Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and r...Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.展开更多
Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalizati...Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.展开更多
基于SOPC神经网络的地铁屏蔽门故障报警系统与传统报警相比有很大的区别。采用神经网络的自学习能力,建立故障报警系统的数学模型。以Altera公司的Cyclone IV E的EP4CE15E22C8为硬件开发平台,设计了一种地铁安全门故障报警系统。利用MOD...基于SOPC神经网络的地铁屏蔽门故障报警系统与传统报警相比有很大的区别。采用神经网络的自学习能力,建立故障报警系统的数学模型。以Altera公司的Cyclone IV E的EP4CE15E22C8为硬件开发平台,设计了一种地铁安全门故障报警系统。利用MODELSIM软件进行模拟仿真试验,改变了传统地铁安全门故障报警模型的稳定性较差、误报频率较高的缺点,对避免影响地铁正常运行具有重要的指导作用。展开更多
文摘Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.
基金Supported by the National Natural Science Foundation of China(61473026,61104131)the Fundamental Research Funds for the Central Universities(JD1413)
文摘Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.
基金supported by the National Natural Science Foundation of China(Nos.51921003,52275153,51975292)the Outstanding Youth Foundation of Jiangsu Province of China(No.BK20211519)+3 种基金Fundamental Research Funds for the Central Universities(No.1001-XAC21022)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics(NUAA)(No.BCXJ19-01)the Fund of Prospective Layout of Scientific Research for NUAAthe Priority Academic Program Development of Jiangsu Higher Education Institutions of China。
文摘基于SOPC神经网络的地铁屏蔽门故障报警系统与传统报警相比有很大的区别。采用神经网络的自学习能力,建立故障报警系统的数学模型。以Altera公司的Cyclone IV E的EP4CE15E22C8为硬件开发平台,设计了一种地铁安全门故障报警系统。利用MODELSIM软件进行模拟仿真试验,改变了传统地铁安全门故障报警模型的稳定性较差、误报频率较高的缺点,对避免影响地铁正常运行具有重要的指导作用。
