=Paper=
{{Paper
|id=Vol-1507/dx15paper31
|storemode=property
|title=Chronicle Based Alarm Management in Startup and Shutdown Stages
|pdfUrl=https://ceur-ws.org/Vol-1507/dx15paper31.pdf
|volume=Vol-1507
|dblpUrl=https://dblp.org/rec/conf/safeprocess/VasquezTSJA15
}}
==Chronicle Based Alarm Management in Startup and Shutdown Stages==
https://ceur-ws.org/Vol-1507/dx15paper31.pdf
Proceedings of the 26th International Workshop on Principles of Diagnosis
Chronicle based alarm management in startup and shutdown stages
John W. Vasquez1,3,4 , Louise Travé-Massuyès1,2 ,Audine Subias1,3 Fernando Jimenez4 and Carlos Agudelo5
1
CNRS, LAAS, 7 avenue du colonel Roche, F-31400 Toulouse, France
2
Univ de Toulouse, LAAS, F-31400 Toulouse, France
3
Univ de Toulouse, INSA, LAAS, F-31400 Toulouse, France
4
Universidad de los Andes, Colombia.
5
ECOPETROL ICP, Colombia.
e-mail: jwvasque@laas.fr, louise@laas.fr, subias@laas.fr,
fjimenez@uniandes.edu.co, carlos.agudelo@ecopetrol.com.co
Abstract used for the chronicle design. The section 5 is devoted to
the chronicle generation. Finally , an illustrative application
The transitions between operational modes on real data from a petrochemical plant is given section 6.
(startup/shutdown) in chemical processes gen-
erate alarm floods and cause critical alarm
saturation. We propose in this paper an approach
2 State of the art: Alarm management
of alarm management based on a diagnosis Alarm management has recently focused the attention of
process. This diagnosis step relies on situation many researchers in themes such as:
recognition to provide to the operators relevant Alarm historian visualization and analysis: A combined
information on the failures inducing the alarms analysis of plant connectivity and alarm logs to reduce the
flows. The situation recognition is based on number of alerts in an automation system was presented by
chronicle recognition. We propose to use the [3]; the aim of the work presented is to reduce the num-
information issued from the modeling of the ber of alerts presented to the operator. If alarms are re-
system to generate temporal runs from which the lated to one another, those alarms should be grouped and
chronicles are extracted. An illustrative example presented as one alarm problem. Graphical tools for rou-
in the field of petrochemical plants ends the tine assessment of industrial alarm systems was proposed
article. by [4], they presented two new alarm data visualization tools
for the performance evaluation of the alarm systems, known
as the high density alarm plot (HDAP) and the alarm sim-
1 Introduction ilarity color map (ASCM). Event correlation analysis and
The petrochemical industries losses have been estimated at two-layer cause-effect model were used to reduce the num-
20 billion dollars only in the U.S. each year, and the AEM ber of alarms in [5]. A Bayesian method has been intro-
(Abnormal Events Management) has been classified as a duced for multimode process monitoring in [6]. This type
problem that needs to be solved. Hence the alarm man- of techniques helps us to recognize alarm chattering, group-
agement is one of the aspects of great interest in the safety ing many alarms or estimate the alarm limits in transition
planning for the different plants. In the process state tran- stages, but the time and the procedure actions are not in-
sitions such as startup and shutdown stages the alarm flood cluded.
increases and it generates critical conditions in which the Process data-based alarm system analysis and rational-
operator does not respond efficiently, then a dynamic alarm ization: The evaluation of plant alarm systems by behavior
management is required [1]. Currently, many fault detec- simulation using a virtual subject was proposed by [7]. [8]
tion and diagnosis techniques for multimode processes have introduced a technique for optimal design of alarm limits
been proposed; however, these techniques cannot indicate by analyzing the correlation between process variables and
fundamental faults in the basic alarm system [2], in the other alarm variables. In 2009 a framework based on the receiver
hand the technical report ”Advance Alarm System Require- operating characteristic (ROC) curve was proposed to op-
ments” EPRI (The Electric Power Research Institute) sug- timally design alarm limits, filters, dead bands, and delay
gests a cause-consequence and event-based processing. In timers; this work was presented in [9] and a dynamic risk
this perspective, diagnosis approaches based on complex analysis methodology that uses alarm databases to improve
events processing or situation recognition are interesting is- process safety and product quality was presented in [10]. In
sues. Therefore, in this paper, a dynamic alarm management [11] the Gaussian mixture model was employed to extract
strategy is proposed in order to deal with alarm floods hap- a series of operating modes from the historical process data
pening during transitions of chemical processes. This ap- and then the local statistic and its normalized contribution
proach relies on situations recognition (i.e. chronicle recog- chart were derived for detecting abnormalities early and for
nition). As, the efficiency of alarm management approaches isolating faulty variables. We can see that the use of virtual
depends on the operator expertise and process knowledge, subjects could be applied to probe the alarm system and us-
our final objective is to develop a diagnosis approach as a ing historical information about the alarm behavior for de-
decision tool for operators. The paper is divided into 6 sec- tecting abnormalities. The problem is presented when the
tions. Section 2 gives an overview on the relevant literature. simulation requires a lot time to probe the totally of scenar-
The section 3 concerns the modeling of the system. The sec- ios and when we have new plants that do not contain infor-
tion 4 is about the chronicle principle and the temporal runs mation about historical data.
241
Proceedings of the 26th International Workshop on Principles of Diagnosis
S
Plant connectivity and process variable causality analy- • CSD ◆ i CSDi is the Causal System
sis (causal methods): In the literature, transition monitor- Description or the causal model used to repre-
ing of chemical processes has been reported by many re- sent the constraints underlying in the continuous
searchers. In [12] was presented a fault diagnosis strategy dynamic of the hybrid system. Every CSDi asso-
for startup process based on standard operating procedures, ciated to a mode qi , is given by a graph (Gc = #
this approach proposes behavior observer combined with [ K, I). I is the set of influences where there is
dynamic PCA (Principal Component Analysis) to estimate an edge e(vi , vj ) 2 I from vi 2 # to vj 2 # if the
process faults and operator errors at the same time, and in variable vi influences variable vj . Then, the vertices
[13] was presented a framework for managing transitions represent the variables and the edges represent the
in chemical plants where a trend analysis-based approach influences between variables and for each edge exists
for locating and characterizing the modes and transitions in an association with a component in the system. The
historical data is proposed. Finally, in [14] a hybrid model- set of components is noted as COMP .
based framework was used for alarm anticipation where the • Init is the initial condition of the hybrid system,
user can prepare for the possibility of a single alarm occur-
rence. For the transition monitoring, these types of tech- 3.2 Qualitative abstraction of continuous
niques are the most used in industrial processes and the hy- behavior
brid model based framework could be a good representation
In each mode of operation, variables evolve according to
of our system. We can observe that a causal model allows
the corresponding dynamics. This evolution is represented
identify the root of the failures and check the correct evo-
with qualitative values. The domain D(Vi ) of a qualitative
lution in a transitional stage. Our proposal is closer to the
variable Vi 2 VQ is obtained through the function fqual :
third type of approach and seeks to exploit the causal rela-
D(vi ) ! D(Vi ) that maps the continuous values of variable
tionships as presented in the next sections.
vi to ranges defined by limit values (High Hi and Low Li ).
8
3 Representation of the system >
>
>
ViH if vi Hi ^ dv dt > 0
i
>
> M dv
if vi < Hi ^ dti < 0
3.1 Hybrid Causal Model < Vi
The hybrid system is represented by an extended transition f(vi )qual = _ (2)
>
> dvi
system [15], whose discrete states represent the different >
> vi Li ^ dt > 0
>
: L
modes of operation for which the continuous dynamics are Vi if vi < Li ^ dv dt < 0
i
characterized by a qualitative domain. Formally, a hybrid dvi
causal system is defined as a tuple: dt > 0 represents that the continuous variable vi is increas-
ing and dv
dt < 0 that it is decreasing. The behavior of these
i
= (#, D, Conf, T r, E, CSD, Init) (1) qualitative variables is represented in Figure 1. by the graph
Where GVi = (VQ , ⌃c , ) where VQ is the set of the possible qual-
itative states (ViL : Low, ViM : Medium, ViH : High) of
• # = {vi } is a set of continuous process variables
the continuous variable vi , ⌃c is the finite set of the events
which are function of time t.
associate to the transitions and : VQ ⇥ ⌃c ! VQ is the
• D is a set of discrete variables. D = Q [ K [ VQ . Q transition function. The corresponding event generator is
is a set of states qi of the transition system which repre-
sent the system operation modes. The set of auxiliary
discrete variables K = {Ki , i = 1, ...nc } represents
the system configuration in each mode qi as defined
below by Conf(qi ). VQ = {Vi } is a set of qualitative
variables whose values are obtained from the behavior
of each continuous variable vi .
• Conf(qi ): Q ! ⌦i D(Ki ) where ⌦ is the Cartesian
product and D(Ki ) is the domain of Ki 2 K that
provides the configuration associated to the mode. i.e.
the modes of the underlying multimode components
(typically, a valve has two normal modes, opened and
closed)
Figure 1: Qualitative values of the process variables
• E = ⌃[⌃c is a finite set of event types noted , where:
– ⌃ is the set of event type associated to the proce- defined by the abstraction function fVQ !
dure actions in a startup or shutdown stages.
– ⌃c is the set of event type associated to the behav- fVQ ! : VQ ⇥ (VQ , ⌃c ) ! ⌃c
8+
ior of the continuous process variables. > l (vi ) if ViL ! ViM
>
<
• T r : Q⇥ ⌃ ! Q is the transition function. The tran- l (vi ) if ViM ! ViL
8Vi 2 VQ , (Vin , Vim ) !
sition from mode qi to mode qj with associated event >
> h+ (vi ) if ViM ! ViH
:
is noted (qi , , qj ) or qi ! qj . We assume that the h (vi ) if ViH ! ViM
n m L M H
model is deterministic, without loss of generality i.e. Vi , Vi 2 {Vi , Vi , Vi }
whenever qi ! qj and qi ! qk then qj = qk for each S (3)
(qi , qj , qk ) 2 Q3 and each 2 ⌃. ⌃c = vi 2# {l+ (vi ), l (vi ), h+ (vi ), h (vi )} (4)
242
Proceedings of the 26th International Workshop on Principles of Diagnosis
3.3 Automatic derivation of the causal model
To obtain the causal model of a system in a given operat-
ing mode implies to collect the equations that represent the
behavior of the system in this mode. The theory of causal
ordering issued from the Qualitative Reasoning community
can be well applied to obtain automatically the causal struc-
ture associated to a set of equations. Now, associating acti-
vation conditions to the equations extend the causal order-
ing to systems with several operating modes [16]. Then
these activation conditions can be related in the influences
of the resulting causal graph.The proposed algorithm, im-
plemented in the Causalito software makes use of condi-
tions that avoid recomputing a totally new perfect matching
for every operating mode, thus reducing the computational
cost. In this work, the Causal System Description is given Figure 2: Principle of chronicle generation
by CSD = (#, I), where each influence I is labeled with:
• An activation condition indicating the modes in which
it is active (or no label if it is active in all modes), 4.2 Temporal runs
• The corresponding equation,
• The component whose behavior is expressed by the We denote a temporal run as h R, T i where R is a run and T
equation. is the time graph of the run that includes the time constraints
In the follow section we expose the principle of the chroni- CT between each pair of time points where must occurs the
cle generation where concepts such as event, chronicle and events type. Figure 3 gives time graph examples and the
temporal run are described. possible composition of time graphs. In our approach the
4 Chronicles
4.1 Events and chronicles
Let us consider time as a linearly ordered discrete set of in-
stants. The occurrence of different events in time represents
the system dynamics and a model can be determined to di-
agnose the correct evolution. An event is defined as a pair
( i , ti ), where i 2 E is an event type and ti is a variable of
integer type called the event date. We define E as the set of
all event types and a temporal sequence on E is an ordered
set of events denoted S = h( i , ti )ij with j 2 Nl where l
is the size of the temporal sequence S and Nl is a finite set
of linearly ordered time points of cardinal l. A chronicle is
a triplet C = (⇠, CT , G) such that ⇠ ✓ E, CT is the set of
temporal constraints. G = (N, It) is a directed graph where
N represent event types of E and the arcs It represent the Figure 3: Time graphs example
relationship between events 2 E, if the event 1 occurs t
time units after 2 , then it exists a directed link from 1 to runs are issued from the system evolution from one oper-
2 associated with a time constraint. Considering the two ation mode to another. The interleaved sequence of event
events ( i , ti ) and ( j , tj ), we define the time interval as types ↵1 , ↵2 , . . . ↵n represents the procedure actions and the
the pair ⌧ij = [t , t+ ], ⌧ij 2 CT corresponding to the lower behavior evolution of the process variables. The time con-
and upper bounds on the temporal distance between the two straints between each pair of event types are determined by
event dates ti and tj [17]. The idea of our proposal is to simulation of the continuous behavior for each process vari-
design the chronicles from the hybrid causal model of the able, responding to the procedure actions.
system. Indeed the evolution of the system can be captured
with temporal runs from which chronicles can be learn (See
Figure 2). More precisely, the system initiates in the state q0
and it evolves according to the transitions resulting from the 5 Generation of Chronicles
events defined by the procedure actions for specific scenar-
ios (startup/shutdown). For a given system modes qi 2 Q, 5.1 Chronicle database
the associated CSDi is used to generate the set of event
types corresponding to the evolution of the continuous pro- An industrial or complex process P r is composed of differ-
cess variables. A run is defined by a sequence of event types ent areas P r = {Ar1 , Ar2 , ...Arn } where each area Ark
↵1 , ↵2 , ....↵n where ↵i 2 E generated for each scenario us- has different operational modes such as startup, shutdown,
ing the startup/shutdown procedures. These runs with time slow march, fast march, etc. The set CArk of chronicles Cijk
constraints permit to construct the chronicle database of the for each area Ark is presented in the matrix below, where
system. In this preliminary approach, time constraints are the rows represent the operating modes (i.e. O1 : Startup,
obtained by simulation. O2 : Shutdown, O3 : Startuptype , O4 : Startuptype , etc)
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Proceedings of the 26th International Workshop on Principles of Diagnosis
and the columns the different faults. 6 Case study
N f1 f2 . . . . . . fn 6.1 HTG (Hydrostatic Tank Gauging) system
2 k3
O1 C01k
C11 k
C21k
. . . . . . Ci1 In the Cartagena Refinery currently are being implemented
O2 6 C k C12 k
C22k
. . . . . . Ci2 k7 news units and elements. In the startup stage they will need
6 02 k k k k7 a tool to help the operator to recognize dangerous condi-
O3 6C03 C13 C23 . . . . . . Ci3 7
CArk = 6 k k k k7 tions. We will analyze the startup and shutdown stages in the
O4 6C04 C14 C24 . . . . . . Ci4 7
... 6 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
7 unit of water injection. This process is a HTG (Hydrostatic
... 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 Tank Gauging) system composed by the following compo-
Oj C0jk
C1j k
C2jk
. . . . . . Cijk nents: one tank (T K), two normally closed valves (V 1 and
(5) V 2), one pump (P u), a level sensor (LT ), a pressure sensor
The chronicle database used for diagnosis is composed by (P T ), inflow sensor (F T1 ) and an outflow sensor (F T2 ), see
the entries of all the matrices {CArk }. This chronicle Figure 5.
database is submitted to a chronicle recognition system that
identifies in an observable flow of events all the possible
matching with the set of chronicles from which the situation
(normal or faulty) can be assessed.
5.2 Chronicle learning
As explained previously when the system changes mode of
operation, a set of event types occurs forming a run R. As
this evolution is due to procedure actions. Not only a unique
temporal run can occur. Hence, we need to set up the maxi-
mal number of temporal runs that it could occur in each sce-
nario represented in the matrix (5). To obtain the chronicle
in each scenario is necessary to obtain the larger time graph
with as many event types and with the minimal values of the Figure 5: Process diagram
constraints. [18] proposes to determine the chronicles from
the temporal runs. They define a partial order relation be- Assuming this system as a hybrid causal model, the un-
tween two temporal runs as hR, T i h R0 , T 0 i when the set derlying discrete event system and the different process
of event types in R0 is a subset of event type in R and the operation modes are described in Figure 6 where we can
time graphs T and T 0 are related by T T 0 determining the see a possible correct evolution for the startup procedure.
result graph where exists a unique equivalent constraint that The events V 1c,o , V 2c,o represents that the valves V 1,V 2
is the minimal. The relation expresses that the set of con- move from the state closed to the state opened, the events
straints in the time graph T 0 is a subset of constraints in T , V 1o,c ,V 2o,c represents on the contrary the valves moving
CT (t, t0 ) ✓ CT 0 (t, t0 ). Therefore, we apply the composition from the state opened to the state closed. The event P uf n
(see Figure 3) between the time graphs in order to merge the indicates that the pump P u is turned on and the event
constraints obtaining the larger and constrained time graph P un f indicates that the pump P u is turned off.
that represents the chronicle in that scenario. Figure 4 gives
an example of a chronicle generation from a maximal tem- 6.2 Identification of causal relationships
poral run. In the next section a case study is presented in
The level (L) in the tank is related to the weight (m) of
the liquid inside, its density (⇢) and the tank area (A). The
density (⇢) is the relationship of the pressures (Pmed ,Pinf )
in separated points (h). Based on the global material bal-
ance, we define that the input flow is equal to the outlet flow.
Then, the variation of the weight (dm(t)/dt) in the tank is
proportional to the difference between the inflow (QiT K )
and the outflow (QoV 2 ). The differential pressure in the
pump and in V 2 are specified as PP u and PV 2 . The
outlet pressure in the pump (P o) is related with the outlet
flow tank (QoT K ), the revolutions per minute in the pump
(RP MP u ), his capacity (C) and the radio of the outlet pipe
(r). The outflow (QoV 2 ) and inflow (QiT K ) control are re-
lated to the percentage aperture of the valves V 1 (LV 1) and
V 2 (LV 2) and differential pressures ( PV 1 , PV 2 ). In Fig-
ure 7 we can see the CSD of the system in the modes q1 ,
Figure 4: Chronicle example q5 and q7 . For example, the mode q1 activates the influence
of QiT K to L. The mode q5 activates the influence of QiT K
to L and the influence of L to P o and finally the mode q7
which the chronicle generation from the temporal runs is il- activates the influence of QiT K to L, L to P o and P o to
lustrated. QoV 2.
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Proceedings of the 26th International Workshop on Principles of Diagnosis
6.3 Event identification
One of the most important steps for fault diagnosis based
on chronicle recognition is to determine the set of events
that can carry the system to a failure. Each situation pat-
tern (normal or abnormal) is a set of events and temporal
constraints between them; then a situation model may also
specify events to be generated and actions to be triggered
as a result of the situation occurrence. For a startup proce-
dure in the example process, the set of event types ⌃ that
represent the procedure actions is:
⌃ = {V 1c,o , V 2c,o , P uf n , V 1o,c , V 2o,c , P un f } (6)
According to the causal graphs associated to the modes in-
volved in the sequence of procedure actions (i.e q1 , q5 and q7
indicated by red arrows on Figure 6), the event types of ⌃c
correspond to the behavior of the variables L,Po and QoV 2 .
+
{l(L) , l(L) , h+(L) , h(L) ,
c + +
⌃ = l(P o) , l(P o) , h(P o) , h(P o) , (7)
l(QoV 2 ) , l(QoV 2 ) , h+
+
(QoV 2 ) , h(QoV 2 ) }
From the startup/shutdown procedures the different tempo-
ral runs are determined and these temporal runs are related
to the normal and abnormal situations. The chronicle result-
ing from a normal startup procedure is presented in Figure
8. The model system was developed in Matlab including
Figure 6: Underlying DES of the HGT system
Figure 8: Chronicle C01 for normal behavior startup
the injection water process area. The continuous behavior
is related to the evolution of the level L, outlet pump pres-
sure P o and the outlet flow QoV 2 in the system. The dis-
crete evolution is related to the event evolution of the pro-
cedures in the startup and shutdown stages. From the dif-
ferent failure modes of the process, the dynamic behavior
of the variables is shown with a detection for the possible
process states, including the normal procedure without fail-
ure. The simulation includes 3 types of startup procedures
(OK, fail1 and fail2 ) with 4 types of fault modes (V1 , V2 ,
Figure 7: CSD in the modes q1 , q5 and q7 P ump and Drainopen ) and 3 types of Shutdown proce-
dure (OK, N on actived and F ail). The evolution of the
continuous variables in the startup procedure without failure
is shown in Figure 9. The events are generated by the pro-
gram through the evolution of the differential equations, the
245
Proceedings of the 26th International Workshop on Principles of Diagnosis
variable conditions and the procedural actions. Recognition [6] Z. Ge and Z. Song. Multimode process monitoring
of the chronicles was done using the tool stateflow. based on bayesian method. Journal of Chemometrics,
23, 636e650., 2009.
[7] M. Noda X. Liu and H. Nishitani. Evaluation of plant
alarm systems by behavior simulation using a virtual
subject. Computers & Chemical Engineering, 34,
374e386, 2010.
[8] D. Xiao F. Yang, S. L. Shah and T. Chen. Im-
proved correlation analysis and visualization of indus-
trial alarm data. 18th IFAC World Congress Milano
(Italy), 2011.
[9] D.S. Shook S.R. Kondaveeti I. Izadi, S.L. Shah and
Figure 9: Normal behavior in startup procedure without fail- T. Chen. A framework for optimal design of alarm
ure. Blue: Level, Green:Pressure, Red: ouletflow systems. 7th IFAC symposium on fault detection, su-
pervision and safety of technical processes, Barcelona,
Spain, 2009.
7 Conclusion [10] U.G. Oktem A. Pariyani, W.D. Seider and M. Soroush.
A preliminary method for alarm management based on au- Dynamic risk analysis using alarm databases to im-
tomatically learned chronicles has been proposed. The pro- prove process safety and product quality: Part ii
posal is based on a hybrid causal model of the system and a bayesian analysis. AIChE Journal, 58, 826e841.,
chronicle based approach for diagnosis. An illustrative ex- 2012.
ample of an hydrostatic tank gauging has been considered [11] J. Liu and D. Chen. Non stationary fault detection and
to introduce the main concepts of the approach. In this pa- diagnosis for multimode processes. AIChE Journal,
per the design of the temporal constraints of the chronicles 56, 207e219., 2010.
were performed from simulation results, but further research
[12] L. Boang Z. Jing and Y. Hao. Fault diagnosis strategy
aim to generate the chronicles from the model of the system.
for startup process based on standard operating proce-
Learning approaches are currently considered for acquiring
dures. 25th Chinese Control and Decision Conference
the chronicle base directly from the sequences of events rep-
(CCDC), 2013.
resenting the situations. For this propose the algorithm HC-
DAM (Heuristic Chronicle Discovery Algorithm Modified [13] H. Vedam R. Srinivasan, P. Viswanathan and
[17]) may be used. The use of HIL (Hardware in the loop) A. Nochur. A framework for managing transitions in
to simulate and validate the proposal is also in our prospects. chemical plants. Computers & Chemical Engineering,
29, 305e322., 2005.
8 Acknowledge [14] A. Adhitya S. Xu and R. Srinivasan. Hybrid model-
based framework for alarm anticipation. Industrial &
The ECOPETROL - ICP engineers Jorge Prada, Francisco
Engineering Chemistry Research, 2014.
Cala and Gladys Valderrama help us to develop and validate
the simulations. [15] L. Travé-Massuyès R. Pons, A. Subias. Iterative hy-
brid causal model based diagnosis: Application to au-
tomotive embedded functions. Engineering Applica-
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