1. F
aced with deregulation and grow-
ing competition, many utilities (and
industries) are seeking to maximize
the value of their existing assets by lever-
aging new technologies to optimize
Operations & Maintenance activities.
One of the most successful maintenance
strategies is a Condition-Based approach
to performing maintenance, which uti-
lizes data collected from periodic inspec-
tions, testing, and Predictive
Maintenance (PDM) technologies to
determine the optimum maintenance
strategy. Contrary to the traditional time-
based maintenance approach, Condition-
Based Maintenance (CBM) is a process
that utilizes monitoring and diagnostic
data to drive the maintenance decision
process. The process of collecting equip-
ment condition data, converting that data
to information, and taking action based
on that information is a vital part of
implementing an effective asset manage-
ment program. This article will define the
role of condition monitoring in today’s
asset management strategy, and how data
management techniques can be integrat-
ed with work management to facilitate
the implementation of a Condition Based
Maintenance program.
Redefining Asset Management
Historically, asset management has
typically focused on planning, schedul-
ing, and executing maintenance tasks
using a Computerized Maintenance
Management System (CMMS). Although
this approach has proven successful in
how managing preventive and corrective
maintenance work gets done, it is limited
in determining what maintenance needs
to be performed, and when it needs to
occur. In order to effectively determine
what maintenance needs to be done, util-
ities have implemented more advanced
maintenance strategies like Predictive
and Proactive Maintenance which collect
and analyze monitoring and diagnostic
data to assess equipment condition. By
integrating these condition monitoring
techniques with the Work Management
process, a more effective asset manage-
ment strategy can be executed, a strategy
where condition data drives maintenance
decisions. This process is defined as
Condition Based Maintenance (CBM).
Understanding Condition Based
Maintenance (CBM)
Condition Based Maintenance inte-
grates and utilizes inspection, testing, and
PDM diagnostic data, operator logs, and
maintenance history to assess equipment
condition and make more effective deci-
sions regarding how equipment is operat-
ed and maintained. The goals of CBM are
to increase equipment reliability, reduce
operating and maintenance costs, and
maximize equipment performance and
production. It is important to realize that
CBM is a process, not the application of
advanced technologies. The CBM
process involves efficiently collecting
data, converting that data into useful
information, and taking action (where
needed) to ensure business goals are
achieved
In a typical power plant, there are
many different data collection/equipment
monitoring processes which are being
employed to assess equipment condition:
operator rounds, periodic surveillance
and performance testing, PDM diagnos-
tics (vibration, oil, and infrared analysis),
electrical testing (resistance, power fac-
tor, etc.), instrument calibrations, the use
of online monitoring systems (DCS), and
many others. Unfortunately, many of the
these data processes are manual and uti-
lize paper-based inspection and testing
forms to collect information; others are
electronic, but do not make the data read-
ily available to the people that need it to
make operating and maintenance deci-
sions; and some processes are not docu-
mented at all. At best, this equipment
condition information resides in islands
of information scattered throughout the
enterprise in independent databases and
spreadsheets. In order to effectively uti-
40 Electricity Today
Refining Assest Management by Integrating
Condition Monitoring and Work Management
By Jean-Marc Demers
ELECTRICAL MAINTENANCE
Figure 1: The Condition Based Maintenance Process
DATA COLLECTION - PdM/Diagnostic data
- Inspection/Process data
- Testing and Performance data
- On-line Monitoring data
INFORMATION
- Automatic alarming
- Trending and Statistical Analysis
- Data Integration
- Failure Cause(s) Determination
- Cost/Benefit Analysis
- Maintenance Recommendations
ACTION
- Initiate Work Order
- Prioritize Corrective Actions
- Post Maintenance Follow-up
- Root Cause Failure Analysis
- Maintenance Program Update

2. lize this data to determine maintenance
needs, these data sources need to be inte-
grated in a central data warehouse, with
standardized business processes for man-
aging the data and ensuring this informa-
tion is integrated into the work manage-
ment process.
Data Management vs. Work
Management
Work Management Systems
(CMMS) have been utilized for years in
the utility industry. These systems focus
on the effective planning, scheduling, and
executing of work orders to perform
Preventive and Corrective Maintenance
activities. They track labor hours, parts,
purchasing information, maintenance
costs, the results of maintenance activi-
ties, and other work management infor-
mation. Inherent in these work manage-
ment systems are business rules and
processes to effectively execute how
maintenance is performed.
In contrast, Data Management
Systems collect, analyze, trend, and man-
age Condition Monitoring data including
process data (pressures, temperatures,
flow), inspection and log data, testing
data (resistance readings, calibration
data, performance data), and PDM diag-
nostic data (vibration, oil analysis,
infrared thermography, ultrasonic). Data
Management Systems rely on data min-
ing tools like statistical analysis, data
integration techniques, and advanced
analysis tools to take raw data and con-
vert it into useful information. They also
have the capability to automatically noti-
fy personnel when certain conditions
exist, and allow plant personnel to docu-
ment analysis results and decisions made
regarding actions taken.
Essentially, Data Management
Systems are designed to assess and man-
age equipment condition and determine
what maintenance is required and when.
In a CBM environment, the Data
Management System is a front-end sys-
tem that drives the Work Management
System; a process where data drives deci-
sions; a process where work orders are
triggered based on condition data (see
Figure 2). By integrating the data man-
agement process with the work manage-
ment process, an improved asset manage-
ment strategy can be implemented; a
strategy that supports the CBM process.
Automating the CBM Process
There are many different monitoring
and diagnostic activities that are typical-
ly performed in a power plant to assess
equipment condition. Many of these
processes are manual, where the condi-
tion data is recorded on hardcopy inspec-
tion sheets. For example, most utilities
have operator rounds that collect process
variables from existing field instrumenta-
tion, check oil levels, and test the opera-
tion of equipment. Typically, operators
either utilize paper inspection sheets to
record the data/results of their rounds, or
they do not record the data at all. Other
work groups perform periodic testing of
equipment like ‘meggering’ motors, uti-
lizing preconfigured forms to record the
actual test results. In some cases, the
CMMS system may be used to document
labor hours and the overall results of the
inspection or test, but the actual testing
data is usually stored on hardcopy
inspection/testing forms. In both of these
cases, the condition data is typically filed
away, rarely to be used again for trend-
ing, analysis, or comparison with other
condition monitoring information.
One technology which has proven
effective in automating these processes
and maximizing the utilization of this
data is the use of Automated Data
Collection (ADC) techniques. ADC uses
mobile data collection devices to perform
periodic inspections and testing, elec-
tronically recording the condition moni-
toring data in the field at the point of col-
lection.
Once data is collected in the field, it
can be uploaded into the central Data
Management System for alarming, trend-
ing, analysis, and integration with other
condition monitoring data. The use of
bar-coding technology can also be used
to streamline the data collection process
and eliminate the need for plant person-
nel to manually type in descriptive com-
ments when problems are identified in
the field. The benefits of utilizing the
ADC technology can be significant:
increases in productivity of 10-15%,
reduced data errors, improved data accu-
racy and integrity, and an increased uti-
lization of data for maintenance deci-
sions.
Of course, the use of ADC tech-
niques alone does not result in an effec-
tive CBM process; it only helps to
streamline the first step of the process,
that being efficient data collection. Once
the data is obtained electronically, analy-
sis and data mining tools are required to
make information out of the raw data.
One key requirement for effective data
analysis is the ability to notify plant per-
sonnel when adverse conditions exist. In
a typical CBM program, there are literal-
ly millions of data points that may be col-
Issue 3, 2002 41
Continued on page 42
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3. 42 Electricity Today
Integrating Data Management
with Work Management
Condition Based Maintenance is a
process of collecting data, converting
that data into useful information, and
taking action based on the information.
In order to effectively implement a CBM
strategy, the Data Management process
must be integrated with the Work
Management process, to create the Data-
to-Information-to-Action model.
Ultimately, these two processes can
be tied together, so that work orders can
be triggered based on the analysis results
of the condition data. For example,
assume an operator records a bearing
temperature that exceeds some pre-
determined alarm limit during his nor-
mal equipment inspection. At the initiat-
ing stage, this data point is essentially an
“unanalyzed” alarm condition; it is data,
not information. In order to effectively
analyze this condition and determine
what action (if any) is required, plant
personnel may:
1. Analyze data trends and statistical
parameters;
2. Review other open issues or alarms
that may exist on the equipment
(from other monitoring and diag-
nostic activities);
3. Review equipment fault history
records (alarm history, maintenance
history);
4. Compare data from the equipment
in alarm to a similar piece of equip-
ment (i.e. same manufacturer, same
application);
5. Perform cost benefit analysis to
assess financial/risk factors associ-
ated with various action scenarios.
Because the analysis process is very
complex and many factors must be con-
sidered before making a sound decision
regarding what actions are required, it is
not recommended that work orders be
automatically triggered in the CMMS
before this analysis is performed. In a
data intensive CBM environment, the
CMMS system would be inundated with
work orders for basic alarm conditions
that may or may not actually require
maintenance action. This would obvi-
ously have a negative effect on many of
the key performance indicators (i.e. CM
backlog, CM/PM ratios) used to track
work management effectiveness.
Instead, the data management
process should be linked with the work
process such that work orders are only
triggered after the condition has been
adequately analyzed.
In cases where the analysis of con-
dition data indicates maintenance action
information. Using this approach, plant
personnel do not have to review all of the
data to identify degraded trends or con-
ditions. The system will automatically
analyze the data and notify them via
email or paging when certain conditions
exist. This automated analysis capability
minimizes human intervention and helps
focus resources on the problems that
really require investigation. Less time is
spent trying to find potential problems,
and more time is spent resolving the
issues that warrant attention.
lected every year to assess equipment
condition. Most utilities do not have the
resources required to review all of this
data and find potential problems. Thus
an automated alarming and notification
strategy is required to “identify and
push” potential problems to the people
that need to know. Using advanced tools
like statistical analysis, multi-point cross
trending, threshold alarming, and
knowledge-based decision rules, helps
take the data and convert it into useful
Continued from page 41
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4. Issue 3, 2002 43
is required, work orders can be triggered
automatically by passing critical
data/information from the data manage-
ment system to the CMMS. Once the
work order is initiated, the work manage-
ment process typically takes over; the
work order is planned, scheduled, and
executed as part of the normal work
managementlCMMS process. Once the
maintenance is performed and the work
order is closed, information from the
CMMS (i.e. what maintenance was per-
formed) can be transferred back to the
Data Management system.
Internet Utilization for Asset
Management
The emergence of the Internet tech-
nology has opened up new methods for
utilizing and sharing information within
the utility and industries alike. Today,
using Internet-based applications, plants
can share reliability and diagnostic infor-
mation, network plant resources, com-
municate experiences and knowledge,
and maximize the utilization of informa-
tion throughout the enterprise. In some
utilities, corporate resources have actual-
ly been consolidated into central “diag-
nostic” teams that provide technical
analysis and decision support for multi-
ple plants in a given geographic region.
Internet technology has also opened up
new deployment models for business
applications, which has significantly
lowered the total cost of ownership for
these highly advanced information sys-
tems; systems that are the backbone of
today’s business infrastructure.
The emergence of Application
Service Providers (ASP) has provided
utilities with low-cost methods for utiliz-
ing advanced technology without having
to make a significant investment in
upgrading their infrastructure. ASP’s
essentially “rent” their applications to
utility customers and use the Internet to
deploy it. No hardware, software, or IT
support is required at the utility level,
which significantly lowers the cost of
deployment. Access to the system and all
of the information it manages, is
obtained anytime, from anywhere, using
a standard Internet browser.
Under an ASP model, this requires a
minimal monthly fee for application
hosting and data warehousing. Best of
all, the application can be deployed with-
in a couple of weeks, not a couple of
years like traditional client/servers appli-
cations.
In the CBM environment, the ASP
model allows utilities to integrate their
plants and share condition monitoring
information and maintenance experi-
ences, to help achieve operational and
maintenance excellence.
Conclusion
As deregulation and increased com-
petition drives utilities and industries to
become more performing, business
processes must be critically evaluated to
identify the value-added components
that help the organization succeed.
Condition Based Maintenance is a
proven process that utilizes monitoring
and diagnostic data to optimize mainte-
nance, and improve the operational
effectiveness of plant assets. The process
of collecting data, converting it into use-
ful information, and taking actions based
on the information, is the foundation of
the CBM approach.
When implemented effectively, sig-
nificant benefits can be realized from
CBM:
- Reduced O&M Costs
- Increased Equipment Reliability
Continued on page 44
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5. - Improved Process for Work
Identification
- Optimization of Maintenance Tasks
- Reduced Failures
- Increased Utilization of Data
It is important to understand that
CBM is a process; a process that involves
developing new business rules, imple-
menting new technologies, and manag-
ing organizational change.
As a result, it is not something that is
implemented once. It is a continuous
process of improvement; a living pro-
gram that never ends.
Jean-Marc Demers is with ABB Inc.
He can be reached at jean-
marc.m.demers@ca.abb.com ET
44 Electricity Today
Data Analysis & Integration Information
Data Management
(WebView 2000)
Risk Management
(Calpos MAIN)
Work Management
(MAXIMO)
Financial Management
(SAP)
Planning
Scheduling
Parts/
Inventory
Labour
Tracking
Applied RCM
Risk Analysis Financial Analysis
Action
OLM
On-Line
Data
e-Rounds
Inspection
Data
PDM
Diagnostic
Data
Work status and cost
information
Work order triggers
based on Condition
Data
Figure 2: Data Management vs. Work Management
Continued from page 43