An online method for detection and reduction of chattering alarms due to oscillation

Chattering alarms, which repeatedly and rapidly make transitions between alarm and normal states in a short time period, are the most common form of nuisance alarms that severely degrade the performance of alarm systems for industrial plants. One reason for chattering alarms is the presence of oscillation in process signals. The paper proposes an online method to promptly detect the chattering alarms due to oscillation and to effectively reduce the number of chattering alarms. In particular, a revised chattering index is proposed to quantify the level of chattering alarms; the discrete cosine transform-based method is used to detect the presence of oscillation; two mechanisms by adjusting the alarm trippoint and using a delay timer are exploited to reduce the number of chattering alarms. An industrial case study is provided to illustrate the effectiveness of the proposed method.     

Is Average Run Length to False Alarm Always an Informative Criterion?

Abstract

Apart from Bayesian approaches, the average run length (ARL) to false alarm has always been seen as the natural performance criterion for quantifying the propensity of a detection scheme to make false alarms, and no researchers seem to have questioned this on grounds that it does not always apply. In this article, we show that in the change-point problem with mixture prechange models, detection schemes with finite detection delays can have infinite ARLs to false alarm. We also discuss the implication of our results on the change-point problem with either exchangeable prechange models or hidden Markov models. Alternative minimax formulations with different false alarm criteria are proposed.     

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。     

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.     

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     

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。     

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。     

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.     

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.     

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…     

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。     

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。     

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。     

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。     

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.     

Nonparametric Sequential Bayes Estimation of the Distribution Function

Abstract

There are many reasons why the average run length has been the operating characteristic of choice to quantify the propensity of a control scheme to give rise to false alarms. Professor Mei's paper dents this consensus. An alternative to Professor Mei's suggested direction is considered.     

Shewhart Revisited

Abstract

The Shewhart control chart was first to monitor an ongoing process and raise an alarm when it appears that the level has changed. We show that the Shewhart chart is optimal for the criterion of maximizing the probability of detecting a change upon its occurrence subject to an average run length to false alarm. It is remarkable, particularly when the change is of moderate size, that Shewhart's procedure is better than cumulative sum (CUSUM). In the multivariate setting, applying the Shewhart procedure to each process separately is suboptimal. We create a generalized Shewhart procedure that is optimal for the aforementioned criterion. The results are illustrated in common settings.     

Assessing the Impact of Head Starts in the Performance of One-Sided Markov-Type Control Schemes

Abstract

Head start values—that is, nonzero initial values for the summary statistics of Markov-type control schemes such as the well-known cumulative sum (CUSUM) schemes—are throughly recommended in the literature and used by quality control practitioners. The rationale is as follows: if the process is operating in control, the summary statistic of the control scheme is soon brought to zero, so that the expected effect of the head start is minimal; otherwise, the operator is alerted to the out-of-control situation much sooner, which may prevent start-up problems.

This article assesses the impact of the adoption of a head start on the run length, indisputably the most popular performance measure of control schemes. It also brings stochastically monotone matrices into focus in the statistical process control setting and investigates their influence on the survival function, the alarm rate function and the aging character of the run length of one-sided Markov-type control schemes.

The derived properties, which may be of great interest to quality control practitioners, are illustrated through examples.     

Process computer alarm and disturbance analysis: outline of methods for systematic synthesis of the fault propagation structure

There is increasing interest in the use of the process control computer to analyse alarms as they occur and thus to assist the operator in assimilating the alarm information. It is, however, time-consuming to have to create the necessary data structure manually. The work reported here involves the development of two methods of creating the data structure systematically, and hence largely automatically. The techniques are conceptual rather than fully engineered.     

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.