多传感器耦合区域火灾报警技术研究

早期预警与低误报率一直是建筑火灾探测面临的挑战与难题。已有研究多针对特殊场所特定燃烧产物或多种传感器耦合,普适性不强,探测设备成本高,无法大规模应用。通过将火灾烟气蔓延规律与探测器信号时空分布融合,在不增加探测器数量和分布的情况下,提出了一种基于建筑结构微元的多传感器耦合区域火灾报警模型。对典型火灾场景的烟气蔓延情况进行了模拟分析,狭长结构中探测器信号强度变化具有一定的规律性。应用区域火灾报警模型后,报警时间较传统模式提前了14.7%,基本杜绝了单个探测器误报引发建筑物火灾报警的问题。结果表明:多传感器耦合探测模式显著缩短了火灾报警时间,降低了火灾探测误报率,实现了火灾的早期准确识别与预警。     

Improving the effectiveness of warnings by increasing the appropriateness of their information content: some hypotheses about human compliance

The ways that warnings are administered vary greatly. A warning may come as a message broadcast on the radio about severe weather, as a flashing light in the cockpit of an airplane, or as an audible smoke alarm. Typically, warnings provide an auditory or visual signal to assist in the detection of an anticipated stimulus. However, warnings tend to operate in an all or none mode: either the warning is present, or it is not. Consequently, the information they provide is limited. If warnings are provided too often, their information content becomes even lower and frequent false alarms render them ineffective because of the “cry-wolf” effect. On the other hand, if warnings are not administered frequently enough, they result in too many potentially costly misses. In this conceptual paper, it is argued that the effectiveness of warnings might be significantly improved if warnings are made more “intelligent” by providing information about the likelihood of the occurrence of the stimulus. Several representative cases are discussed and analyzed in order to demonstrate the advantages of the proposed methods.     

A dual-sensor-based method to recognize pipeline leakage and interference signals

During the detection of pipeline leakages, false alarms of leak detection could be markedly reduced if the interference signals resulting from pressure regulating, pump regulating or valve movements could be accurately distinguished. A digital recognition method for interference signals and leakage signals based on a dual-sensor system is proposed in this paper. It is demonstrated that the direction of the signal can be recognized by a cross-correlation calculation between two signals from the dual-sensor, one of which undergoes forward linear interpolation and backward linear interpolation. Based on this theory, the interference signal and the leak signal can be discriminated exactly, and the distance between the two sensors in the dual-sensor system can be considerably reduced without needing to increase the sampling frequency. The monotonicity of the cross-correlation function is demonstrated, and a fast discrimination algorithm based on a binary extreme search method, which decreases the computational load and maintains global optimization, is also proposed. A pre-processing method of the actual signal is proposed to decrease the identity requirement for the two sensors in a dual-sensor system. In the experiment based on artificial signals, the proposed discrimination algorithm could achieve accurate recognition of the abnormal signal, and as such, the theory and application of pipeline leak detection based on dual-sensor systems are extended.     

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.     

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.     

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。     

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.     

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.     

Forward collision warning based on a driver model to increase drivers’ acceptance

Abstract

Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers’ acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT (“Proactive Safety for Pedestrians and Cyclists”).

Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4?s (it takes the cyclist 4?s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6?s (asymptotic value of the model at TTA = 4?s) and the warning outside the CB was at TTC = 1.7?s (below the lower 95% value at TTA = 4?s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey.

Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models.

Conclusions: Similar to other studies’ results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers.     

The effects of roadway environment and fatigue on sign perception

Drivers were stopped 200 m (656 ft) after passing a warning sign and tested for their recall and recognition of the sign. An average of 5 to 10% of drivers registered the sign under various conditions, but these results were independent of specific sign content or roadway environment. Both objective and subjective measures of fatigue were related to the probability of seeing a sign on a straight and level road but not on a hilly and winding road. It was concluded that under normal daylight conditions warning signs are either redundant (contain information directly available) or irrelevant to the driver's perceived needs and the driving task. Before ruling out their usefulness, warning signs should be evaluated under conditions of degraded visibility.     

Effects of multitasking on operator performance using computational and auditory tasks

This study investigated the effects of multiple cognitive tasks on human performance. Twenty-four students at North Carolina A&;T State University participated in the study. The primary task was auditory signal change perception and the secondary task was a computational task. Results showed that participants' performance in a single task was statistically significantly different from their performance in combined tasks: (a) algebra problems (algebra problem primary and auditory perception secondary); (b) auditory perception tasks (auditory perception primary and algebra problems secondary); and (c) mean false-alarm score in auditory perception (auditory detection primary and algebra problems secondary). Using signal detection theory (SDT), participants' performance measured in terms of sensitivity was calculated as ?0.54 for combined tasks (algebra problems the primary task) and ?0.53 auditory perceptions the primary task. During auditory perception tasks alone, SDT was found to be 2.51. Performance was 83% in a single task compared to 17% when combined tasks.     

水蒸气引起感烟火灾探测器误报研究

针对水蒸气这种典型的火灾探测误报源．在自行研制的火灾探测综合模拟实验平台中,通过收集水沸腾形成的水雾模拟水蒸气干扰源,研究了冬季与夏季,即不同环境温度务件下,水蒸气对散射型光电感烟火灾探测器的误报影响。通过比较感烟火灾探测器的输出值与其附近湿度变化曲线,分析了误报发生的原因。结果表明在气温较低的冬季,水蒸气较易形成液态小水珠,容易导致感烟火灾探测器误报;而在夏季由于气温较高,这种误报不易发生。     

Adaptive response criteria in road hazard detection among older drivers

Objectives: The majority of existing investigations on attention, aging, and driving have focused on the negative impacts of age-related declines in attention on hazard detection and driver performance. However, driving skills and behavioral compensation may accommodate for the negative effects that age-related attentional decline places on driving performance. In this study, we examined an important question that had been largely neglected in the literature linking attention, aging, and driving: can top-down factors such as behavioral compensation, specifically adaptive response criteria, accommodate the negative impacts from age-related attention declines on hazard detection during driving?

Methods: In the experiment, we used the Drive Aware Task, a task combining the driving context with well-controlled laboratory procedures measuring attention. We compared younger (n = 16, age 21–30) and older (n = 21, age 65–79) drivers on their attentional processing of hazards in driving scenes, indexed by percentage of correct responses and reaction time of hazard detection, as well as sensitivity and response criteria using signal detection analysis.

Results: Older drivers, in general, were less accurate and slower on the task than younger drivers. However, results from this experiment revealed that older, but not younger, drivers adapted their response criteria when the traffic condition changed in the driving scenes. When there was more traffic in the driving scene, older drivers became more liberal in their responses, meaning that they were more likely to report that a driving hazard was detected.

Conclusions: Older drivers adopt compensatory strategies for hazard detection during driving. Our findings showed that, in the driving context, even at an older age our attentional functions are still adaptive according to environmental conditions. This leads to considerations on potential training methods to promote adaptive strategies that may help older drivers maintain performance in road hazard detection.     

Modeling drivers’ reaction when being tailgated: A Random Forests Method

Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles’ reaction using existing naturalistic driving data. Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle’s reaction based on corresponding factors including driver, vehicle, and environmental variables. Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers’ reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.     

Developing fuzzy signal detection theory for workers’ hazard perception measures on subway operations

The ability to perceive hazards has been suggested as being an important factor in determining safety risk. By measuring the ability to perceive hazard, at-risk workers could be identified and provided with additional training. This can reduce knowledge-based human errors and thereby help to avoid accidents. This paper proposes the use of fuzzy signal detection theory (FSDT), which combines fuzzy logic and conventional signal detection theory (SDT) to measure hazard perception. The flexibility of the proposed method allows multiple forms of qualitative and quantitative information to be used, including expert opinions and workers’ responses when quantitative data are lacking or when only qualitative aspects are known. Details have been provided on the basic postulates, formulas, and steps involved in FSDT analysis. A real-world example demonstrates the feasibility of the proposed framework. The results of a case study show that the participating workers had a medium level of hazard perception. This means that these workers were sensitive to the difference between hazard and safety. But there were still opportunities for improvement. Of the workers surveyed, five workers expressed conservative views regarding risk while the other workers expressed risky views. FSDT increases the applicability of conventional SDT analysis to many real-world settings, where the definition of a signal is less dichotomous than in a controlled laboratory environment.     

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。     

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.     

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.     

不同警告方式下驾驶人应激反应能力模拟试验研究

为研究在道路突发危险场景下先进驾驶辅助系统的不同警告方式对驾驶人应激反应能力的影响，利用自主开发的驾驶人应激反应能力测试软件，以计算机模拟与驾驶模拟器为试验平台，以实际驾驶视频为试验场景，选取操作准确率和反应时间为测试指标，分析不同警告方式下驾驶人的应激反应能力。研究结果表明:视觉警告可有效缩短应激反应时间；视听觉组合警告中，视觉警告占主导作用，听觉警告起辅助作用；在真实场景视频试验环境下，驾驶模拟器模拟试验的操作效果优于计算机模拟试验。