A data similarity based analysis to consequential alarms of industrial processes

The analysis of consequential alarms is beneficial to avoiding alarm flooding and finding out root alarms in an industrial process. In this context, a novel similarity computation method taking into account of correlation delays between process alarms is introduced firstly. Subsequently, the Granger causality method is suggested to further clarify mutual impacts of similar alarm variables based on process data. Through the combination of alarm data similarity analysis and process data causality analysis, the consequential alarms can be effectively identified along with their evolution paths. An industrial case is employed to illustrate the benefits of the contribution.     

Fault diagnosis of pipeline and pump unit systems using status coupling analysis

Earlier studies on fault diagnosis of the pipeline and pump unit systems (PPU) relied mainly on independent equipment analyses, which usually lead to false alarms because of the loss of information fusion. The aim of this study is to utilize the status coupling relationship to improve fault detection sensitivity and reduce false alarm rate. A real-time status identification of related equipment step is added between capturing abnormal signals and listing out diagnosis results. For example, when the pipeline pressure fluctuation is found abnormal, a status analysis of pump units is performed immediately, if the pump units are proven to be operational normally, then the pipeline leak alarm is acknowledged valid. The logical reasoning algorithm is used to capture abnormal conditions of pipeline pressures. The pump unit faults are captured by combining information from multiple sources. Field applications show that the proposed method significantly improves the PPU fault detection capability on fault detection sensitive and accuracy.     

Comprehensive alarm information processing technology with application in petrochemical plant

During the abnormal plant conditions, too much information is produced due to momentary plant excursions above alarm limits. This flood of information impedes correct interpretation and correction of plant conditions by the operator. Existing techniques for the design of alarm systems mostly have weak ability to handle complex hazard scenarios and increase the probability of larger safety issues. In this paper, a comprehensive alarm information processing (AIP) technology is introduced, called multi-round alarm management system (MRAMS), including several processing strategies: AIP based on single sensor, AIP based on sensor group, root cause diagnosis based on Bayesian network, sensor fault judgment method and false alarm inhibition method. In case studies, both simulation experiment and pilot application on a real petrochemical plant are presented. Results indicate the MRAMS is helpful in improving the accuracy of correctly diagnosing the root causes and hence avoiding false and redundant alarms. By adopting this new technology, the safe and reliable operation of the plant can be achieved, and the economic loss brought by improper alarms can be reduced.     

基于数据驱动的石化装置静态报警抑制策略研究

由生产状态变化引起的误报警频发,为解决现有针对基于状态报警的抑制方法缺乏完整性与定量分析的问题,提出了新的报警抑制策略。通过分析报警记录等数据库,对引起报警的生产条件进行结构化整定并量化,利用分类筛选与数据过滤找出基于状态的报警,再结合关联性分析和概率判断,建立了基于数据驱动的静态报警抑制策略。使用现场数据的试验证明了该方法的有效性。     

Combining FAP,MAP and correlation analysis for multivariate alarm thresholds optimization in industrial process

In the real industrial process, alarm threshold optimization is an important part of alarm system rationalization. If the design of alarm threshold is unreasonable, it would result in nuisance alarms, among which the critical alarms are overwhelmed. In order to alleviate this phenomenon, we propose a method of multivariate alarm thresholds optimization to reduce the nuisance alarms. Firstly, causal relationship between process variables is constructed based on the time delay estimation method, thus we can determine the alarms propagation path and then select the optimized variables. Secondly, in order to guarantee both the process safety and correlation consistency, three factors - false alarm probability (FAP), missed alarm probability (MAP), and the correlation between the alarm information and process information – are combined to establish the objective function of the optimization process for the first time. Then, the optimal thresholds are obtained by the genetic algorithm. Finally, the validity and effectiveness of the developed method are illustrated by the Tennessee Eastman process.     

Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant

The alarm system given in industrial plants are massive and complex. Under such condition, critical alarms are overwhelmed by false and unnecessary alarms and thus result in severe safety issues. To address the problem, this paper proposes a probabilistic signed digraph (PSDG) based alarm signal selection method that requires achieving maximal system reliability. In this method, a PSDG model is firstly constructed to visualize the causal relations between process variables. Then the criteria of observability and identifiability are imposed to determine the candidate alarm variables that can qualitatively distinguish all assumed faults. Instead of selecting the minimum number of combinations of candidate variables, the alarm variables are optimized by a reliability formulation that takes into account the missed alarm and false alarm probabilities of the system; this formulation is solved by the receiver operating characteristic (ROC) graph. Finally, the developed methodology is illustrated using a Tennessee Eastman process.     

基于贝叶斯估计的多探测器火警判定方法研究*

为应对感烟探测器的大量误报对消防应急响应带来的挑战,考虑目前以感烟探测器为主的火警设施误报率高且短期内难以全部更换的特点,提出基于贝叶斯估计的多探测器火警判定方法,通过多个探测器的报警时间间隔计算火源位置的后验概率分布,并提出火警真实度概念,为火警判定提供依据。结果表明:使用多探测器耦合模型时每增加1个探测器可将误报率降低约4个数量级,该方法在探测器正常、部分失效、误报的情景下均能有效判别火警。     

Alarm management optimization in chemical installations based on adapted HAZOP reports

Most current alarm systems used in chemical installations show poor performance due to alarm flooding. This study focuses on alarm management systems optimization using the deviation propagation relationship hidden in the hazard and operability study (HAZOP) report, which can be transformed into a critical information source for alarm optimization management. More concretely, this means matching the alarm tag number with the process deviations in the deviation column, possible cause column, and consequence column. Furthermore, a backtracking method and a reasoning method were established to identify the initial alarm and associated alarms. Besides, a root fault diagnosis was carried out. A method of detecting hardware faults and unreasonable alarm thresholds is established using alarm causality corresponding to the deviation causality and associated alarm generation-skipping tracing method. According to the severity of the consequence corresponding to the deviation, a determined alarm priority method is constructed. The results show that the deviation propagation relationship in the HAZOP report is clear, and the topological relationship is easy to build based on the deviation propagation relationship. With comprehensive and in-depth HAZOP analysis reports in China, the alarm management optimization technology based on adapted HAZOP reports shows good prospects for application and promotion.     

A risk-based approach to design warning system for processing facilities

Alarm flooding is a major safety issue in today's processing facilities. Important recommendations are available for alarm management; however, they are often violated in practice, especially in the alarm systems implemented through the distributed control system. An effective process alarm prioritization and management system is desired for a safe and effective operation of a process facility.In present work, authors address two main issues related to an alarm system – the reliability and the prioritization of the alarms. The main objective is to deal with the alarm-flooding problem in process facilities. A multi alert voting system based on sensor redundancy approach is proposed to improve the reliability. A quantitative risk-based alarm management approach is proposed to address the flooding issue. In the risk-based approach, an integrated model consisting of the probability (P), the impact (I) of the potential hazards, and the process safety time is proposed to prioritize these raised alarms.The proposed approach is further explained by a reactor system with pressure and temperature variable monitoring and controls, where the hazards associated with two alerts caused by over high pressure and over high temperature are analyzed and integrated with response time for alarms generation and prioritization.     

Model-based information fusion investigation on fault isolation of subsea systems considering the interaction among subsystems and sensors

The offshore oil industry has expanded to deep water and Arctic. The harsh operating conditions (e.g., ice and strong wind) and increasing complicated system raise the occurrence likelihood of system faults. This requires timely fault isolation and management in the subsea system. However, the offshore oil industry mainly relies on humans to isolate faults based on alarms. With harsh operating conditions and increasing complicated system, this industry urgently needs research on more efficient fault isolation and cause diagnosis methods. Unfortunately, limited research is conducted on fault isolation method in the offshore oil industry. Furthermore, in industry 4.0 era, large amounts of information are obtained. This provides precondition for the application of information fusion technique which aims to improve diagnosis results. However, to the authors’ knowledge, information fusion has not been much studied in the fault isolation of the offshore oil industry. Moreover, the interaction of different subsystems contains valuable information. How the interaction of different subsystems can influence the fault diagnosis has not been explored. This paper proposes a Bayesian network (BN) based method for timely fault isolation and cause diagnosis for the offshore oil industry. The work fuses different information, and it also includes the dependency among different subsystems in the fault diagnosis. As an important alarm source, false alarms are also taken into account in the model. A case study on the subject of the subsea wellhead and chemical injection systems is conducted to demonstrate the functions and merits of the proposed method.     

基于电磁辐射峰谷比值法的煤与瓦斯突出预警

为了提高电磁辐射预警煤与瓦斯突出危险的准确性,减少误报现象,在分析大量电磁辐射现场数据基础上,基于电磁辐射预警煤与瓦斯突出危险原理,提出了电磁辐射峰谷比值法,利用正态分布统计特征计算每个班次的电磁辐射瞬时强度峰值均值和谷值均值,并将其比值作为煤与瓦斯突出危险性的预警值。最后,将该方法应用于义忠煤矿11112风巷掘进工作面,研究结果表明:与临界值系数和动态变化趋势系数法相比,峰谷比值法误报较低,预警准确性高,能够很大程度上减少误报对生产的影响。     

Perceptions of implied hazard for visual and auditory alerting signals

Visual and auditory alerts are increasingly important and have many applications, particularly in the presentation of hazard information in transportation and many industrial systems. This paper is concerned with the factors that govern the relative effectiveness of alerting signals involving various combinations of visual and auditory signals. The visual variables were colour, flash rate, and flash mode, combined with or without an auditory alarm. It was found that the subjects associated different levels of hazard with different alerting light colours, flash rates, flashing modes, and with combinations of auditory and visual alerts. A red flashing light was perceived as the most effective hazard warning colour, with yellow and blue warning lights indicative of less hazardous situations. The faster the flash rate, the greater is the hazard perceived. A flash rate of 60 fpm (flashes per minute) was not as effective as the rates of 180 and 240 fpm, and 240 fpm was the most effective. This implies that hazard warning signal should flash at well above 60 fpm. Having a breakup in the flashing pattern so as to provide a double or triple flash mode also increases the effectiveness of the signal. There were significant interactions between the alert variables used. The difference in perceived hazard levels for the colours blue and yellow were statistically non significant, but blue was more effective in conveying hazard message than yellow at the high flash rates. When accompanied with auditory alarms, blue and yellow were perceived to convey the same perception level of hazard as red without auditory alarms. The effect of colour on perceived hazard was also found to vary with flash mode. As compared to either visual signal alone or a visual signal with other types of acoustic alarms, a siren type of auditory alarm was found more effective for eliciting perception of hazards. There was evidence that presenting alerting signal in triple-flash mode and at high flash rate could be annoying and might not help improving hazard awareness.     

Research on a small-noise reduction method based on EMD and its application in pipeline leakage detection

The noise included in pipeline pressure signal is a small noise whose energy takes a small proportion of pressure signal and is concentrated on high frequency components. However, it will influence pipeline leakage identification and even cause false alarms. Thus, a small-noise reduction method based on EMD (SNR-EMD) is proposed to remove small noise from pressure signal. EMD is applied for extracting the mean envelope of the signal. Then, small fluctuations around the mean envelope are considered to be small noises. Meanwhile, end effect of SNR-EMD is restrained by extrema mirror extension (EME). The results of simulation studies with SNR-EMD show that the larger the noisy signal's signal-to-noise ratio (SNR) is, the better noise reduction effect becomes. And SNR-EMD considered as a low-pass filter removes or reduces the high frequency components. Furthermore, superiorities of SNR-EMD are verified by comparison studies with wavelet packet transform (WPT) and singular value decomposition (SVD). Finally, a case study of leakage identification shows that SNR-EMD can improve the performance of leakage identification and reduce the possibility of false alarms, which makes much easier and further effective to distinguish the leakage mode from other modes after removing noise from pressure signal.     

Exploring the management of industrial hazardous waste based on recent accidents

Three accidents occurred in east China's Jiangsu Province in March and April 2019. These accidents sounded the safety alarm for the management of industrial hazardous waste in the “heavy industry” era. This paper explored the internal causes of these accidents through the current situation of the generation, disposal and comprehensive utilization of industrial hazardous waste in China. The results of the analysis showed that the enterprises management systems of hazardous waste had serious problems at the current stage, leading to many potential risks. The chief problem resided in the fact that these systems failed to take full account of safety and environmental protection. Finally, suggestions for improvement were proposed concerning the above problems.     

Leak detection for long transportation pipeline using a state coupling analysis of pump units

Leak detection for long transportation pipeline with a large economic and environmental impact has been an area of intensive research for more than five decades. This paper presents a novel pipeline leak detection scheme based on a state coupling analysis (SCA). Instead of monitoring the pipeline and pump units separately, SCA introduces a new detecting method of analyzing data in a coupling running condition. A novel capture method for abnormal pressure based on logical reasoning algorithm is proposed. Hamming approach degree arithmetic is applied to calculate the matching mode identifying the state of units. SCA is used to reduce the rate of false alarm and detect the leak with a high detecting sensitivity for long transportation pipeline. An on-line software system based on SCA is utilized to achieve superior accuracy and implementation. An industrial case study for coupling system pipeline leak detection is used as an example to validate the effectiveness of the proposed method.     

基于BP神经网络的受限空间火灾联合探测方法

针对航空受限空间火灾探测高误报的问题,在现有技术成果基础上对多种火灾探测方式进行研讨,并提出1种基于BP神经网络技术的飞机机身内部受限空间火灾联合探测报警方法。该方法结合现有烟雾感应、气体传感器探测等常用火灾探测技术,以红外热成像探测为辅助手段,采用神经网络实现数据融合,对模拟实验舱火灾烟雾进行联合探测,在单一火灾探测方式基础上提高了探测准确率。     

Evaluation and comparison of alarm reset modes in advanced control room of nuclear power plants

An automation function has been widely applied in main control room of nuclear power plants (NPPs). The alarm system of fourth nuclear power plant (FNPP) in Taiwan is also going to be developed with automatic technology that is expected to support the operators’ performance and reduce the number of alarms. In this study, an experiment with a training simulator as an advanced alarm system was conducted to compare the effects of different alarm reset modes on performance and subjective ratings. The objective was to evaluate the practicability of alarm system with only auto-reset function in FNPP. Results revealed that, using the auto-reset mode, participants had lower task load index (TLX) on effort in the first test trial and was more satisfied under multi-task condition. In contrast, using manual reset mode, participants were more satisfied on alarm handling, monitoring, and decision making. In other words, both reset modes are necessary to assist the operator in different aspects, but with only single reset mode is insufficient. The reset function in advanced alarm system therefore should be very flexible.     

PLS-based EWMA fault detection strategy for process monitoring

Fault detection (FD) and diagnosis in industrial processes is essential to ensure process safety and maintain product quality. Partial least squares (PLS) has been used successfully in process monitoring because it can effectively deal with highly correlated process variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T² and the Q statistics are ill suited to detect small faults because they only use information from the most recent observations. Other univariate statistical monitoring methods, such as the exponentially weighted moving average (EWMA) control scheme, has shown better abilities to detect small faults. However, EWMA can only be used to monitor single variables. Therefore, the main objective of this paper is to combine the advantages of the univariate EWMA and PLS methods to enhance their performances and widen their applicability in practice. The performance of the proposed PLS-based EWMA FD method was compared with that of the conventional PLS FD method through two simulated examples, one using synthetic data and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS, especially in the presence of faults with small magnitudes.     

An imprecise Fault Tree Analysis for the estimation of the Rate of OCcurrence Of Failure (ROCOF)

The paper proposes an imprecise Fault Tree Analysis in order to characterize systems affected by the lack of reliability data. Differently from other research works, the paper introduces a classification of basic events into two categories, namely Initiators and Enablers. Actually, in real industrial systems some events refer to component failures or process parameter deviations from normal operating conditions (Initiators), whereas others refer to the functioning of safety barriers to be activated on demand (Enablers). As a consequence, the output parameter of interest is not the classical probability of occurrence of the top event, but its Rate of OCcurrence (ROCOF) over a stated period of time. In order to characterize the basic events, interval-valued information supplied by experts are properly aggregated and propagated to the top. To this purpose, the Dempster–Shafer Theory of evidence is proposed as a more appropriate mathematical framework than the classical probabilistic one. The proposed methodology, applied to a real industrial scenario, can be considered a helpful tool to support risk managers working in industrial plants.