的准确识别。构建电网告警信息样本数据集,并通过结合Word2vec和GloVe(glob...在电网故障诊断领域,传统的诊断方法主要依赖于专家经验设计诊断规则,由此提出一种新的电网故障诊断方法,从电网告警信息中提取故障特征,从而实现对电网故障类型的准确识别。构建电网告警信息样本数据集,并通过结合Word2vec和GloVe(global vectors for word representation),构建告警信息集的词向量;使用文本卷积神经网络(TextCNN)和双向长短期记忆(BiLSTM)提取告警信息中的故障特征信息;利用注意力机制关注对故障类型识别更关键的告警信息文本;采用Focal Loss处理数据集中的类不平衡问题,模型最终输出识别的电网故障类型。在中国某电网公司的真实数据集上进行实验,结果表明,所提出的模型的Macro-F_(1)分数达到了93.1%,优于其他8个分类模型,验证了所提出的方法的有效性。展开更多
水电站计算机监控系统长期运行于强电磁干扰、多源异构数据并发及工况瞬变的复杂工业环境中,导致传统监控方法虚警频发。为此,提出基于径向基函数神经网络(radial basis function neural network,RBFNN)的水电站监控系统智能告警方法。...水电站计算机监控系统长期运行于强电磁干扰、多源异构数据并发及工况瞬变的复杂工业环境中,导致传统监控方法虚警频发。为此,提出基于径向基函数神经网络(radial basis function neural network,RBFNN)的水电站监控系统智能告警方法。该方法首先采集了水电站监控系统中的设备状态数据、日志数据和配置数据,构建了包含输入层、隐含层和输出层的RBFNN故障诊断模型。其中输入层设置3个节点对应三类监控数据,输出层设置4个节点分别对应电气、机械、环境及其他监控部分的故障分类。通过参数寻优确定隐含层结构,并采用聚类方法确定基函数中心,使用最小二乘法优化网络权重,完成模型训练。实验依托某实际水电站监控系统展开,设计涵盖紧急至信息级的七级故障分类体系,并部署分层告警框架进行验证。结果表明,所提方法能有效抑制环境干扰引致的虚警,精准识别并分级呈现故障,仅在非关键性预警与信息级故障中各出现一次误判,显著优于对照方法。研究表明,所提方法能显著提升复杂工业背景下的故障辨识鲁棒性与告警直观性,为水电站监控系统的智能化运维提供了一种有效解决方案。展开更多
Quantized fault detection for sensor/actuator faults of networked control systems （NCSs） with time delays both in the sensor-to-controller channel and controller-to-actuator channel is concerned in this paper. A fau...Quantized fault detection for sensor/actuator faults of networked control systems （NCSs） with time delays both in the sensor-to-controller channel and controller-to-actuator channel is concerned in this paper. A fault model is set up based on the possible cases of sensor/atuator faults. Then, the model predictive control is used to compensate the time delay. When the sensors and actuators are healthy, an H stability criterion of the state predictive observer is obtained in terms of linear matrix inequality. A new threshold computational method that conforms to the actual situation is proposed. Then, the thresholds of the false alarm rate （FAR） and miss detection rate （MDR） are presented by using our proposed method, which are also compared with the ones given in the existin~ literatures. Finally, some numerical simulations are shown to demonstrate the effectiveness of the proposed method.展开更多
Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit ...Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.展开更多
文摘A false alarm fault frequently appeared in antenna-servo system of the CINRAD/SA weather radar of Shanwei in the second half of 2011, so possible reasons for the false alarm fault were listed firstly using method of exhaustion, and then the main reason was determined using exclusive method. That is, the fault was closely related to the signal transmission channel from the antenna mount to servo system in RDA cabinet. After ex- amining questionable nodes in the transmission channels of the alarm signal, we found that the false alarm fault might result from the interference of a burr in the temperature sensing circuit of the elevation motor. In actual operation, a filter capacitor was connected with the corresponding pin in the upper optical board to screen the interference of a burr, thereby successfully eliminating the false alarm fault in antenna-servo system of the CIN- RAD/SA radar of Shanwei.
文摘在电网故障诊断领域,传统的诊断方法主要依赖于专家经验设计诊断规则,由此提出一种新的电网故障诊断方法,从电网告警信息中提取故障特征,从而实现对电网故障类型的准确识别。构建电网告警信息样本数据集,并通过结合Word2vec和GloVe(global vectors for word representation),构建告警信息集的词向量;使用文本卷积神经网络(TextCNN)和双向长短期记忆(BiLSTM)提取告警信息中的故障特征信息;利用注意力机制关注对故障类型识别更关键的告警信息文本;采用Focal Loss处理数据集中的类不平衡问题,模型最终输出识别的电网故障类型。在中国某电网公司的真实数据集上进行实验,结果表明,所提出的模型的Macro-F_(1)分数达到了93.1%,优于其他8个分类模型,验证了所提出的方法的有效性。
文摘水电站计算机监控系统长期运行于强电磁干扰、多源异构数据并发及工况瞬变的复杂工业环境中,导致传统监控方法虚警频发。为此,提出基于径向基函数神经网络(radial basis function neural network,RBFNN)的水电站监控系统智能告警方法。该方法首先采集了水电站监控系统中的设备状态数据、日志数据和配置数据,构建了包含输入层、隐含层和输出层的RBFNN故障诊断模型。其中输入层设置3个节点对应三类监控数据,输出层设置4个节点分别对应电气、机械、环境及其他监控部分的故障分类。通过参数寻优确定隐含层结构,并采用聚类方法确定基函数中心,使用最小二乘法优化网络权重,完成模型训练。实验依托某实际水电站监控系统展开,设计涵盖紧急至信息级的七级故障分类体系,并部署分层告警框架进行验证。结果表明,所提方法能有效抑制环境干扰引致的虚警,精准识别并分级呈现故障,仅在非关键性预警与信息级故障中各出现一次误判,显著优于对照方法。研究表明,所提方法能显著提升复杂工业背景下的故障辨识鲁棒性与告警直观性,为水电站监控系统的智能化运维提供了一种有效解决方案。
基金supported by National Natural Science Foundation of China(No.61074065)Natural Science Foundation of Hebei Province(No.F2012203184)Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20111333120009)
文摘Quantized fault detection for sensor/actuator faults of networked control systems （NCSs） with time delays both in the sensor-to-controller channel and controller-to-actuator channel is concerned in this paper. A fault model is set up based on the possible cases of sensor/atuator faults. Then, the model predictive control is used to compensate the time delay. When the sensors and actuators are healthy, an H stability criterion of the state predictive observer is obtained in terms of linear matrix inequality. A new threshold computational method that conforms to the actual situation is proposed. Then, the thresholds of the false alarm rate （FAR） and miss detection rate （MDR） are presented by using our proposed method, which are also compared with the ones given in the existin~ literatures. Finally, some numerical simulations are shown to demonstrate the effectiveness of the proposed method.
文摘Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.
