A View on Advanced Standby Control in Industry from a Knowledge

Engineering Perspective

Andreas W. Mueller and Joern Peschke

Siemens AG, Process Industries and Drives, Technology and Innovations, Nuremberg, Germany

Keywords: Energy Efficiency, Energy Management, Standby Control, Knowledge Acquisition, Knowledge

Engineering, Energy Engineering.

Abstract: In improving the energy efficiency of industrial plants, advanced standby control depicts a strategic

instrument subject to various influences and with impacts on models and software tools along the entire

plant lifecycle. In order to effectively switch plants and components to different levels of energy

consumption, the required knowledge needs to be properly engineered and applied. This paper presents an

overview of this knowledge and its interrelations in the industrial domain, with special focus on the

requirements for automated generation of energy state models and switching paths. For these tasks,

integrative approaches are proposed.

1 INTRODUCTION

Reducing energy-related expenses without negative

effects on productivity is a prime goal in many

manufacturing industries. Yet, these expenses are

often a result of the way plants are operated:

Machinery is kept ready for production, even during

non-productive phases (Hübner, 2011). In such

cases, situatively switching components to a lower

energy consumption state and back to production

state offers a large savings potential. This is a facet

of “intelligent plant control tailored to situative

needs”, which has been identified as the technical

trend with both the highest potential for making

improvements in the field of energy efficiency and

the highest need for R&D (Bründl et al., 2015).

However, in terms of the energy transition, as

promoted by the German government (BMWi,

2014), the importance of this goal will likely

decrease as decentralized power generation and

storage is increasingly emphasized. Thus, the

currently primary advantage of standby control (SC)

will lose in importance in favor of other motivators

for standby. First, the concept of “carbon footprint”

is a main value in the energy balance of various

industries (IEA, 2007). Further, components are

subject to wear. Standby phases might affect

underlying processes, and timely powering down a

component might therefore help prolong its lifetime.

In coupling SC to indicator values, and in cases

where the course of wear is actively influenceable,

standby is another tool in predictive maintenance

(Mobley, 2002). Another potential use comes from

standby as a redundancy concept in interplay with

energy consumption. This addresses component

availability while simultaneously reducing costs,

which is the object of reliability-centered

maintenance (Moubray, 1997). For this, parameters

like maintenance schedules are needed.

An advanced SC system is vital for the pursuit of

such motivators. This requires a consistent and

integrated model of the domain and plant, together

with representations of standby strategies to be

employed, linked to manufacturing execution

systems and asset management systems. In light of

the demands for integrated plant IT systems (Sauer,

2010), SC systems will eventually become an

integrated functionality. Based on existing

technologies of SC, we propose a basic approach

that permits the generic integration and combination

of standby-related knowledge to dynamically

generate switching sequences and thus flexibly

apply standby strategies.

This paper is organized as follows: Section 2 lists

work related to this topic. Section 3 describes

exemplary SC technologies. Section 4 highlights the

knowledge necessary to operate SC, and Section 5

presents the basic approach. Section 6 concludes the

paper.

364

Mueller, A. and Peschke, J..

A View on Advanced Standby Control in Industry from a Knowledge Engineering Perspective.

In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 2: KEOD, pages 364-369

ISBN: 978-989-758-158-8

2 RELATED WORK

Schöfberger et al. (2010) point out the variety of

aspects involved in SC in industrial production

plants, such as equipment types, interdependencies

among components as well as between components

and processes, in order to describe and evaluate

energy consumption. Along with transparency for

energy-related data, standardized tools and plant

models are therefore prerequisites.

Wolff et al. (2013) elaborate on the requirements

for the engineering of such SC systems, showing the

demand for digital models and integrated toolchains.

Emphasis is placed on the distinction between static

information models for intrinsic properties of the

plant and models for dynamic behavioral aspects.

Focused on the domain of machine tools, the

research group ECOMATION presents an approach

to the description and exchange of energy-related

knowledge using a special Energy Information

Description Language (EIDL) (Schlechtendahl et al.,

2013). Here, consumers in machine tools and their

interplay are described by interrelated models and

linked to the underlying physical components.

3 STANDBY CONTROL

TECHNOLOGIES IN

INDUSTRY

3.1 Basic Terminology

The term “standby” actually subsumes several

energy-saving modes, distinguished by their levels

of energy consumption. Böde et al. (2000) name

four basic modes. In normal mode, a device fulfills

its main task at a level of 100%, while in general

standby mode it does not fulfill its main task but can

be reactivated at any time. This mode can be

subdivided into the ready(-to-use) mode with almost

no decrease in consumption, the actual standby

mode with reduced consumption, and sleep mode

with the largest decrease in consumption. Another

mode is the pseudo off mode, in which a device still

absorbs energy despite being switched off physically

(an error state). The last basic mode is the actual off,

with no power consumption at all. In this paper,

these modes will also be referred to as energy states

ε. Finally, various triggers exist for activating a

standby mode. These may be device-specific, time-

based presets as well as external schedules, e.g.

pause schedules or reactions to unplanned situations

such as faults.

3.2 Technologies

Initiated by the German automotive industry,

PROFIenergy (PE) aims to reduce power

consumption of production plants through controlled

switching to standby mode during non-productive

phases, based on the PROFINET field bus (PNO,

2010). PE serves the four use cases brief pauses,

longer pauses unscheduled pauses, and measuring

and load visualization. Any device supporting PE

needs to individually implement the responses to the

mandatory commands. The architecture is based on

one PE controller acting as managing instance for

many PE devices representing the components to be

controlled. Here, time – as requested durations for

pauses – is the only criterion: The controller receives

a standby request for a specific duration (e.g. pause

for 15 minutes), and sends Start_Pause and

End_Pause commands to devices in a timely

manner. The devices then autonomously determine

the ε they can switch to, while guaranteeing the

return to production mode when needed. For this, a

state model with mandatory and optional transitions

between states is employed (Figure 1). This covers a

spectrum of discrete ε ordered by their associated

consumption levels and the length of time required

to return to production mode.

Figure 1: PE state model (adapted from (PNO, 2013)).

Sercos Energy (sE) is an application profile for

the sercos field bus system common in the domain

of production machines (Schlechtendahl, 2012). In

addition to the also-covered PE use cases, two

scenarios are served: partial machine operation

(switching off temporarily unneeded components)

and partial load operation (adapting the machine’s

energy consumption according to production

completion dates). The architecture is also 1:n-based

with a controller and subordinate devices. A sercos

controller is called coordinating system, as it is

aware of the overall process state and its influences

on the devices, permitting standalone device control.

Underlying this functionality is a rich state model

A View on Advanced Standby Control in Industry from a Knowledge Engineering Perspective

365

with discrete, semantically divisible ε and with

mandatory and optional transitions (Figure 2)

(sercos, 2011). Unlike PE, the sE state model

permits three mixable modes of control: ε may be

directly addressed, durations for pauses may be

provided (“PE mode”), or a maximum level of

energy consumption may be specified. Switching to

off mode is hence permitted.

Figure 2: sercos Energy state model (from (sercos, 2011)).

The Energy State Controller (ESC) is a result

of a work conducted at a Siemens research facility

for manufacturing automation. Built upon existing

technologies for communication and plant control, it

can perform fine-grained switching of production

plants and subsystems to predefined ε. The ESC

employs an automaton-based approach that requires

a predefined plant model covering aggregation

hierarchy, state models per component, conditions

and costs for switching, and qualification of

transitions by temporal constraints and states of

subordinate components as preconditions. Details

are given in (Mechs, 2013). The architecture

comprises 1 controller and n devices, operating on

individually addressable state machines. Calls to

devices are coordinated by the controller, based on

current subordinate ε and process values. Unlike PE

and sE, the ESC permits arbitrary topologies of

states to be defined. Target ε or durations for pauses

can be stipulated, and the required ε for each

component are inferred from these. For this, the

sequences for reaching any state are inferred from a

static model.

4 KNOWLEDGE ENGINEERING

Today, the desired behavior for such SC needs to be

manually programmed or modeled, with the key

challenge of properly determining, applying, and

monitoring the switching actions in reaction to

triggers. These steps are complex and error-prone.

This section provides an overview of the relevant

knowledge to be engineered in order to enhance

generic integration in plant software systems.

4.1 Switching Paths

Due to interdependencies between components,

switching actions need to be performed in specific

sequences. This ordered set of actions, which is later

executed by a dedicated logic, is subsequently

referred to as the switching path σ, with three basic

phases and corresponding knowledge entities. First,

before switching, the dependencies and valid actions

need to be determined in order to generate a basic σ.

This needs to be checked for both prospective

duration and effects being in line with the specific

demands of the desired action, and whether

returning to the productive state can be guaranteed.

Second, if the σ is executable, it is mandatory to

verify on the path that the next pending switching

action can be performed and, after this, whether the

action has been traceably completed according to the

expected effects. Third, after switching, it must be

checked that all affected components now inhibit the

target ε as expected. Consequently, two main

qualities are essential for any handling of a σ:

Systematic knowledge management is required to

provide the plant knowledge and suitable diagnostics

must be present to interpret actions’ results and to

identify and initiate remedial measures.

4.2 Categories to Be Covered

The knowledge required for handling ε may be

subdivided into four categories. First, there is

structure and functionality, i.e. the static and

dynamic aspects of the plant. Covering components,

their interdependencies, processes and their effects,

this depicts the basic knowledge for determination of

σ. The second category classifies components by

their switchability: Non-switchable or always on

components (e.g. safety controllers) as well as those

non-switchable by programmable logic controllers

(PLC)(e.g. lighting systems) do not contribute to

energy efficiency through SC, whereas those

switchable on-demand by PLC (e.g. conveying

systems) are the most relevant for SC as they are

switchable in dependency on a process or PLC

program. The third category covers the effects of

actions. This subsumes knowledge on components

being affected and on the delays the actions’ effects

usually require to become apparent. Finally, the

fourth category handles classifications of the

components’ states according to the energy

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

366

consumption levels. This addresses the ε-specific

energy consumptions as well as the energy demand

for transitions between ε.

4.3 Diagnostics

Effective diagnostics of SC requires a cross-level

view. While determination of σ and coordination of

their execution takes place on a plant or subsystem

level, switching actions are performed on the field

level with typically heterogeneous devices. Requests

for switching actions run from control level to field

level over various communication technologies and

field bus systems. As failures and effects may

generally involve all levels in arbitrary

combinations, full diagnostics must cope with this

issue. The knowledge described above forms the

basis for this. However, this basis is highly dynamic:

First, the plant itself is subject to change during its

lifecycle and second, all related knowledge is

subject to refinement (Figure 3; arrows symbolize

different communication technologies).

Figure 3: Interrelations of knowledge.

A special challenge arises from the infrastructure

connecting the levels, as different technologies with

own diagnostic functionalities are employed, e.g.

(PNO, 2015) or (IO-Link Community, 2013). Thus,

a great deal of knowledge needs to be captured

redundantly, due to incompatibilities. Hence, an

overall approach for diagnostics is strongly advised.

4.4 Towards an Integrated Standby

Management

In terms of the topics presented above, the following

basic methodology for knowledge organization is

proposed for creating an integrated SC system

(Figure 4). A concept for knowledge distribution

must be established initially. Strategies must be

adopted for handling the knowledge needed for

generation of σ and for coordinating its execution,

and to provide all involved components with the

necessary information. On this basis, domain models

that describe the plant in terms of structure and

function are introduced. This references aspects of

knowledge distribution as these mechanisms are part

of the plant. Energy state models are developed,

based on this. These describe the ε of the

components and processes, along with their

interconnections. An idea for this is given in

(Schlechtendahl et al., 2012). Next, switching

strategies are defined. As switching actions are

transmitted over communication channels and affect

components and processes, this information needs to

be available in order to evaluate the plausibility of a

strategy (see (Wolff et al., 2013)). Finally, models

for diagnostics are created. These contain knowledge

about the interpretation of phenomena observed

during switching actions.

Figure 4: Knowledge organization methodology.

5 A UNIFIED SWITCHING

MODEL

Having presented the knowledge relevant for SC, we

will now outline an integrative approach to the

generation and application of energy state models.

5.1 Switching Matrix

Switching to an ε is bound to certain preconditions

and has certain effects. In addition, the criterion for

switching may either be duration, a target ε, an

associated attribute, or a combination of these along

a σ. Based on their characteristics and by

interpreting state models on the graph level, we

propose the organizational means of a switching

matrix M as the central generic knowledge structure

for handling energy state models.













−

101

010







SSSSn

SSSS

TTS

SSS

(1)

Built upon the adjacency matrix of the respective

state graph, M has the following properties:

A View on Advanced Standby Control in Industry from a Knowledge Engineering Perspective

367

• A header row and column is added, with distinct

elements for entering and leaving (“

-1

”) states S,

linked by transitions T. The order of the S within

M reflects the levels of energy consumption, with

for normal mode (operational state) and S

for

maximum energy-saving mode (lowest standby).

• Each element is defined as an individual

criterion-based (c) evaluable configuration

f(S, c), f(T, c) of preconditions P (must hold for

the S, T to be considered), actions A (performed

upon valid P), and effects E (occur by

performing the A): S, T := P, A, E. An S may

comprise distinct P, A, E for entry/exit. An E

may comprise both triggering of switching

actions and changes of values. The criterion and

thus evaluation result may be duration, state, or

attribute value.

• All P, A, E are rooted in the underlying plant

models through their elements referencing plant

entities with evaluable and modifiable attributes.

• Missing or prohibited T as well as elements

along the main diagonal evaluate to neutral.

The potential states for each component can be

inferred from the energy-consumption properties and

the components’ interrelations in the plant model.

Ultimately, a σ depicts a trajectory in this search

space, whose determination is controlled using

policies or switching strategies, which effectively

depicts a planning problem. Hence, different modes

of control can be realized by means of M. E.g., since

PE mode requires finding the lowest ε possible for a

given t

Pause

, starting at a current state x, this can be

modeled using (c as duration omitted for brevity):

()

Pause

iiiiii

TfSfSfTf

SfSf

≤

+++



−

)()()()(

)()(

(2)

Switching matrices may be employed at any

level of a plant, with ideally one M per component.

Viewing an M as a container for inferred knowledge,

they may be centrally engineered on the

manufacturing execution system level and deployed

to both controller and device level, along with the

logic for σ generation. In addition, when initially

supplied for lowest (field) level components, higher-

level M may be inferred bottom-up.

5.2 Automated Generation of

Switching Matrices and Paths

Switching paths depict sequences of actions on a

dynamic knowledge corpus. Also, the domain of SC

is well-defined in terms of relevant relationships. By

taking advantage of these facts, dynamic taskflow

generation (Brecher et al., 2010) can be used for the

automated generation and population of M and for

the generation of σ. There, A are defined by

ontology-based P and E inferred via rule sets. Given

a state-based task on a plant model as parameter, the

proper sequence of A is inferred via a state-based

planning approach. This utilization is outlined in the

following.

M act as guides to each component’s energy

states and thus to the A required for switching ε.

Hence, determining these A is a prerequisite. For

this, the following information is required:

• Meta-model-based rules for prioritized handling

of components according to the basic semantic

relations of is-part-of, is-a, is-related-to as well

as depends-on, and according to the roles they

occupy within the relationships: For entries/exits

of S, additional semantics can be defined.

Prioritizations can be specified either by

precedence or by temporal relationship (Allen,

1983), e.g. ∀x, y: depends-on (x, y) → after (x,

y). This was impractical for the maintenance and

service domain of the original approach, due to

the great variety of relations. Yet, this is feasible

for the domain of SC as the set of distinct

relations is much smaller.

• The characterizing relationships between

components in the plant model: In combination,

these specify candidate classes for P.

• The overall E to be achieved on the classes given

by the M, along with the information about the

component values that need to be set to cause the

E and whether activation is permitted.

• The initial state of the model.

While iterating over an M, the

P, A, E are

inferred for each element, with initial P of the target

state not being met. The candidate classes are

determined by finding those components for which

any of the given rules would fire. In conjunction

with this, the premises of those rules identified as

valid for candidates constitute the P that are required

to hold for an A to be performed while the E are

defined as the states to be reached. With the actions

at hand, the required σ can be inferred. For this, a

reverse search strategy is applied on the search space

given by the plant model and the associated state

space. Starting with the one E representing the target

state on a considered component, the associated P

are matched with other E that cause the respective P

to hold. This search yields a raw σ, typically with

multiple relevant P and A required for an E. Using

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

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the prioritization rules, this graph is linearized in

analogous reverse search manner, yielding the final

σ. This reasoning process can either be applied a-

priori in order to provide and deploy fully qualified

M or it can be performed on-demand. Hence,

dynamics of the plant model can be met

appropriately. In conjunction with diagnostics, this

also permits the automatic generation of bypass

solutions for σ. In addition, other scenarios based on

sequences of actions can be handled with this

approach as well.

6 CONCLUSION AND OUTLOOK

Although it appears to be a simple task at first

glance, a thorough examination of “standby” in an

industrial context requires broader consideration

beyond the original focus on energy efficiency.

Switching industrial plants to energy states during

non-productive times requires that consideration of

many details that extend into different adjacent

domains. This requires systematic and integrated

knowledge management that combines disparate

knowledge artefacts across the entire plant lifecycle.

This paper has highlighted aspects of knowledge

engineering significant for the enabling of

comprehensive advanced standby control (SC). In

addition, it presented an approach to the flexible,

automated provision of the necessary energy state

models and switching paths. Future work will

address the evolution of the tools and components

involved in plant automation, with the goal of

offering advanced SC as an integrated feature. Also,

in light of the evolving general frameworks, the

implications of advanced SC as a dedicated tool

must be evaluated in the contexts of predictive

maintenance and reliability-centered maintenance so

that its potential can be classified.

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