An online method to remove chattering and repeating alarms based on alarm durations and intervals

Chattering and repeating alarms, which repeatedly make transitions between alarm and non-alarm states without operators’ response, are the most common form of nuisance alarms encountered in industrial plants. The paper formulates two novel rules to detect chattering alarms caused by random noise and repeating alarms by regular patterns such as oscillation, and proposes an online method to effectively remove chattering and repeating alarms via the m-sample delay timer. Industrial examples are provided to support the formulated rules and to illustrate the effectiveness of the proposed method.     

A new method to detect and quantify correlated alarms with occurrence delays

This paper proposes a new method to detect correlated alarms and quantify the correlation level to improve the management of industrial alarm systems. The method is mainly composed of three parts. First, a so-called occurrence delay is defined as the main cause leading to erroneous conclusions from existing methods to detect correlated alarms. In order to tolerate the presence of occurrence delays, a mechanism is presented to generate continuous-valued pseudo alarm signals. Second, a novel approach is given to estimate the correlation delay between alarm signals, so that the correlation delay can be separated from occurrence delays to obtain real occurrence delays (ROD). Third, a statistical test based on the ROD is proposed to determine whether two alarm signals are correlated or not, and the Pearson's correlation coefficient is applied to quantify the correlation level. Numerical examples and industrial case studies are provided to support the proposed method.     

过程参数滋扰报警的一类自适应管理策略

滋扰报警是过程工业报警系统的主要问题,然而,目前报警优化方法一般敏感度低、可靠性差,根据报警变量之间的时间间隔和报警持续时间确定滋扰报警的形式,提出了自适应报警死区和报警延时器的计算方法,利用时间序列ARMA模型预测报警死区来处理高频报警,并利用报警间隔时间更新报警延时器参数n来处理低频报警。通过工业实例数据验证表明,所提出的方法能够有效减少滋扰报警的数量、提高报警系统的性能。     

Quantification of alarm chatter based on run length distributions

In the process industry, alarms are configured on the control system to provide indication of abnormal events to the control room operators. In the presence of improper design of alarm generating algorithm or lack of appropriate tuning, alarms are announced more frequently than what is typically sufficient to alert the operator, a condition commonly known as ‘alarm chatter’. Chattering alarms are the most common form of nuisance alarms. The concept of run length is introduced in the alarm management context to study alarm chatter and an index is proposed to quantify the degree of alarm chatter based on run length distributions obtained exclusively from readily available historical alarm data. Chatter index hence plays a crucial role in routine assessment of industrial alarm systems. Prominent features of the proposed chatter index and its variant are demonstrated using industrial data.     

Design and assessment of delay timer alarm systems for nonlinear chemical processes

In process and manufacturing industries, alarm systems play a critical role in ensuring safe and efficient operations. The objective of a standard industrial alarm system is to detect undesirable deviations in process variables as soon as they occur. Fault detection and diagnosis systems often need to be alerted by an industrial alarm system; however, poorly designed alarms often lead to alarm flooding and other undesirable events. In this article, we consider the problem of industrial alarm design for processes represented by stochastic nonlinear time‐series models. The alarm design for such complex processes faces three important challenges: (1) industrial processes exhibit highly nonlinear behavior; (2) state variables are not precisely known (modeling error); and (3) process signals are not necessarily Gaussian, stationary or uncorrelated. In this article, a procedure for designing a delay timer alarm configuration is proposed for the process states. The proposed design is based on minimization of the rate of false and missed alarm rates—two common performance measures for alarm systems. To ensure the alarm design is robust to any non‐stationary process behavior, an expected‐case and a worst‐case alarm designs are proposed. Finally, the efficacy of the proposed alarm design is illustrated on a non‐stationary chemical reactor problem. © 2017 American Institute of Chemical Engineers AIChE J, 63: 77–90, 2018     

一种新的模糊聚类-分级算法及其在流程报警管理中的应用

1 INTRODUCTION Normally, operating management experience of in- dustrial process indicates that the probability one ig- nores alarm is 10-4 after handling one alarm signal and the probability one takes no direct action is 10-5 after hearing one alarm. Experiences also indicate that the probability one ignores alarm will increase to 10-3 from 10-4 if one alarm isn't handled within one minute[1]. Therefore, when the second alarm is appearing the probability which it can't be identified is 10…     

基于数据驱动的化工过程参数报警阈值优化

为了提高化工过程报警系统的性能,需要对过程参数的报警阈值进行优化设置。针对传统阈值方法存在的问题,采用核密度估计方法、基于历史数据对过程报警状态进行估计,从最小化误报警和漏报警概率的角度建立了优化过程报警阈值的目标函数,并采用数值优化的方法进行求解。应用于TE过程的仿真结果表明,此方法能够有效地减少过程误报警的次数,并且对漏报警的次数进行抑制。     

工业报警序列的模糊关联规则挖掘方法

面向寻找工业报警序列根源,抑制报警泛滥,论文提出了一种模糊加权关联规则挖掘方法,结合模糊集合、Apriori数据挖掘算法和时间序列分析挖掘报警关联规则。基于报警数据的时间约束属性和相似度约束属性,利用相似度作为模糊加权关联规则挖掘算法的权重,提高挖掘效率和准确性。并且,相对于定量表达,模糊关联规则对于操作者来说更加容易使用。工业实例验证了方法的有效性。     

Dynamic alarm prediction for critical alarms using a probabilistic model

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper, we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.     

Hidden Markov model based approach for diagnosing cause of alarm signals

When a fault occurs in a process, it slowly propagates within the system and affects the measurements triggering a sequence of alarms in the control room. The operators are required to diagnose the cause of alarms and take necessary corrective measures. The idea of representing the alarm sequence as the fault propagation path and using the propagation path to diagnose the fault is explored. A diagnoser based on hidden Markov model is built to identify the cause of the alarm signals. The proposed approach is applied to an industrial case study: Tennessee Eastman process. The results show that the proposed approach is successful in determining the probable cause of alarms generated with high accuracy. The model was able to identify the cause accurately, even when tested with short alarm sub-sequences. This allows for early identification of faults, providing more time to the operator to restore the system to normal operation.     

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Alarmflood is one of themain problems in the alarmsystems of industrial process. Alarmroot-cause analysis and alarmprioritization are good for alarmflood 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 alarmpriority and reduce the blindness of alarmhandling. As a case study, the Tennessee Eastman process is utilized to showthe effectiveness and validity of proposed approach. Alarmsystem performance comparison shows that our rationalization methodology can reduce the alarmflood to some extent and improve the performance.     

非规整报警的相关性分析方法

报警管理在化工过程安全中具有重要地位,而报警相关性挖掘是报警管理的重要组成部分。在传统的基于凝聚层次聚类的报警相关性挖掘方法基础上,提出非规整报警相关性计算方法,弥补了传统方法难以处理延迟时间变化与非对称情况的不足。同时以概率形式表示相关性大小,使不同位点之间的相关性计算结果具有可比性。经过仿真案例与工厂真实生产案例测试,该方法能够有效挖掘过程中出现的报警相关性,进而指导报警系统合 理化。     

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.     

过程工业报警系统可视化监控技术及应用

合理有效的可视化监控技术及工具有助于操作员及时理解报警信息并采取响应措施。针对现有可视化技术存在的缺点及不足, 如资源利用不充分、报警等级划分不明确、报警根源分析不彻底等, 构建了4种新型可视化工具:基于信息融合的解释结构模型(静态和动态)、层次高密度报警图、层次优先级色彩图、性能水平趋势图, 分别实现了过程递阶模型建立、报警根源分析、滋扰报警识别、报警优先级划分、报警系统性能常规评估等目的。以TE仿真模型为例, 阐明了上述可视化技术及工具的实用性和有效性, 不仅可以展示报警全貌原始信息, 还可快速识别报警根源、关键报警、滋扰报警以及报警系统性能水平, 实现了高效监控,从一定程度上解决了报警泛滥问题。     

考虑人因指标的过程报警阈值自适应优化方法

目前大多过程参数报警阈值优化方法没有考虑操作员的人因影响,为此,结合人因工程的理论,提出了一种考虑操作员人因影响的过程报警阈值自适应优化方法。首先,采用APRIORI算法挖掘报警事件与操作事件的关联关系;基于在线报警数据,模糊推理获得人因综合指标;根据误报、漏报与人因指标的关系计算两者的权重,建立报警阈值优化目标函数。工业实例数据验证表明,此方法对于过程报警阈值的优化设置具有较好的人因环境适应性,能够使得过程报警系统的性能得以进一步的改善。     

A practical approach to improve alarm system performance: Application to power plant

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal condition in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. However, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers(i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.     

On reducing false alarms in multivariate statistical process control

The primary objective of this note is to reduce the false alarms in multivariate statistical process control (MSPC). The issue of false alarms is inherent within MSPC as a result of the definition of control limits. It has been observed that under normal operating conditions, the occurrence of “out-of-control” data, i.e. false alarms, conforms to a Bernoulli distribution. Therefore, this issue can be formally addressed by developing a Binomial distribution for the number of “out-of-control” data points within a given time window, and a second-level control limit can be established to reduce the false alarms. This statistical approach is further extended to consider the combination of multiple control charts. The proposed methodology is demonstrated through its application to the monitoring of a benchmark simulated chemical process, and it is observed to effectively reduce the false alarms whilst retaining the capability of detecting process faults.     

ALARM CHARACTERISTICS FOR A FLOOD WARNING SYSTEM WITH DETERMINISTIC COMPONENTS

Abstract. A method for evaluating a predictor-based alarm system is studied in this paper. The predictor is composed of a 'deterministic' component reflecting external information and a statistically based component for the error between the measurements and the external predictor. The aim of the predictor study is twofold:it is a means of interpreting the connections between the alarm and the catastrophe, and it can be used to select suitable alarm levels. As an application, the performance of a water-level predictor as part of a flood warning system has been evaluated. The result of this analysis shows that an alarm system which operates when the predictor reaches a certain level will tend to give either too many alarms or alarms that are out of phase with the catastrophe.     

基于物元分析的过程工业报警优化

过程工业报警系统设定的报警点多且复杂,给监控生产带来了一定的困难.结合报警系统的特点和物元分析方法,构造各报警参数的物元模型,定义各报警参数与报警级别的关联函数,提出基于关联函数的权重分配改进,计算各报警参数与报警级别的综合关联度.在保证安全生产的前提下,根据综合关联度的大小对各报警参数进行优化选择,形成适合过程工业的报警优化方法.结合精对苯二甲酸溶剂脱水塔报警系统验证了该方法的有效性,基于物元分析的报警优化方法合理地降低了报警系统的报警量和报警频率,为报警管理和操作优化提供了新思路.     

基于LSTM时间序列重建的生产装置异常检测

工业生产装置通常设置传感器报警阈值进行报警，但是对处于报警阈值以下的时间序列异常难以及时捕捉。基于统计的传统检测方法在解决时间序列异常检测上存在很大挑战，因此提出基于long short term memory (LSTM)时间序列重建的方法进行生产装置的异常检测。该算法首先引入一层LSTM网络对传感器数据的时间序列进行向量表示，采用另一层LSTM网络对时间序列进行逆序重建，然后利用重建值与实际值之间的误差，通过极大似然估计方法对该段序列进行异常概率估计，最终通过学习异常报警阈值实现时间序列异常检测。采用ECG测试数据、能源数据与危险品储罐传感器数据进行了仿真实验，验证了所提方法在不同长度的数据上的有效性。